Measuring Urban Sprawl and Validating Sprawl Measures [PDF]

Abstract Across the nation, the debate over metropolitan sprawl and its impacts continues. A decade ago, Smart Growth America (SGA) and the U.S. Environmental Protection Agency (EPA) sought to raise the level of this debate by sponsoring groundbreaking research on sprawl and its quality-of-life consequences (Ewing et al. 2002; Ewing et al. 2003a, 2003b, 2003c). The original sprawl indices were made available to researchers who wished to explore the various costs and benefits of sprawl. They have been widely used in outcome-related research, particularly in connection with public health. Sprawl has been linked to physical inactivity, obesity, traffic fatalities, poor air quality, residential energy use, emergency response times, teenage driving, lack of social capital, and private-vehicle commute distances and times (Ewing et al. 2003a; Ewing et al. 2003b; Ewing et al. 2003c; Kelly-Schwartz et al. 2004; Sturm and Cohen 2004; Cho et al. 2006; Doyle et al. 2006; Ewing et al. 2006; Kahn 2006; Kim et al. 2006; Plantinga and Bernell 2007; Ewing and Rong 2008; Joshu et al. 2008; Stone 2008; Trowbridge and McDonald 2008; Fan and Song 2009; McDonald and Trowbridge 2009; Trowbridge et al. 2009; Lee et al. 2009; Nguyen 2010; Stone et al. 2010; Schweitzer and Zhou 2010; Zolnik 2011; Holcombe and Williams 2012; Griffin et al. 2013; Bereitschaft and Debbage 2013). In this study for the National Cancer Institute, the Brookings Institution, and Smart Growth America, we begin in Chapter 1 by updating the original county indices to 2010. As one would expect, the degree of county sprawl does not change dramatically over a 10-year period. Also, given their fixed boundaries, most counties become more compact (denser and with smaller blocks) over the 10-year period. Sprawl occurs mainly as previously rural counties (in 2000) outside metropolitan areas become low density suburbs and exurbs of metropolitan areas (in 2010). In Chapter 2, we develop refined versions of the indices that incorporate more measures of the built environment. The refined indices capture four distinct dimensions of sprawl, thereby characterizing county sprawl in all its complexity. The four are development density, land use mix, population and employment centering, and street accessibility. The dimensions of the new county indices parallel the metropolitan indices developed by Ewing et al. (2002), basically representing the relative accessibility provided by the county. The simple structure of the original county sprawl index has become more complex, but also more nuanced and comprehensive, in line with definitions of sprawl in the technical literature. In Chapter 3, we develop metropolitan sprawl indices that, like the refined county indices, have four distinct dimensions-- development density, land use mix, population and employment centering, and street accessibility. Compared to metropolitan sprawl indices from the early 2000s, these new indices 1

incorporate more variables and hence have more construct validity. For example, the earlier effort defined density strictly in terms of population concentrations, while this effort considers employment concentrations as well. The reason for developing metropolitan sprawl indices, rather than limiting ourselves to counties, is that metropolitan areas are natural units of analysis for certain quality-of-life outcomes. In Chapter 4, we conduct one of the first longitudinal analysis of sprawl to see which areas are sprawling more over time, and which are sprawling less or actually becoming more compact. To conduct such as analysis, we need to employ a new level of geography, the census urbanized area. In contrast of counties and metropolitan areas, urbanized areas expand incrementally as areas grow and rural tracts are converted to urban and suburban uses. The analysis shows that, on average, urban sprawl in the U.S. increased between 2000 and 2010, but that there are many exceptions to this generalization. Finally, in chapter 5, we develop compactness indices for census tracts within metropolitan areas. We know from the travel and public health literatures that there is a demand in the research community for built environmental metrics at the sub-county level, what might be described as the community or neighborhood scale. The appendices provide values of compactness/sprawl indices for census tracts, counties, metropolitan areas, and urbanized areas. Data are available in electronic form at http://gis.cancer.gov/tools/urbansprawl/

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Table of Contents Abstract ......................................................................................................................................................... 1 Chapter 1. Updated County Sprawl Index .................................................................................................... 5 Update to 2010 ......................................................................................................................................... 7 Chapter 2. Refined County Sprawl Measures ............................................................................................. 11 Density .................................................................................................................................................... 11 Mixed Use ............................................................................................................................................... 12 Centering ................................................................................................................................................. 14 Street Accessibility .................................................................................................................................. 17 Relationship Among Compactness Factors ............................................................................................. 18 Composite Index ..................................................................................................................................... 18 Greater Validity of New Index................................................................................................................. 20 Chapter 3. Derivation of Metropolitan Sprawl Indices ............................................................................... 25 Methods .................................................................................................................................................. 25 Sample ........................................................................................................................................... 25 Variables ........................................................................................................................................ 26 Results ..................................................................................................................................................... 28 Individual Compactness/Sprawl Factors ........................................................................................ 28 Overall Compactness/Sprawl Index for 2010 ................................................................................ 30 Discussion................................................................................................................................................ 32 Chapter 4. Urbanized Areas: A Longitudinal Analysis ................................................................................. 79 Methods .................................................................................................................................................. 79 Sample ........................................................................................................................................... 79 Variables ........................................................................................................................................ 79 Results ..................................................................................................................................................... 82 Individual Compactness/Sprawl Factors ........................................................................................ 82 Overall Compactness/Sprawl Index for 2010 ................................................................................ 83 Overall Compactness/Sprawl Index for 2000 ................................................................................ 84 Discussion................................................................................................................................................ 85 Chapter 5. Derivation of Census Tract Sprawl Indices ................................................................................ 86 Chapter 6. Conclusion ................................................................................................................................. 87 References .................................................................................................................................................. 88 3

Appendix A. County Compactness Indices for 2010, 2000, and Changes .................................................. 91 Appendix B. County Compactness Factors and Composite Indices for 2010 .......................................... 115 Appendix C. 2010 Metropolitan Indices ................................................................................................... 141 Appendix D. Urbanized Areas Compactness Indices 2010 ....................................................................... 149 Appendix E. Urbanized Areas Compactness Indices 2000 ........................................................................ 154

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Chapter 1. Updated County Sprawl Index Ewing et al. (2003b; 2003c) originally estimated a single county sprawl index for each of 448 metropolitan counties or statistically equivalent entities (e.g., independent towns and cities). These counties comprised the 101 most populous metropolitan statistical areas, consolidated metropolitan statistical areas, and New England county metropolitan areas in the United States as of the 1990 census, the latest year for which metropolitan boundaries were defined as that study began. Nonmetropolitan counties, and metropolitan counties in smaller metropolitan areas, were excluded from the sample. More than 183 million Americans, nearly two-thirds of the United States population, lived in these 448 counties in 2000. Six variables were part of the original county sprawl index (as shown in Table 1). U.S. Census data were used to derive three population density measures for each county:  



gross population density in persons per square mile (popden) percentage of the county population living at low suburban densities, specifically, densities between 100 and 1,500 persons per square mile, corresponding to less than one housing unit per acre (lt1500) percentage of the county population living at medium to high urban densities, specifically, more than 12,500 persons per square mile, corresponding to about 8 housing units per acre, the lower limit of density needed to support mass transit (gt12500)

In deriving population density measures, census tracts were excluded if they had fewer than 100 residents per square mile (corresponding to rural areas, desert tracts, and other undeveloped lands). Ewing et al. were only concerned with sprawl in developed areas where the vast majority of residents live. A fourth density variable was derived from estimated urban land area for each county from the National Resources Inventory of the U.S. Department of Agriculture. 

net population density of urban places within the county (urbden)

Data reflecting street accessibility for each county were also obtained from the U.S. Census. Street accessibility is related to block size since smaller blocks translate into shorter and more direct routes. A census block is defined as a statistical area bounded on all sides by streets, roads, streams, railroad tracks, or geopolitical boundary lines, in most cases. A traditional urban neighborhood is composed of intersecting bounding streets that form a grid, with houses built on the four sides of the block, facing these streets. The length of each side of that block, and therefore its block size, is relatively small. By contrast, a contemporary suburban neighborhood does not make connections between adjacent cul-desacs or loop roads. Instead, local streets only connect with the street at the subdivision entrance, which is on one side of the block boundary. Thus, the length of a side of this block is quite large, and the block itself often encloses multiple subdivisions to form a superblock, a half mile or more on a side. Large block sizes indicate a relative paucity of street connections and alternate routes.

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Two street accessibility variables were computed for each county:  

average block size (avgblk) percentage of blocks with areas less than 1/100 square mile, the size of a typical traditional urban block bounded by sides just over 500 feet in length (smlblk).

Blocks larger than one square mile were excluded from these calculations, since they were likely to be in rural or other undeveloped areas. The six variables were combined into one factor representing the degree of sprawl within the county. This was accomplished via principal component analysis, an analytical technique that takes a large number of correlated variables and extracts a small number of factors that embody the common variance in the original data set. The extracted factors, or principal components, are weighted combinations of the original variables. When a variable is given a great deal of weight in constructing a principal component, we say that the variable loads heavily on that component. The greater the correlation between an original variable and a principal component, the greater the loading and the more weight the original variable is given in the overall principal component score. The more highly correlated the original variables, the more variance is captured by a single principal component. The principal component selected to represent sprawl was the one capturing the largest share of common variance among the six variables, that is, the one upon which the observed variables loaded most heavily. This one component accounted for almost two-thirds of the variance in the dataset. Because this component captured the majority of the combined variance of these variables, no subsequent components were considered. To arrive at a final index, Ewing et al. transformed the principal component, which had a mean of 0 and standard deviation of 1, to a scale with a mean of 100 and standard deviation of 25. This transformation produced a more familiar metric (like an IQ scale) and ensured that all values would be positive, thereby allowing us to take natural logarithms and estimate elasticities. The bigger the value of the index, the more compact the county. The smaller the value, the more sprawling the county. Scores ranged from a high of 352 to a low of 63. At the most compact end of the scale were four New York City boroughs, Manhattan, Brooklyn, Bronx, and Queens; San Francisco County; Hudson County (Jersey City); Philadelphia County; and Suffolk County (Boston). At the most sprawling end of the scale were outlying counties of metropolitan areas in the Southeast and Midwest such as Goochland County in the Richmond, VA metropolitan area and Geauga County in the Cleveland, OH metropolitan area. The county sprawl index was positively skewed. Most counties clustered around intermediate levels of sprawl. In the U.S., few counties approach the densities of New York or San Francisco. For these counties, the original sprawl index was validated against journey to work, adult obesity, and traffic fatality data (Ewing et al. 2003a; Ewing et al. 2003b; Ewing et al. 2003c). Later, the same county sprawl index was used to model the built environment in a study of youth obesity (Ewing et al. 2006). For this study, the index was computed for additional counties or county equivalents in order to have 6

sprawl data for more National Longitudinal Survey of Youth (NLSY97) respondents. The 954 counties or county equivalents in the expanded sample represented the vast majority of counties lying within U.S. metropolitan areas, as defined by the U.S. Census Bureau in December 2003. Almost 82% of the U.S. population lived in metropolitan counties for which county sprawl indices were now available. Most recent research on sprawl and its impacts has made use of this expanded dataset.

Update to 2010 In updating the original county sprawl index to 2010, five of the six variables were derived in the exact same way as for 1990 and 2000. U.S. Census files for summary levels 140 (census tracts) and 101 (census blocks) were downloaded from American FactFinder. Population data were extracted for all census tracts in all metropolitan counties. Land area data were extracted for all census blocks in all metropolitan counties. Ninety-nine metropolitan counties were lost to the sample because they had no census tracts averaging 100 persons per square mile or more. They were deemed to be rural. The sixth variable, net density of urban areas within the county, was originally computed using data on “urban and built up uses” from the National Resources Inventory of the U.S. Department of Agriculture. The most recent NRI (2007) does not provide data at the county level. Therefore the U.S. Geological Survey’s National Land Cover Database (NLCD) was used instead. NLCD serves as the definitive Landsatbased, 30-meter resolution, land cover database for the Nation. It is a raster dataset providing spatial reference for land surface classification (for example, urban, agriculture, forest). It can be geoprocessed to any geographic unit. For the current work, the urban land area was generated at the county level using NLCD 2006 (the latest product) and county geography (2010) for the entire U.S. Using the “Tabulate Area” spatial analyst tool within ArcGIS, urban land areas within each county were calculated. The noncontiguous areas in the same county were aggregated resulting in total urban area in square miles. The value codes treated as urban were: 21. Developed, Open Space - Areas with a mixture of some constructed materials, but mostly vegetation in the form of lawn grasses. Impervious surfaces account for less than 20% of total cover. These areas most commonly include large-lot single-family housing units, parks, golf courses, and vegetation planted in developed settings for recreation, erosion control, or aesthetic purposes. 22. Developed, Low Intensity - Areas with a mixture of constructed materials and vegetation. Impervious surfaces account for 20% to 49% percent of total cover. These areas most commonly include single-family housing units. 23. Developed, Medium Intensity – Areas with a mixture of constructed materials and vegetation. Impervious surfaces account for 50% to 79% of the total cover. These areas most commonly include single-family housing units.

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24. High Intensity - Highly developed areas where people reside or work in high numbers. Examples include apartment complexes, row houses and commercial/industrial. Impervious surfaces account for 80% to 100% of the total cover. The NRI and NLCD datasets are fairly comparable (see Appendix A), making the county sprawl indices for 1990, 2000, and 2010 fairly comparable. However, NLCD is only available for the continental U.S. Therefore counties and county equivalents from Alaska, Hawaii, and Puerto Rico, 72 in total, were lost to the sample. Once again, principal component analysis was used to reduce the six variables to a single index. This index accounts for 59 percent of the variance in the original six variables. Factor loadings are shown in Table 1.1. Table 1.1. County Sprawl Index Variables and Factor Loadings in 2010 Observed variable popden lt1500 gt12500 urbden avgblk smlblk Eigenvalue Explained variance * Correlation with county sprawl index

Factor loading* 0.858 -0.658 0.821 0.876 -0.664 0.711 3.56 59.3%

We transformed the overall compactness score into an index with a mean of 100 and a standard deviation of 25. This was done for the sake of consistency and ease of understanding. With this transformation, the more compact counties have index values above 100, while the more sprawling have values below 100. Appendix A contains county sprawl (compactness) indices for 994 county and county equivalents in 2010. The 10 most compact and 10 most sprawling counties are shown in Tables 1.2 and 1.3. The most compact counties are as expected, central counties of large, older metropolitan areas. The most sprawling counties are outlying counties of large metropolitan areas, or component counties of smaller metropolitan areas. Values range from 54 for Jackson County outside Topeka, Kansas, the most sprawling county in 2010, to 464 for New York County (Manhattan), the most compact county in 2010. Appendix A also contains estimates of county sprawl in 2000, derived by applying the 2010 component score coefficient values to data for counties in 2000. Finally, the appendix presents changes in county sprawl, measured equivalently, between the two census years. Table 1.2. 10 Most Compact Counties in 2010 According to the Six Variable Index

1 2 3

County New York County, NY Kings County, NY Bronx County, NY

Metropolitan Area New York-Northern New Jersey-Long Island, NY-NJ-PA New York-Northern New Jersey-Long Island, NY-NJ-PA New York-Northern New Jersey-Long Island, NY-NJ-PA

Index 463.9 341.4 331.5 8

4 5 6 7 8 9 10

Queens County, NY San Francisco County, CA Hudson County, NJ Suffolk County, MA Philadelphia County, PA District of Columbia, DC Richmond County, NY

New York-Northern New Jersey-Long Island, NY-NJ-PA San Francisco-Oakland-Fremont, CA New York-Northern New Jersey-Long Island, NY-NJ-PA Boston-Cambridge-Quincy, MA-NH Philadelphia-Camden-Wilmington, PA-NJ-DE-MD Washington-Arlington-Alexandria, DC-VA-MD-WV New York-Northern New Jersey-Long Island, NY-NJ-PA

272.1 247.8 228.8 217.1 216.8 193.3 190.1

Table 1.3. 10 Most Sprawling Counties in 2010 According to the Six Variable Index

985 986 987 988 989 990 991 992 993 994

Ford County, IL Osage County, KS Jasper County, IN Grant County, AR Tipton County, IN Chester County, TN Morrow County, OH Greene County, NC Polk County, MN Jackson County, KS

Metropolitan Area Champaign-Urbana, IL Topeka, KS Chicago-Joliet-Naperville, IL-IN-WI Little Rock-North Little Rock-Conway, AR Kokomo, IN Jackson, TN Columbus, OH Greenville, NC Grand Forks, ND-MN Topeka, KS

Index 67.3 66.9 66.8 66.8 66.4 65.4 63.4 63.3 61.1 54.6

Figure 1.1 is a plot of 2010 sprawl index values vs. 2000 sprawl index values computed with the same component score coefficient values. As one would expect, the degree of county sprawl does not change dramatically over a 10-year period. Figure 1.2 is a histogram of changes in county sprawl values between 2000 and 2010, where 2000 sprawl values are computed using the 2010 component score coefficient values. As one would expect, given their fixed boundaries, most counties become more compact (denser and with smaller blocks) over the ten-year period. Sprawl occurs mainly as previously rural counties (in 2000) outside metropolitan areas become low-density suburbs and exurbs of metropolitan areas (in 2010).

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Figure 1.1. Scatterplot of 2010 Sprawl Index vs. 2000 Sprawl Index (Estimated Equivalently)

Figure 1.2. Histogram of Changes in County Sprawl Index Between 2000 and 2010 (Estimated Equivalently)

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Chapter 2. Refined County Sprawl Measures A literature review by Ewing (1997) found poor accessibility to be the common denominator of sprawl. Sprawl is viewed as any development pattern in which related land uses have poor access to one another, leaving residents with no alternative to long distance trips by automobile. Compact development, the polar opposite, is any development pattern in which related land uses are highly accessible to one another, thus minimizing automobile travel and attendant social, economic, and environmental costs. The following patterns are most often identified in the literature: scattered or leapfrog development, commercial strip development, uniform low-density development, or single-use development (with different land uses segregated from one another, as in bedroom communities). In scattered or leapfrog development, residents and service providers must pass by vacant land on their way from one developed use to another. In classic strip development, the consumer must pass other uses on the way from one store to the next; it is the antithesis of multipurpose travel to an activity center. Of course, in low-density, single-use development, everything is far apart due to large private land holdings and segregation of land uses. While the technical literature on sprawl focuses on land use patterns that produce poor regional accessibility, poor accessibility is also a product of fragmented street networks that separate urban activities more than need be. When asked, planners now routinely associate sprawl with sparse street networks as well as dispersed land use patterns. The original county sprawl index operationalized only two dimensions of urban form—residential density and street accessibility. Our grant from the National Institutes of Health (NIH) provides for the development of refined measures of county compactness or, conversely, county sprawl. These measures are modeled after the more complete metropolitan sprawl indices developed by Ewing et al. (2002). The refined indices operationalize four dimensions, thereby characterizing county sprawl in all its complexity. The four are density, mix, centering, and street accessibility. The dimensions of the new county indices parallel the metropolitan indices, basically representing the relative accessibility provided by the county. The full set of variables was used to derive a refined set of compactness/sprawl factors using principal component analysis. One principal component represents population density, another land use mix, a third centering, and a fourth street accessibility. County principal component values, standardized such that the mean value of each is 100 and the standard deviation is 25, are presented in Appendix B. The simple structure of the original county sprawl index has become more complex, but also more nuanced and comprehensive, in line with definitions of sprawl in the technical literature.

Density Low residential density is on everyone’s list of sprawl indicators. Our first four density variables are the same as in the original sprawl index, gross density of urban and suburban census tracts (popden), percentage of the population living at low suburban densities (lt1500), percentage of the population

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living at medium to high urban densities (gt12500), and urban density based on the National Land Cover Database (urbden). The fifth density variable is analogous to the first, except it is derived with employment data from the Local Employment Dynamics (LED) database rather than population data from the 2010 Census. The LED database is assembled by the Census Bureau through a voluntary partnership with state labor market information agencies. The data provide unprecedented details about America's jobs, workers, and local economies. The LED data, available from 2002 to 2010, are collected at census block geography level and can be aggregated to any larger geography, in this case block groups. LED variables include total number of jobs, average age of workers, monthly earnings, and as of 2009 sex, race, ethnicity, and education levels. In this case, LED data were processed for the year 2010. The data were aggregated from census block geography to census block group geography to generate total jobs by two-digit NAICS code for every block group in the nation, except those in Massachusetts, which doesn’t participate in the program. The density variable derived from the LED database is: 

gross employment density of urban and suburban census tracts (empden)

Principal components were extracted from the five density-related variables, and the principal component that accounted for the greatest variance became the county density factor. Factor loadings (that is, correlations of these variables with the density factor) are shown in Table 2.1. The eigenvalue of the density factor is 3.56, which means that this one factor accounts for more of the variance in the original dataset than three of the component variables combined. In other words, the density factor accounts for more than 70 percent of the total variance in the data set. As expected, one of the variables loads negatively on the density factor, that being the percentage of population living at less than 1,500 persons per square mile. The rest load positively. Thus, for all component variables, higher densities translate into higher values of the density factor. Table 2.1. Variable Loadings on the County Density Factor for 2010 Observed variable Factor loading* popden lt1500 gt12500 urbden empden Eigenvalue Explained variance * Correlation with the density factor

0.983 0.848 -0.440 0.850 0.977 3.56 71.1%

Mixed Use Three types of mixed-use measures are found in the land use-travel literature: those representing relative balance between jobs and population within subareas of a region; those representing the diversity of land uses within subareas of a region; and those representing the accessibility of residential 12

uses to nonresidential uses at different locations within a region. In this study, all three types were estimated for counties in our sample and became part of a mix factor. The first two variables were calculated for each block group using block-level population data from the 2010 Census, and block-level employment data from the 2010 LED database. For the first variable, each block group centroid was buffered with a one-mile ring, and jobs and population were summed for blocks within the ring. One-mile rings were used to standardize geography for census block groups, which vary widely in size, making balance easier to achieve in the larger block groups. The resulting job and population totals were used to compute a job-population balance measure. 1 This variable equals 1 for block groups with the same ratio of jobs-to-residents within the one-mile ring as the metropolitan area as a whole; 0 for block groups with only jobs or residents within the one-mile ring, not both; and intermediate values for intermediate cases. All values were weighted by the sum of block group jobs and residents as a percentage of the county total to obtain: 

countywide average job-population balance (jobpop).

For the second mixed-use variable, each block group centroid was again buffered with a one-mile ring, and jobs by sector were summed for blocks within the ring. An entropy formula was then used to compute a measure of job mix. 2 The variable equals 1 for block groups with equal numbers of jobs in each sector within the ring; 0 for block groups with all jobs in a single sector within the ring; and intermediate values for intermediate cases. The sectors considered in this case were retail, 1

The equation used to calculate job-population balance was:

i n

 (1  ( ABS ( J i 0

i

 JP * Pi )) /( J i  JP * Pi )) * (( BJ i  BPi ) /(TJ  TP))

where: i = census tract number (excluding those with fewer than 100 persons per square mile) n = number of census tracts in the county J = jobs in the census tract P = residents in the census tract JP = jobs per person in the metropolitan area TJ = total jobs in the county TP = total residents in the county 2

The equation for this measure is: n

 ((P * LN ( P )) / LN ( j)) * (( BJ i 1

j

j

j

i

 BPi ) /(TJ  TP))

where: i = census tract number (excluding those with fewer than 100 persons per square mile) n = number of census tracts in the county j = number of sectors Pj = proportion of jobs in sector j JP = jobs per person in the metropolitan area TJ = total jobs in the county TP = total residents in the county

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entertainment, health, education, and personal services. Values were weighted by the sum of block group population and employment as a percentage of the county total to obtain: 

countywide degree of job mixing (jobmix).

A third mixed-use variable uses data from Walk Score, Inc. to measure proximity to amenities, with different amenities weighted differently and amenities discounted as the distance to them increases up to one mile and a half, where they are assumed to be no longer accessible on foot.3 Classic Walk Score data were acquired for all urban census tracts in the United States. Year 2012 data were purchased to reduce the cost of data acquisition. Values were weighted by the sum of block group population and employment as a percentage of the county total to obtain: 

countywide average Walk Score (walkscore)

Principal components were extracted from the three mix-related variables, and the principal component that accounted for the greatest variance became the mix factor. Loadings of these variables on the mix factor are shown in Table 2.2. The eigenvalue of the mix factor is 2.30, which means that this one factor accounts for more than two-thirds of the total variance. Table 2.2. Variable Loadings on the County Mix Factor for 2010 Observed variable Factor loading* jobpop jobmix walkscore Eigenvalue Explained variance * Correlation with the mix use factor

0.891 0.942 0.784 2.30 76.6%

Centering Urban centers are concentrations of activity that provide agglomeration economies, support alternative modes and multipurpose trip making, create a sense of place in the urban landscape, and otherwise differentiate compact urban areas from sprawling ones. Centeredness can exist with respect to population or employment, and with respect to a single dominant center or multiple subcenters. The technical literature associates compactness with centers of all types, and sprawl with the absence of centers of any type. Ewing et al. (2002) measured metropolitan centering, in part, in terms of concentrations of development in or around historic central business districts (CBDs) of metropolitan areas. This concept of centering does not make much sense when applied to the individual counties that make up a metropolitan area, only one of which can contain the historic central business district. Other counties have their own subcenters in the polycentric metropolitan areas of today, and the existence of and proximity to these 3

A grocery store, for example, gets three times the weight of a book score. The distance decay function starts with a value of 100 and decays to 75 percent at a half mile, 12.5 percent at one mile, and zero at 1.5 miles.

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are what distinguish counties with concentrations of activity from those without. Four measures of centering were derived for metropolitan counties: The first centering measure came straight out of the 2010 census: 

coefficient of variation in census block group population densities, defined as the standard deviation of block group densities divided by the average density of block groups. The more variation in densities around the mean, the more centering and/or subcentering exists within the county (varpop)

The second centering measure was derived from the LED database and is analogous to the first measure, except for its use of employment density by block group rather than population density to compute: 

coefficient of variation in census block group employment densities, defined as the standard deviation of block group densities divided by the average density of block groups. The more variation in densities around the mean, the more centering and/or subcentering exists within the county (varemp)

The last two centering variables measure the proportion of employment and population within CBDs and employment sub-centers. We first identified the location of CBDs and employment sub-centers for all metropolitan areas. For identifying CBDs, we ran a local spatial autocorrelation procedure using the local Moran’s I statistic (Anselin, 1995).4 With this procedure, it is possible to quantify the degree of clustering of neighboring zones with high levels of density. This method has been used by Baumont & Le Gallo (2003) and Riguelle et al. (2007).

Local Moran’s I is defined as:

where Ii is the local Moran’s I coefficient, X is the value of the employment density, wij is the matrix of spatial weights, and n is the number of observations. Through calculating z-values of the local Moran statistic (see Anselin, 1995; Getis and Ord, 1996) it is then possible to identify two types of spatial clusters, two types of outliers :    

High-high Low-low High-low Low-high

High values around neighbors with high values (cluster) Low values around neighbors with low values (cluster) High values around neighbors with low values (outlier) Low values around neighbors with high values (outlier)

Using LED data of block groups, the Moran’s I analysis was done for all Metropolitan areas. The High-High clusters with the highest employment density in each metropolitan statistical area (MSA) were considered as CBD candidates. However not all of them are CBDs. We excluded the hot spots containing large firms such as hospitals, malls and university campuses by applying the threshold of having employment share of no more than 75 percent in each sector. We identified CBD for a total of 356 Metropolitan areas.

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Having CBDs for 356 metropolitan areas, we identified employment sub-centers as the positive residuals estimated from an exponential employment density function using Geographically Weighted Regression method (GWR).5 In the literature, urban sub-centers are areas with significantly higher employment density than the surrounding areas (McDonald 1987). To identify sub-centers, researchers have used several types of procedures: a minimum density procedure (Giuliano and Small 1991), identification of local peaks (Craig & Ng, 2001), and a nonparametric method (McMillen 2004). The last of these methods works best, according to literature review by Lee (2007). Using this procedure, we found 224 metropolitan areas to be monocentric (have only one center), 132 to be polycentric (have more than one center), and 18 metropolitan areas to be dispersed (have no CBD and no sub-center). This procedure resulted in two new centering variables. These findings were validated by inspecting Google Earth satellite images to identify concentrations of activity, and see whether they corresponded to our findings with GWR.  

Percentage of county population in CBD or sub-centers (popcen) Percentage of county employment in CBD or sub-centers (empcen)

Principal components were extracted from the set of centering variables, and the principal component that accounted for the greatest variance became our centering factor. All component variables loaded positively on the centering factor (see table 2.3). The eigenvalue of the centering factor is 1.96, which means that this one factor accounts for just under half of the total variance in the data set. Table 2.3. Variable Loadings on the County Centering Factor for 2010 Observed variable Factor loading* varpop varemp popcen empcen Eigenvalue Explained variance * Correlation with the centering factor

0.085 0.642 0.820 0.932 1.96 49.1%

GWR estimates a smoothed employment density surface using only nearby observations for any data point (block groups), with more weights given to closer observations. The dependent variable of the GWR estimations is employment density by block groups and the independent variable is the distance of the block group centroid from the CBD. We used the Adaptive kernel type with 30 numbers of neighbors. The block groups with highest positive residual (if residual is 4 times greater than predicted) are candidates for employment sub-centers. As with CBD identification, we excluded the block groups containing large firms such as hospitals, regional malls, and university campuses by applying the requirement that the employment share be no more than 75 percent in each sector. Finally we excluded cases when their ratio of employment to population was less than 2.5 (Gordon et al 1986). We identified a total of 451 sub-centers in 132 metropolitan areas.

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Street Accessibility In the refined sprawl indices, two street variables are the same as in the original county sprawl index: average block size excluding rural blocks of more than one square mile (avgblk) and percentage of small urban blocks of less than one hundredth of a square mile (smlblk). To these two street accessibility variables were added. The two new street variables are:  

intersection density for urban and suburban census tracts within the county, excluding rural tracts with gross densities of less than 100 persons per square mile (intden) percentage of 4-or-more-way intersections, again excluding rural tracts (4-way)

Intersection density captures both block length and street connectivity. Percentage of 4-or-more-way intersections provides a pure measure of street connectivity, as 4-way intersections provide more routing options than 3-way intersections. Starting with a 2006 national dataset of street centerlines generated by TomTom that ships with ArcGIS, we produced a national database of street intersection locations, including for each intersection feature a count of streets that meet there. The TomTom dataset includes one centerline feature for each road segment running between neighboring intersections; i.e. every intersection is the spatially coincident endpoint of 3 or more road segments.6 The resulting national intersection database contains 13.1 million features; 77% of these are three-way intersections, and the remaining 23% are four- or more-way intersections. Total counts of 3- and 4-ormore-way intersections were tabulated for census tracts, and census tracts were aggregated to obtain county-level data. For each county, the total number of intersections in urban and suburban tracts was divided by the land area to obtain intersection density (intden), while the number of 4-or-more-way intersections was multiplied by 100 and divided by the total number of intersections to obtain the percentage of 4-or-more way intersections (4way).

6

Intersection features were created as follows: Using Census Feature Class Code (CFCC) values, we filtered out all freeways, unpaved tracks, and other roadways that don't function as pedestrian routes. Divided roadways, which from a pedestrian mobility perspective function similarly to undivided roadways of the same functional class, were represented in the source data as pairs of (roughly) parallel centerline segments. These were identified by CFCC value and merged into single segments using GIS tools. Streets intersecting the original divided roadways were trimmed or extended to the new merged centerlines, and the new merged centerlines were split at each intersection with side streets such that centerline features only intersect each other at feature endpoints. Roundabouts were assumed to function similarly to single 4+-way intersections, rather than close-set clusters of intersections joining the roundabout proper and the incoming streets. As such, centroids of roundabout circles were located and assigned an assumed count of four incoming streets; endpoints of incoming street features were ignored. With the corrected street centerline data prepared, we generated point features at both endpoints of each street segment. Points closer together than 12m were adjusted to be spatially coincident in order to control for any possible remaining geometric errors related to divided roadways. We then used GIS tools to count the number of points (representing ends of street segments) coinciding at any location. Locations with point counts of one (dead ends) or two (locations where a roadway changes name, functional class, or other attribute) were discarded as non-street intersections. Remaining locations were flagged with attributes indicating whether a point was a threeway or a four- or more-way intersection.

17

Principal components were extracted from the full set of street-related variables, and the principal component that accounted for the greatest variance became our street accessibility factor. Loadings of these variables on the street factor are shown in Table 2.4. The eigenvalue of the street factor is 2.39, which means that this one factor accounts for more than half of the total variance in the data set. As expected, one of the variables loads negatively on the street accessibility factor, that being the average block size. The rest load positively. Thus, for all component variables, more accessibility translates into higher values of the street factor. Table 2.4. Variable Loadings on the County Street Factor for 2010 Observed variable Factor loading* avgblk smlblk inden 4-way Eigenvalue Explained variance * Correlation with the street factor

-0.764 0.901 0.836 0.545 2.39 59.8%

Relationship Among Compactness Factors It has been said that measures of the built environment are so highly correlated that they should not be represented separately, but instead should be combined into a single index. Thus, for example, overall measures of walkability have been advanced as an alternative to individual measures. This position is not borne out by this study, at least not at the county level. While correlated, as one might expect, the four compactness factors seem to represent distinct constructs. Their simple correlation coefficients are shown in Table 2.5. The highest is 0.647, which means that each factor explains less than 42 percent of the variation in the other. Table 2.5. Simple Pearson Correlation between four factors density factor

mix factor

centering factor

street factor

density factor mix factor

1 0.399**

0.399** 1

0.523** 0.421**

0.583** 0.647**

centering factor street factor

0.523** 0.583**

0.421** 0.647**

1 0.438**

0.438** 1

Composite Index The next issue we had to wrestle with was how to combine the four factors into a single sprawl index. A priori, there is no “right” way to do so, only ways that have more or less face validity. Should the four factors be weighted equally, or should one or another be given more weight than the others? Density has certainly received more attention as an aspect of sprawl than has, say, street accessibility. However, beyond play in the literature, we could think of no rationale for differential weights. The first three factors all contribute to the accessibility or inaccessibility of different 18

development patterns, none presumptively more than the others. Depending on their values, all move a county along the continuum from sprawl to compact development to sprawl. Thus they were simply summed, in effect giving each dimension of sprawl equal weight in the overall index. As with the individual sprawl factors, we transformed the overall compactness score into an index with a mean of 100 and a standard deviation of 25. This was done for the sake of consistency and ease of understanding. With this transformation, the more compact counties have index values above 100, while the more sprawling have values below 100. Appendix B contains compactness factors and refined county sprawl (compactness) indices for 967 county and county equivalents in 2010. Note that Massachusetts counties are missing from the mix factor and overall index for lack of LED data. The ten most compact and ten most sprawling counties are shown in Tables 3.6 and 3.7. The rankings are similar to those with the original county sprawl index. The most compact counties are central counties of large, older metropolitan areas. The most sprawling counties are outlying counties of large metropolitan areas, or component counties of smaller metropolitan areas. Values range from 42 for Oglethorpe County, GA outside Athens, the most sprawling county in 2010. Looking at Tables 1.2 and 2.6, it would seem that the original and new compactness indices are measuring the same construct, but that is not quite true. Just compare Tables 1.3 and 2.7, where there is no overlap in the most sprawling counties according to the two indices. The original compactness index is dominated by density variables (four of six variables in the index) and only slightly diluted by street variables (two of the six), which correlate strongly with density. The new compactness index dilutes the role of density by adding two new factors (mix and centering). The simple correlation coefficient between original and new indices is 0.865, which means that about 25 percent of the variance in each index is unexplained by the other. We would expect that they have similar but not identical relationships to outcome variables, and similar but not identical predictive power. Table 2.6. 10 Most Compact Counties in 2010 According to the Four-Factor Index (excluding Massachusetts counties)

1 2 3 4 5 6 7 8 9 10

County New York County, NY Kings County, NY San Francisco County, CA Bronx County, NY Philadelphia County, PA District of Columbia, DC Queens County, NY Baltimore city, MD Norfolk city, VA Hudson County, NJ

Metropolitan Area New York-Northern New Jersey-Long Island, NY-NJ-PA New York-Northern New Jersey-Long Island, NY-NJ-PA San Francisco-Oakland-Fremont, CA New York-Northern New Jersey-Long Island, NY-NJ-PA Philadelphia-Camden-Wilmington, PA-NJ-DE-MD Washington-Arlington-Alexandria, DC-VA-MD-WV New York-Northern New Jersey-Long Island, NY-NJ-PA Baltimore-Towson, MD Virginia Beach-Norfolk-Newport News, VA-NC New York-Northern New Jersey-Long Island, NY-NJ-PA

Index 425.2 265.2 251.3 224.0 207.2 206.4 204.2 190.9 179.6 178.7

Table 2.7. 10 Most Sprawling Counties in 2010 According to the Four-Factor Index (excluding Massachusetts counties)

19

960 961 962 963 964 965 967 966 968 969

County Spencer County, KY Morrow County, OH Brown County, IN Blount County, AL Greene County, NC Harris County, GA Macon County, TN Elbert County, CO Grant Parish, LA Oglethorpe County, GA

Metropolitan Area Louisville/Jefferson County, KY-IN Columbus, OH Indianapolis-Carmel, IN Birmingham-Hoover, AL Greenville, NC Columbus, GA-AL Nashville-Davidson--Murfreesboro--Franklin, TN Denver-Aurora-Broomfield, CO Alexandria, LA Athens-Clarke County, GA

Index 60.4 58.8 58.5 56.6 56.6 55.1 54.3 54.3 53.8 45.5

Greater Validity of New Index Compared to the original county compactness index, the new four-factor index has greater construct and face validity. It has greater construct validity because it captures four different dimensions of the construct “compactness” (density, mix, centering, and street accessibility), whereas the original index captures only two dimensions (density and street accessibility). The greater face validity of the new four-factor index requires some explanation. The very first county compactness indices were derived for only 448 counties in the largest 101 metropolitan areas. The most sprawling counties, such as Geauga County outside Cleveland, have classic sprawl patterns of lowdensity suburban development. Expanding to 994 counties and adding smaller metropolitan areas, the picture becomes more complicated. Tables 1.2 and 2.8 list the most compact counties as measured by both indices. The ten most compact counties based on the original index largely overlap with the top ten based on the new index (with the notable exception of Suffolk County (Boston), for which we don’t have all required variables). New York County (Manhattan) is the most compact according to both indices (see Figure 2.1). Kings County (Brooklyn) is the second most compact according to both indices (see Figure 2.2).

20

Figure 2.1. Most Compact County According to Both Indices (New York County, NY)

Figure 2.2. Second Most Compact County According to Both Indices (Kings County, NY)

However, the ten most sprawling counties are entirely different when measured by different indices (see Tables 1.3 and 2.9). Which index has greater face validity? We reviewed satellite imagery for the ten most sprawling counties, according to both indices, and found that the development patterns for the new index are much more representative of classic suburban sprawl (see Tables 2.8 and 2.9). While all 20 counties are part of metropolitan areas, many of the counties rated as most sprawling according to the original index have different development patterns than expected. They would best be described as exurban counties with small towns surrounded by farmlands (see Figures 2.3 and 2.4). The small towns have moderate densities and gridded streets. The fact they are part of larger census tracts, our 21

units of analysis, depresses their densities and compactness scores. They are not examples of classic suburban or exurban sprawl. On the other hand, the counties rated as most sprawling according to the new four-factor index have census tracts with very low-density residential development. Table 2.8. 10 Most Sprawling Counties in 2010 According to the Six-Variable Index

Ford County, IL Osage County, KS Jasper County, IN Grant County, AR Tipton County, IN Chester County, TN Morrow County, OH Greene County, NC Polk County, MN Jackson County, KS

Development Pattern Small town surrounded by rural development Small town surrounded by rural development Continuous low density suburban development Continuous low density suburban development Small town surrounded by rural development Continuous low density suburban development Continuous low density suburban development Continuous low density suburban development Small town surrounded by rural development Small town surrounded by rural development

Index 67.3 66.9 66.8 66.8 66.4 65.4 63.4 63.3 61.1 54.6

Table 2.9. 10 Most Sprawling Counties in 2010 According to the Four-Factor Index (excluding Massachusetts counties) County Spencer County, KY Morrow County, OH Brown County, IN Blount County, AL Greene County, NC Harris County, GA Macon County, TN Elbert County, CO Grant Parish, LA Oglethorpe County, GA

Metropolitan Area Louisville/Jefferson County, KY-IN Columbus, OH Indianapolis-Carmel, IN Birmingham-Hoover, AL Greenville, NC Columbus, GA-AL Nashville-Davidson--Murfreesboro--Franklin, TN Denver-Aurora-Broomfield, CO Alexandria, LA Athens-Clarke County, GA

Index 60.4 58.8 58.5 56.6 56.6 55.1 54.3 54.3 53.8 45.5

22

Figure 2.3. Most Sprawling County According to Six-Variable Index (Jackson County, KS)

Figure 2.4. Second Most Sprawling County According to Six-Variable Index (Polk County, MN)

23

Figure 2.5. Most Sprawling County According to Four-Factor Index (Oglethorpe County, GA)

Figure 2.6. Second Most Sprawling County According to Four-Factor Index (Grant Parish, LA)

24

Chapter 3. Derivation of Metropolitan Sprawl Indices Sprawl is ordinarily conceptualized at the metropolitan level, encompassing cities and their suburbs. When we say Atlanta sprawls badly, we are probably referring to metropolitan Atlanta, not the city of Atlanta or Fulton County. The focus up to this point in the report has been on counties, because counties are typically smaller than metropolitan areas and more homogeneous than metropolitan areas. They more closely correspond to the environment in which individuals live, work, and play on a daily basis, and hence are affected by the built environment. But certain phenomena are manifested at the regional or metropolitan level, such as ozone levels and racial segregation. So in this chapter we derive metropolitan sprawl indices.

Methods Sample The unit of analysis in this study is the metropolitan area. A metropolitan area is a region that consists of a densely populated urban core and its less-populated surrounding territories that are economically and socially linked to it. The criteria of defining metropolitan areas changed in 2003. Smaller MSAs remained the same, but larger metropolitan areas, previously referred to as consolidated metropolitan statistical areas (CMSAs) are now defined as MSAs. Different portions of CMSAs, previously referred to as primary metropolitan statistical areas (PMSAs), have been redefined and reconfigured as metropolitan divisions. For example, the old New York CMSA consisted of eleven counties in two states and four PMSAs: New York PMSA, Nassau-Suffolk PMSA, Dutchess County PMSA and Newburgh, NY-PA PMSA. The current New York MSA consists of twenty-three counties in three states and four metropolitan divisions. The New York MSA now is strikingly heterogeneous, whereas the old New York PMSA contained only the five boroughs that make up New York City. Metropolitan divisions do not perfectly substitute for PMSAs, as they have different size thresholds (2.5 million vs. 1 million population), but they come as close to representing homogenous units as we can come with current census geography. Metropolitan divisions are designated for each of the eleven largest MSAs.7 The sample in this study is limited to medium and large metropolitan areas, and metropolitan divisions where they are defined. It initially included a total of 228 areas with more than 200,000 population in 2010. The rationale for thus limiting our sample is simple: the concept of sprawl has particular relevance to large areas where the economic, social, and environmental consequences of sprawl can be significant. The concept of sprawl does not have much relevance to small MSAs such as Lewiston, ID and Casper, WY. Parenthetically, a total of seven metropolitan areas and divisions were ultimately dropped from our sample due to the lack of local employment dynamics (LED) data, a key data source for measuring sprawl. These metropolitan areas, or a portion of them, are located in Massachusetts, which does not participate in the LED program. This reduces the final sample size to 221 MSAs and metropolitan divisions. 7

The metropolitan divisions, as components of MSAs, somewhat resemble PMSAs under the old system. However, PMSAs were much more common. The higher population threshold for establishing metropolitan divisions (at least 2.5 million), opposed to the threshold of at least 1 million to establish PMSAs, means that the new system contains twenty-nine metropolitan divisions within eleven MSAs, compared to seventy-three PMSAs within eighteen CMSAs under the old system.

25

Variables Development Density Our first five density variables are the same as in the original sprawl index (Ewing et al., 2002): gross density of urban and suburban census tracts (popden), percentage of the population living at low suburban densities (lt1500), percentage of the population living at medium to high urban densities (gt12500), and urban density based on the National Land Cover Database (urbden). These variables are measured the same way for metropolitan areas as for counties (see Chapter 2). A fifth variable is the estimated density at the center of the metropolitan area derived from a negative exponential density function (dgcent). The function assumes the form: Di = Do exp (-b di). where: Di = the density of census tract i Do = the estimated density at the center of the metropolitan area b = the estimated density gradient or rate of decline of density with distance di = the distance of the census tract from the center of the principal city The higher the central density, and the steeper the density function, the more compact the metropolitan area (in a monocentric sense).8 The sixth density variable, which is new, is analogous to the first, except it is derived with employment data from the Local Employment Dynamics (LED) database (empden). The LED data were aggregated from census block geography to generate total jobs by 2-digit NAICS code for every block group in the nation. This was then divided by land area to produce a density measure. The last two variables are related to employment centers identified by the authors as a part of this study. For more information on how the centers were identified for MSAs see “Activity Centering” in Chapter 3.The two variables are weighted average population density (popdcen) and weighted average employment density (empdcen) of all centers within a metropolitan area. The average densities were weighted by the sum of block group jobs and residents as a percentage of the MSA total. Land Use Mix The two mixed-use variables were calculated for each block group’s buffer using block-level population data from the 2010 Census, and block-level employment data from the 2010 LED database. The first variable is a job-population balance measure (jobpop). This variable equals 1 for block groups with the same ratio of jobs-to-residents within the one-mile ring as the metropolitan area as a whole; 0 for block groups with only jobs or residents within the one-mile ring, not both; and intermediate values for 8

The function was estimated as follows. The principal cities of the metro areas were identified as the first-named cities in the 1990 definitions of those areas. Their centers were determined by locating central business district tracts within the principal cities as specified in the 1980 STF3 file. 1980 designations were adopted because central business districts have not been designated since then. The means of the latitudes and longitudes of the centroids of those central business district tracts were taken as the metropolitan centers. The distances from the centers to all tracts were calculated using an ArcGIS. Finally, a negative exponential density function was fit to the resulting data points to estimate the intercept and density gradient.

26

intermediate cases. All values were weighted by the sum of block group jobs and residents as a percentage of the MSA total. 9 We also derived a job mix variable (jobmix). The variable, an entropy measure, equals 1 for block groups with equal numbers of jobs in each sector; 0 for block groups with all jobs in a single sector within the ring; and intermediate values for intermediate cases. The sectors considered in this case were retail, entertainment, health, education, and personal services. Values were weighted by the sum of block group population and employment as a percentage of the MSA total. A third mixed-use variable is metropolitan weighted average Walk Score (walkscore). It was computed using data from Walk Score, Inc. to measure proximity to amenities, with different amenities weighted differently and amenities discounted as the distance to them increases up to one mile and a half, where they are assumed to be no longer accessible on foot.10 Classic Walk Score data were acquired for all urban census tracts in the United States. Values were weighted by the sum of census tract population and employment as a percentage of the MSA total. Activity Centering The first centering variable came straight out of Ewing et al. (2002) and the 2010 census. It is the coefficient of variation in census block group population densities, defined as the standard deviation of block group densities divided by the average density of block groups (varpop). The more variation in population densities around the mean, the more centering and/or subcentering exists within the MSA. The second centering variable is analogous to the first, except it is derived with employment data from the LED database. It is the coefficient of variation in census block group employment densities, defined as the standard deviation of block group densities divided by the average density of block groups (varemp). The more variation in employment densities around the mean, the more centering and/or subcentering exists within the MSAs. The third variable contributing to the centering factor is the density gradient moving outward from the CBD, estimated with a negative exponential density function. The faster density declines with distance from the center, the more centered (in a monocentric sense) the metropolitan area will be (dgrad). The next two centering variables measure the proportion of employment and population within CBDs and employment sub-centers. For computing them, we first identified the location of CBDs and employment sub-centers for all metropolitan areas (see “Activity Centering” section on Chapter 3). This procedure resulted in two new centering variables as the percentage of MSA population (popcen) and employment (empcen) in CBDs and sub-centers. Street Accessibility Street accessibility is related to block size since smaller blocks translate into shorter and more direct routes. Large block sizes indicate a lack of street connections and alternate routes. So, three street accessibility variables were computed for each MSA based on blocks size: average block length (avgblklngh), average block size (avgblksze) and the percentage of blocks that are less than 1/100 square mile, which is the typical size of an urban block (smlblk). 9

See “land use mix” section for the formula used for computing job-population balance and job mix measures. A grocery store, for example, gets three times the weight of a book score. The distance decay function starts with a value of 100 and decays to 75 percent at a half mile, 12.5 percent at one mile, and zero at 1.5 miles. 10

27

These three variables were part of Ewing et al.’s original sprawl metrics. To them, we have added two new variables. They are intersection density and percentage of 4-or-more way intersections. Intersections are where street connections are made and cars must stop to allow pedestrians to cross. The higher the intersection density, the more walkable the city (Jacobs, 1993). Intersection density has become the most common metric in studies of built environmental impacts on individual travel behavior (Ewing and Cervero, 2010). Another common metric in such studies is the percentage of 4-or-more-way intersections (Ewing and Cervero, 2010). This metric provides the purest measure of street connectivity, as 4-way intersections provide more routing options than 3-way intersections. A high percentage of 4-way intersections does not guarantee walkability, as streets may connect at 4-way intersections in a super grid of arterials. But it does guarantee routing options. For each MSA, the total number of intersections in the urbanized portion of MSA was divided by the land area to obtain intersection density (intden), while the number of 4-or-more-way intersections was multiplied by 100 and divided by the total number of intersections to obtain the percentage of 4-ormore way intersections (4way).

Results Individual Compactness/Sprawl Factors For each dimension of sprawl, we ran principal component analysis on the measured variables, and the principal component that captured the largest share of common variance among the measured variables was selected to represent that dimension. Factor loadings (the correlation between a variable and a principal component), eigenvalues (the explanatory power of a single principal component), and percentages of explained variance are shown in Table 3.1. The eigenvalue of the density factor is 5.82, which indicates that this one factor accounts for about three quarters of the total variance in the dataset. As anticipated, the percentage of the population living at less than 1,500 persons per square mile loads negatively on the density factor. The rest load positively. The eigenvalue for the mix factor is 2.30, which indicates that this one factor accounts for more than three quarter of the total variance in the dataset. All component variables load positively on the mix factor. The eigenvalue of the centering factor is 1.90, which indicates that this factor accounts for about 38% of the total variance in the datasets. The density gradient loads negatively on centering factor as expected. The rest load positively. The eigenvalue of the street factor is 2.51, which indicates that this factor accounts for more than a half of the total variance in the dataset. As expected, the average block size and average block length load negatively on the street accessibility factor. The rest load positively. Table 3.1: Variable Loadings of Four Factors for 2010

28

Component Matrix Density Factor popden gross population density empden gross employment density lt1500 percentage of the population living at low suburban densities gt12500 percentage of the population living at medium to high urban densities urbden net population density of urban lands dgcent estimated density at the center of the metro area derived from a negative exponential density function popdcen weighted average population density of centers empdcen weighted average employment density of centers Eigenvalue Explained variance Mix use Factor jobpop job-population balance jobmix degree of job mixing (entropy) walkscore weighted average Walk Score Eigenvalue Explained variance Centering Factor varpop coefficient of variation in census block group population densities varemp coefficient of variation in census block group employment densities dgrad density gradient moving outward from the CBD

Data Sources

Census 2010 LED 2010 Census 2010

0.900 0.898 -0.597

Census 2010

0.879

NLCD Census 2010, Tiger 2010

0.925 0.948

Census 2010 LED 2010

0.810 0.817 5.82 72.80%

LED 2010 LED 2010 Walk Score Inc.

0.834 0.921 0.870 2.30 76.72%

Census 2010

0.495

LED 2010

0.313

popcen

percentage of MSA population in CBD or sub-centers

Census 2010, Tiger 2010 Census 2010

empcen

percentage of MSA employment in CBD or sub-centers

LED 2010

Eigenvalue Explained variance Street Factor smlblk percentage of small urban blocks avgblksze average block size avgblklng average block length intden intersection density 4way percentage of 4-or-more-way intersections Eigenvalue Explained variance

Factor Loadings

-0.375 0.833 0.847 1.90 37.89%

Census 2010 Census 2010 NAVTEQ 2012 TomTom 2007 TomTom 2007

0.871 -0.804 -0.649 0.729 0.380 2.51 50.03%

29

Overall Compactness/Sprawl Index for 2010 Although density has received more attention as a dimension of sprawl than have other factors, similar to Ewing et al. (2002) we could think of no rationale for giving different weights to the four factors. All four factors affect the accessibility or inaccessibility of development patterns. Each factor can move a MSA along the continuum from sprawl to compact development. Thus the four were simply summed, in effect giving each dimension of sprawl equal weight in the overall index. The second and more difficult issue was whether to, and how to, adjust the resulting sprawl index for MSA size. As areas grow, so do their labor and real estate markets, and their land prices. Their density gradients accordingly shift upward, and other measures of compactness (intersection density, for example) follow suit. The simple correlation between the sum of the four sprawl factors and the population of the MSA is 0.575, significant at .001 probability level. Thus, the largest urbanized areas, perceived as the most sprawling by the public, actually appear less sprawling than smaller urbanized areas when sprawl is measured strictly in terms of the four factors, with no consideration given to area size. We used the same methodology as Ewing et al (2002) to account for metropolitan area size. We regressed the sum of the four sprawl factors on the natural logarithm of the population of the MSAs. The standardized residuals became the overall measure of sprawl. As such, this index is uncorrelated with population. However, the overall index still has a high correlation (r=0.866) with the sum of four factors before adjustment. We transformed the overall sprawl index into a metric with a mean of 100 and a standard deviation of 25 for ease of use and understanding. More compact metropolitans have index values above 100, while the more sprawling have values below 100. Table 3.2 presents overall compactness scores and individual component scores for the 10 most compact and the 10 most sprawling large metropolitan areas. By these metrics, New York and San Francisco are the most compact large metropolitan divisions (see Figures 3.1a&b), while Hickory, NC and Atlanta, GA are the most sprawling metropolitan areas (see Figure 3.2a&b). These figures are at the same scale, and is clear that the urban footprints of the former are more concentrated than those of the latter. Again all metropolitan areas and divisions in Massachusetts, including the Boston metropolitan division, are not in the list due to the lack of available employment data (LED) for this state. Table 3.2. Compactness/Sprawl Scores for 10 Most Compact and 10 Most Sprawling metropolitan areas and divisions in 2010 Rank

index

denfac

mixfac

cenfac

strfac

203.4

384.3

159.3

213.5

193.8

194.3

185.9

167.2

230.9

162.8

150.4

112.3

148.9

109.5

122.1

146.6

100.8

93.7

137.3

94.1

145.2

160.2

136.4

117.9

166.9

145.0

96.3

100.1

154.5

130.7

Ten Most Compact Metropolitan Areas 1 2 3

New York-White Plains-Wayne, NY-NJ Metro Division San Francisco-San Mateo-Redwood City, CA Metro Division Atlantic City-Hammonton, NJ Metro Area

5

Santa Barbara-Santa Maria-Goleta, CA Metro Area Champaign-Urbana, IL Metro Area

6

Santa Cruz-Watsonville, CA Metro Area

4

30

7

Trenton-Ewing, NJ Metro Area

144.7

98.9

146.2

107.9

112.2

8

Miami-Miami Beach-Kendall, FL Metro Division

144.1

100.0

123.3

153.6

82.8

9 10

Springfield, IL Metro Area

142.2 139.9

142.1 104.8

105.0 117.8

136.4 96.1

114.3 149.9

Santa Ana-Anaheim-Irvine, CA Metro Division

Ten Most Sprawling Metropolitan Areas 212

Kingsport-Bristol-Bristol, TN-VA Metro Area

60.0

85.2

60.7

88.5

73.9

213

Augusta-Richmond County, GA-SC Metro Area

59.2

88.1

60.6

100.8

82.5

214

Greenville-Mauldin-Easley, SC Metro Area

59.0

91.1

71.7

72.6

71.8

215

Riverside-San Bernardino-Ontario, CA Metro Area Baton Rouge, LA Metro Area

56.2

97.9

110.3

70.5

96.2

55.6

88.2

80.6

84.9

70.7

51.7

91.3

72.0

69.7

80.4

216

218

Nashville-Davidson--Murfreesboro--Franklin, TN Metro Area Prescott, AZ Metro Area

49.0

84.5

39.7

74.5

60.8

219

Clarksville, TN-KY Metro Area

41.5

86.7

72.9

81.1

71.4

220

Atlanta-Sandy Springs-Marietta, GA Metro Area

41.0

97.8

85.5

89.9

75.9

221

Hickory-Lenoir-Morganton, NC Metro Area

24.9

78.6

40.5

67.0

56.9

217

Figure 3.1. Most Compact Metropolitan Areas (New York and San Francisco)

Figure 3.2. Most Sprawling Metropolitan Areas (Atlanta and Hickory, NC)

31

Discussion This study used the same basic methodology as Ewing et al. (2002) to measure the sprawl for medium and large metropolitan areas and divisions in 2010. We also expanded the sample size from 83 metropolitan areas in Ewing et al. (2002) to the 221 MSAs in this study. For the 76 areas that are included in both studies, the compactness rankings are generally consistent across years. The Spearman correlation between the compactness rankings in 2000 and 2010 is 0.635, significant at .001 probability level which indicates, in general, the compact areas in 2000 are found to be still compact in 2010; and the sprawling areas in 2000 are still sprawling. New York is the most compact region followed by San Francisco in both years. Atlanta is the fourth most sprawling area in 2000 and the most sprawling area in 2010. Riverside-San Bernardino-Ontario, CA is the most sprawling in 2000 and the third most sprawling area in 2010. There are, however, metropolitan areas with significantly different ranking in 2010 than 2000. One of the surprising cases is the Las Vegas-Paradise, NV metropolitan area. Its ranking rises from the 30th most compact area in 2000 to the 16th in 2010 due to its moderate to high score in all four dimensions. This is consistent with Fulton et al. (2001) study that found Las Vegas is getting more compact. “Las Vegas led the nation with an increase in its metropolitan density of 50 percent, thus rising in the overall density rankings from 114th in 1982 to 14th in 1997” (Fulton et al. 2001, p: 7). Refinements in operationalizing sprawl, is another reason for differences in rankings between years. Land use mix and activity centering are the two dimensions with the most significant changes. As contributors to centering, we now consider not only central business districts (CBDs) but employment sub-centers. The existence of sub-centers is what distinguishes polycentric regions from monocentric regions. The Washington DC metropolitan division is an example of polycentric region. As shown in Figure 3.3, we identified 11 sub-centers (yellow color) in the metropolitan division. Out of 76 metropolitan areas with rankings in both years, the Washington DC metropolitan division has the 27th highest score for activity centering in 2010 while it had the 41st highest score in 2000. Its overall compactness ranking rises from 52nd most compact in 2000 to 27th most compact in 2010 due to its change on the centering score. We also standardized the unit of analysis for mix use metrics by measuring them in half mile buffers from the centroid of block groups. Out of 76 areas that are included in both years, Phoenix has the 19th highest mix factor score in 2000 while it has the 24th lowest mix score in 2010. As a result, the Phoenix metropolitan area’s overall ranking drops from 18th most compact in 2000 to 14th most sprawling in 2010. Finally, the changes in compactness score in some areas are due to changes in metropolitan boundaries. Out of 76 metropolitan areas in both samples, Detroit moved up from 14th most sprawling in 2000 to 5th most compact in 2010. The 2010 Detroit, MI metropolitan division covers only about a fifth of the area of the 2000 Detroit PMSA. The division is mostly limited to the Detroit’s downtown and surroundings. The lowest density portions of Detroit PMSA are not included in 2010 metropolitan division (see Figure 3.4). In particular, Warren-Troy-Farmington Hills, MI is now its own metropolitan division, and a very sprawling one, the 20th most sprawling out of 221 metropolitan areas in 2010.

32

Figure 3.3. Central Business District and Employment Sub-centers in Washington DC Metropolitan Division

Figure 3.4. Detroit 2010 Metropolitan Division (dark) versus Detroit 2000 PMSA Boundary (light)

33

Chapter 4. Urbanized Areas: A Longitudinal Analysis In this chapter we seek to measure changes in sprawl by developing refined and enhanced compactness/sprawl indices for 2000 and 2010 based on definitions and procedures in Ewing et al. (2002, 2003), but refined and applied this time to urbanized areas (UZAs) rather than metropolitan areas or counties. We chose census UZAs as our units of analysis because UZAs are the only census geographies that expand systematically with urban development over time. Counties have fixed boundaries and hence tend to appear more compact over time (except when counties are losing population as in Detroit or New Orleans after Katrina). Metropolitan areas expand in large increments as entire counties, both urban and rural portions, are added to core counties to reflect changing commuting patterns and social and economic integration.

Methods Sample The term “urbanized area “as defined by the U.S. Census Bureau denotes an urban area of 50,000 or more people. Urban areas are defined by core census block groups or blocks with population densities of at least 1,000 people per square mile and surrounding census blocks with densities of at least 500 people per square mile. Urbanized areas often provide a more accurate gauge of city size than do the incorporated political boundaries of cities. This investigation is limited to large urbanized areas. Our sample consists of the 162 largest urbanized areas in the United States, those with more than 200,000 population in 2010. The rationale for thus limiting our sample is simple: the concept of sprawl has particular relevance to large areas where the economic, social, and environmental consequences of sprawl can be significant. The concept of sprawl does not have much relevance to small urbanized areas such as Pine Bluff, AR and Monroe, MI. Variables Development Density Our first four density variables are the same as in the original sprawl index, gross density of urban and suburban census tracts (popden), percentage of the population living at low suburban densities (lt1500), percentage of the population living at medium to high urban densities (gt12500), and urban density based on the National Land Cover Database (urbden). The fifth density variable is analogous to the first, except it is derived with employment data from the Local Employment Dynamics (LED) database rather than population data (empden). In this case, LED data were processed for the years 2005 and 2010. Year 2005 is the earliest year that LED data is available for all states (except Massachusetts).

MEASURING URBAN SPRAWL AND VALIDATING SPRAWL MEASURES

Land Use Mix Although using the same variables as Ewing et al. (2002) to operationalize mixed use, we computed them differently using one-mile buffers around the centers of block groups rather than computing them within the boundaries of block groups. The two mixed use variables were calculated for each block group’s buffer using block-level population data from the 2010 Census, and block-level employment data from the 2010 LED database. The resulting job and population totals were used to compute a job-population balance measure (jobpop). This variable equals 1 for block groups with the same ratio of jobs-to-residents within the one-mile ring as the urbanized area as a whole; 0 for block groups with only jobs or residents within the one-mile ring, not both; and intermediate values for intermediate cases. All values were weighted by the sum of block group jobs and residents as a percentage of the UZA total. For the second mixed-use variable, each block group centroid was again buffered with a one-mile ring, and jobs by sector were summed for blocks within the ring. An entropy formula was then used to compute a measure of job mix (jobmix). The variable equals 1 for block groups with equal numbers of jobs in each sector within the ring; 0 for block groups with all jobs in a single sector within the ring; and intermediate values for intermediate cases. The sectors considered in this case were retail, entertainment, health, education, and personal services. Values were weighted by the sum of block group population and employment as a percentage of the urbanized areas total. 11 Unlike the mixed use factors at the county and metropolitan levels, the mixed use factor at the urbanized area level does not include a third variables, Walk Score. The reason is simple. This is longitudinal comparison of sprawl in 2000 and 2010, and Walk Score data were not available until 2007. Activity Centering The first centering variable came straight out of Ewing et al. (2002, 2003) and the 2010 census. It is the coefficient of variation in census block group population densities, defined as the standard deviation of block group densities divided by the average density of block groups (varpop). The more variation in population densities around the mean, the more centering and/or subcentering exists within the urbanized areas. The second centering variable is analogous to the first, except it is derived with employment data from the LED database. It is the coefficient of variation in census block group employment densities, defined as the standard deviation of block group densities divided by the average density of block groups (varemp). The more variation in employment densities around the mean, the more centering and/or subcentering exists within the urbanized areas.

11

See “land use mix” section for the formula used for computing job-population balance and job mix measures.

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The next two centering variables measure the proportion of employment and population within CBDs and employment sub-centers. We first identified the location of CBDs and employment sub-centers for all metropolitan areas (see Chapter 3). This procedure resulted in two new centering variables as the percentage of UZA population (popcen) and employment (empcen) in CBDs and sub-centers. Street Accessibility Street accessibility is related to block size since smaller blocks translate into shorter and more direct routes. Large block sizes indicate a relative paucity of street connections and alternate routes. So, two street accessibility variables were computed for each urbanized area: average block size (avgblk) and percentage of blocks with areas less than 1/100 square mile, the size of a typical traditional urban block bounded by sides just over 500 feet in length (smlblk). These two variables were part of Ewing et al.’s original sprawl metrics. To them, we have added two new variables. They are intersection density and percentage of 4-or-more way intersections. For each UZA, the total number of intersections in the UZA was divided by the land area to obtain intersection density (intden), while the number of 4-or-more-way intersections was multiplied by 100 and divided by the total number of intersections to obtain the percentage of 4-or-more way intersections (4way). Statistical Methods In this study we use two statistical methods. Principal component analysis (a type of factor analysis) is used to derive individual compactness indices that represent the built environments of UZAs. Then linear regression analysis is used to relate these indices to transportation outcomes, controlling for influences other than the built environment. For each dimension of sprawl, principal components were extracted from the component variables. The principal component selected to represent the dimension was the one capturing the largest share of common variance among the component variables, that is, the one upon which the observed variables loaded most heavily. Because, in this study, the first component captured the majority of the combined variance of these variables, no subsequent components were considered. The other statistical method used in this study is linear regression (ordinary least squares or OLS). Our dependent variables were logged so as to be normally distributed and hence properly modeled with regression analysis. As for the independent variables (control variables), we transformed all variables into log form to achieve a better fit with the data, reduce the influence of outliers, and adjust for nonlinearity of the data. The transformations have the added advantage of allowing us to interpret regression coefficients as elasticities. An elasticity is a percentage change in one variable that accompanies a one percent change in another variable. Elasticities are the most common measures of effect size in both economics and planning.

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Results Individual Compactness/Sprawl Factors Factor loadings (that is, correlations of these variables with each factor), eigenvalues, and percentages of explained variance are shown in Table 4.1. The eigenvalue of the density factor is 3.82, which means that this one factor accounts for more of the total variance in the datasets than three component variables combined, more than three quarters of the total variance. As expected, one of the variables loads negatively on the density factor, that being the percentage of population living at less than 1,500 persons per square mile. The rest load positively. Thus, for all component variables, higher densities translate into higher values of the density factor. The eigenvalue of the mix factor is 1.54, which means that this one factor accounts for more than three quarters of the total variance in the dataset. Both component variables load positively on the mix factor. The eigenvalue of the centering factor is 2.20, which means that this one factor accounts for just over half of the total variance in the datasets. All component variables load positively on the centering factor. The eigenvalue of the street factor is 2.75, which means that this one factor accounts for two-thirds of the total variance in the dataset. As expected, one of the variables loads negatively on the street accessibility factor, that being the average block size. The rest load positively. Thus, for all component variables, more street accessibility translates into higher values of the street factor. Table 4.1. Variable Loadings on Four Factors for 2010 Component Matrix Density Factor popden gross population density empden gross employment density lt1500 percentage of the population living at low suburban densities gt12500 percentage of the population living at medium to high urban densities urbden net population density of urban lands Eigenvalue Explained variance Mix use Factor jobpop job-population balance jobmix degree of job mixing (entropy) Eigenvalue Explained variance Centering Factor varpop coefficient of variation in census block group population densities

Data Sources

2010 Factor Loadings

Census 2010 LED 2010 Census 2010

0.970 0.891 -0.806

Census 2010

0.745

NLCD

0.941 3.82 76.5%

LED 2010 LED 2010

0.879 0.879 1.54 77.2%

Census 2010

0.661

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varemp

coefficient of variation in census block group employment densities percentage of UZA population in CBD or sub-centers percentage of UZA employment in CBD or sub-centers

popcen empcen Eigenvalue Explained variance Street Factor smlblk percentage of small urban blocks avgblksze average block size intden intersection density 4way percentage of 4-or-more-way intersections Eigenvalue Explained variance

LED 2010

0.749

Census 2010 LED 2010

0.757 0.790 2.20 54.8%

Census 2010 Census 2010 TomTom 2007 TomTom 2007

0.844 -0.947 0.726 0.784 2.75 68.8%

Overall Compactness/Sprawl Index for 2010 Some of the technical literature on sprawl includes size in the definition. Certainly, sheer geographic size is central to popular notions of sprawl. Despite their relatively high densities, urbanized areas such as Los Angeles and Phoenix are perceived as sprawling because they “go on forever.” A sprawl index that disregarded this aspect of urban form would never achieve face validity. Accordingly, we sought a method of transforming the sum of the four sprawl factors into a sprawl index that would be neutral with respect to population size. In this study, we use the exact same procedure used with metropolitan area sprawl in the early 2000s (Ewing et al. 2002). The transformation was accomplished by regressing the sum of the four sprawl factors on the natural logarithm of the population of the urbanized area. The standardized residuals (difference between actual and estimated values divided by the standard deviation of the difference) became our overall measure of sprawl. Given the way it was derived, this index is uncorrelated with population. Urbanized areas that are more compact than expected, given their population size, have positive values. Urbanized areas that are more sprawling than expected, again given their population size, have negative values. This adjustment for population size still leaves the sprawl index highly correlated with the sum of the four component factors (r = 0.87). As with the individual sprawl factors, we transformed the overall sprawl index (index) into a metric with a mean of 100 and a standard deviation of 25. This was done for the sake of consistency and ease of understanding. With this transformation, the more compact urbanized areas have index values above 100, while the more sprawling have values below 100. Table 4.2 presents overall compactness scores and individual component scores for the ten most compact and the ten most sprawling large urbanized areas. By these metrics, San Francisco is the most compact large urbanized area, and Atlanta is the most sprawling.

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Table 4.2. Compactness/Sprawl Scores for 10 Most Compact and 10 Most Sprawling UZAs in 2010 Rank comfac denfac mixfac cenfac strfac Ten Most Compact UZAs San Francisco-Oakland, CA 180.94 205.69 129.92 164.34 153.38 1 Reading, PA 169.32 127.71 150.87 124.45 147.46 2 Madison, WI 152.87 118.16 121.82 182.19 99.33 3 Eugene, OR 152.54 114.84 134.37 134.15 123.07 4 Laredo, TX 151.80 123.87 131.21 81.56 166.54 5 Oxnard, CA 146.19 147.55 137.14 82.42 135.08 6 Atlantic City, NJ 144.25 93.87 91.07 157.06 143.86 7 Los Angeles-Long Beach-Anaheim, CA 143.42 212.21 144.75 102.23 138.92 8 Lincoln, NE 143.38 118.63 127.46 97.02 141.77 9 New York-Newark, NY-NJ-CT 142.71 197.50 106.80 179.10 125.06 10 Ten Most Sprawling UZAs Baton Rouge, LA 153 Fayetteville, NC 154 Chattanooga, TN-GA 155 Greenville, SC 156 Nashville-Davidson, TN 157 Charlotte, NC-SC 158 Winston-Salem, NC 159 Victorville-Hesperia, CA 160 Hickory, NC 161 Atlanta, GA 162

64.38 61.05 60.96 60.57 60.27 57.41 55.56 54.15 48.64 37.45

81.92 79.40 68.92 67.92 87.51 82.95 66.31 82.38 46.92 84.64

75.30 73.65 54.18 75.26 47.43 64.56 68.97 67.79 78.41 75.63

77.21 67.16 97.03 89.88 111.18 115.94 88.15 57.01 72.20 107.29

77.61 64.43 70.33 57.88 70.03 53.01 54.29 61.88 44.94 36.84

Overall Compactness/Sprawl Index for 2000 To make apples to apples comparisons between two years (2000 and 2010), we applied the factor coefficient matrices for four principal components in 2010 to built environmental variable values for 2000. This resulted in compactness factors for 2000 that are consistent with those for 2010. Table 4.3 presents overall compactness scores and component scores for the ten most compact and the ten most sprawling large urbanized areas in 2000. As one would expect, rankings did not change dramatically in most cases over the ten years. San Francisco was the most compact in 2000, and has remained so. Atlanta was the most sprawling in 2000, and has remained so. Table 4.3 Compactness/Sprawl Scores for 10 Most Compact and 10 Most Sprawling UZAs in 2000 Rank comfac denfac mixfac cenfac strfac Ten Most Compact UZAs

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San Francisco-Oakland, CA 1 Laredo, TX 2 Reading, PA 3 Eugene, OR 4 New Orleans, LA 5 Stockton, CA 6 Madison, WI 7 Visalia, CA 8 New York-Newark, NY-NJ-CT 9 Lincoln, NE 10 Ten Most Sprawling UZAs 153 Fayetteville, NC 154 Baton Rouge, LA 155 Palm Bay-Melbourne, FL 156 Nashville-Davidson, TN 157 Victorville-Hesperia, CA 158 Winston-Salem, NC 159 Bonita Springs, FL 160 Chattanooga, TN-GA 161 Hickory, NC 162 Atlanta, GA

184.06 174.12 155.74 151.42 149.64 147.55 147.2 145.05 141.75 141.19

219.66 134.65 119.44 121.5 161.24 134.42 122.06 116.84 197.18 118.03

128.39 148.02 157.15 141.47 106.84 145.18 126.86 142.48 115.6 133.12

162.41 86.2 126.12 130.73 95.97 104.41 158.37 107.53 170.57 97.15

149.84 189.55 118.53 114.89 181.06 124.09 101.3 108.93 120.19 135.15

64.13 61.39 58.18 58.11 55.43 53.49 52.49 49.7 48.76 39.5

78.97 83.46 76.29 89.26 74.79 66.67 76.78 65.83 49.14 88.54

98.97 72.66 75.93 67.83 84.24 68.56 77.85 55.21 81.34 90.28

62.63 85.07 62.16 106.22 56.75 93.67 61.38 92.3 75.33 106.29

56.65 64.16 77.64 46.1 51.04 44.02 46.22 53.9 42.67 19.9

Discussion This chapter developed and sought to validate an overall measure of compactness/sprawl for U.S. urbanized areas in 2010. By these measures, San Francisco is the most compact urbanized area in the nation, and Atlanta is the most sprawling. Once we had measures of compactness for 2010, we were able to apply the same factor coefficients to data for 2000, thus generating consistent measures of compactness for 2000 and allowing longitudinal comparisons. Generalizing across the entire universe of large urbanized areas, compactness decreased and sprawl increased between the two census years, but only slightly. Summing the four indices of compactness (each with an average score of 100 in 2010), the average combined score was 405.8 in 2000, dropping to 400 in 2010, a relatively small change. This means that that on average, urbanized areas became less compact between 2000 and 2010. The compactness/sprawl measures have the additional quality of face validity. They paint a plausible picture of sprawl in the U.S.

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Chapter 5. Derivation of Census Tract Sprawl Indices The concept of sprawl naturally brings to mind large geographic areas. When we say Atlanta sprawls badly, we are referring to the Atlanta Metropolitan Area, or perhaps if we are a transportation planner, to the Atlanta Urbanized Area. From the earliest writings on sprawl, sprawl was said to occur primarily at the periphery of urbanized areas moving outward. An individual street or block may contribute to sprawl, but we would not say it is sprawl. This distinction seems particularly poignant when we talk about population and employment centering, which is defined by interrelationships among block groups. If one block group or a group of them has a significantly higher density than those surrounding it, we can say the former serves as a center for the block groups surrounding it. Yet, we know from the travel and public health literatures that there is a demand in the research community for built environmental metrics at the sub-county level, what might be described as the community or neighborhood scale. Most of the built environment-travel studies, and most of the built environment-obesity studies have related individual outcomes to such smaller areas. Therefore, we have derived sprawl-like metrics for census tracts within metropolitan areas, and posted them along with metropolitan area, urbanized area, and county sprawl metrics on the NIH website ((http://gis.cancer.gov). We have used the same type of variables as in larger area analyses, extracted principal components from multiple variables using principal component analysis, and once again, transformed the first principal component to an index with the mean of 100 and a standard deviation of 25. The component variables are: Table 1.1: Variable Loadings on the Census Tract Compactness Index for 2010 Component Matrix Data Sources Density Factor popden gross population density empden gross employment density jobpop job-population balance jobmix degree of job mixing (entropy) walkscore weighted average Walk Score smlblk percentage of small urban blocks avgblksze average block size intden intersection density 4way percentage of 4-or-more-way intersections Eigenvalue Explained variance

Census 2010 LED 2010 LED 2010 LED 2010 Walk Score Inc. Census 2010 Census 2010 TomTom 2007 TomTom 2007

Factor Loadings 0.596 0.207 0.374 0.620 0.864 0.778 -0.785 0.827 0.730 4.11 45.63%

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Chapter 6. Conclusion This study has updated a county and metropolitan compactness/sprawl indices, widely used by planning and public researchers since their release in 2002 and 2003. The updated indices reflect conditions on the ground circa 2010. This study has also developed new measures of compactness/sprawl that incorporate additional dimensions of the construct “sprawl,” and used additional variables to operationalize these dimensions. The four dimensions, measured individually and with a composite index, are development density, land use mix, activity centering, and street accessibility. Measures, presented in the Appendices, are immediately available to study the costs and benefits of different urban forms. Using updated and enhanced measures of compactness/sprawl, this study has validated both the original and new indices, and largely validated the individual measures representing the four dimensions of sprawl. These new results mirror and confirm the earlier findings of Ewing et al. (2002, 2003a, 2003b, 2003c). If anything, relationships of sprawl to important quality-of-life outcomes are stronger than in the original studies. An obvious question is whether the new measures have more face, construct, and internal validity than the original compactness/sprawl indices, and thus should substitute for the original indices in future research. They have more face validity because places that fit the definition of sprawl in satellite imagery rank lowest on compactness. They have more construct validity because they capture more aspect of sprawl. As for internal validity, they generally outperform the original county sprawl indices as predictors of negative outcomes. The new multi-dimensional factors representing density, mix, centering, and streets are somewhat correlated, of course, but still quite distinct in their relationships to outcomes. We can see these being used to determine which specific aspects of sprawl result in costs and benefits.

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References Anselin, L. (1995). Local indicators of spatial association—LISA. Geographical analysis, 27(2), 93-115. Baumont, C., Ertur, C., Le Gallo, J. (2003). 4 Spatial Convergence Clubs and the European Regional Growth Process, 1980. European regional growth, 131. Bereitschaft, B., and K. Debbage. 2013. Urban Form, Air Pollution, and CO2 Emissions in Large U.S. Metropolitan Areas. The Professional Geographer, 65(4), 612–635. Cho, S., Z. Chen, S.T. Yen, and D.B. Eastwood. 2006. The Effects of Urban Sprawl on Body Mass Index: Where People Live Does Matter. The 52nd Annual ACCI Conference, Baltimore, Maryland, March 15–18. Craig, S. G., and P. T. Ng. 2001. Using quantile smoothing splines to identify employment subcenters in a multicentric urban area. Journal of Urban Economics 49: 100-120. Doyle, S., A. Kelly-Schwartz, M. Schlossberg, and J. Stockard. 2006. Active Community Environments and Health: The Relationship of Walkable and Safe Communities to Individual Health. Journal of the American Planning Association. 72(1), 19–31. Ewing, R. (1997). Is Los Angeles-style sprawl desirable? Journal of the American Planning Association, 63(1), 107-126. Ewing, R. and Cervero, R. (2001). Travel and the built environment. Transportation Research Record, 1780, 87-114. Ewing R, Pendall R, Chen D. 2002. Measuring Sprawl and Its Impacts. Washington, DC: Smart Growth America. Ewing, R., R. Pendall, and D. Chen. 2003a. Measuring Sprawl and Its Transportation Impacts. Transportation Research Record. 1832: 175-183. Ewing R.,T. Schmid, R. Killingsworth, A. Zlot, and S. Raudenbush. 2003b. Relationship Between Urban Sprawl and Physical Activity, Obesity, and Morbidity. American Journal of Health Promotion.18:47-57. Ewing, R., R. Schieber and C. Zegeer. 2003c. Urban Sprawl as a Risk Factor in Motor Vehicle Occupant and Pedestrian Fatalities, American Journal of Public Health 93: 1541-1545. Ewing R., R. Brownson, and D. Berrigan. 2006. Relationship Between Urban Sprawl and Weight of U.S. Youth. American Journal of Preventive Medicine. 31: 464-474. Ewing, R. and F. Rong. 2008. Impact of Urban Form on U.S. Residential Energy Use. Housing Policy Debate. 19: 1-30.

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Ewing, R., Cervero, R.: Travel and the built environment: a meta-analysis. Journal of the American Planning Association 76(3), 265–294 (2010) Fan, Y. and Y. Song. 2009. Is Sprawl Associated with a Widening Urban–Suburban Mortality Gap? Journal of Urban Health: Bulletin of the New York Academy of Medicine. 86(5): 708-728. Fulton, W. Pendall, R, Nguyen. M. and Harrison, A. 2001. Who Sprawls Most? How Growth Patterns Differ Across the U.S., Center for Urban & Metropolitan Policy, The Brookings Institution, Washington, D.C. Giuliano, G., & Small, K. A. (1991). Subcenters in the Los Angeles region.Regional science and urban economics, 21(2), 163-182. Griffin, B.A., Eibner, C., Bird, C.E., Jewell, A., Margolis, K., Shih, R., Escarce, J.J. (2013). The Relationship between Urban Sprawl and Coronary Heart Disease in Women. Health & place, 20, 51–61. Holcombe, R.G., and Williams, D.W. (2012). Urban Sprawl and Transportation Externalities. The Review of Regional Studies, 40(3), 257-272. Joshu, C. E., Boehmer, T. K., Brownson, R. C., & Ewing, R. (2008). Personal, neighbourhood and urban factors associated with obesity in the United States. Journal of Epidemiology and community Health, 62, 202-208. Kahn, M.E. 2006. The Quality of Life in Sprawled versus Compact Cities,” prepared for the OECD ECMT Regional Round, Berkeley, California, March 2006, Table 137, 27–28. Kelly-Schwartz, A., J. Stockard, S. Doyle, and M. Schlossberg (2004). Is Sprawl Unhealthy? A Multilevel Analysis of the Relationship of Metropolitan Sprawl to the Health of Individuals. Journal of Planning Education and Research. 24: 184–196. Kim, D., Subramanian, S.V., Gortmaker, S.L., Kawachi, I. (2006). U.S. state-and county-level social capital in relation to obesity and physical inactivity: a multilevel, multivariable analysis. Social science & medicine, 63(4), 1045-1059. Lee, I.M., R. Ewing, and H.D. Sesso. 2009. The Built Environment and Physical Activity Levels: The Harvard Alumni Health Study. American Journal of Preventive Medicine, 37(4): 293-298. Lee, B. (2007). “EDGE” OR “EDGELESS” CITIES? URBAN SPATIAL STRUCTURE IN US METROPOLITAN AREAS, 1980 TO 2000*. Journal of Regional Science, 47(3), 479-515. McDonald, N., Trowbridge, M. (2009). Does the built environment affect when American teens become drivers? Evidence from the 2001 National Household Travel Survey. Journal of Safety Research, 40(3), 177-183.

89

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McMillen, D. P. (2004). Employment densities, spatial autocorrelation, and subcenters in large metropolitan areas. Journal of Regional Science, 44(2), 225-244. Nguyen D, 2010. Evidence of the impacts of urban sprawl on social capital. Environment and Planning B: Planning and Design 37(4) 610 – 627. Plantinga, A. and S. Bernell. 2007. The Association between Urban Sprawl and Obesity: Is It a Two-Way Street? Journal of Regional Science, 47(5): 857–879. Riguelle, F., Thomas, I., Verhetsel, A. (2007). Measuring urban polycentrism: a European case study and its implications. Journal of Economic Geography, 7(2), 193-215. Schweitzer, L. and Zhou, J. 2010. Neighborhood Air Quality Outcomes in Compact and Sprawled Regions, Journal of the American Planning Association, 76(3): 363-371 Stone, B. 2008. Urban Sprawl and Air Quality in Large U.S. Cities. Journal of Environmental Management, 86:688-698. Stone, B., J. Hess, H. Frumkin. 2010. Urban Form and Extreme Heat Events: Are Sprawling Cities More Vulnerable to Climate Change than Compact Cities? Environmental Health Perspectives, 118(10): 14251428. Sturm, R. and D. Cohen. 2004. Suburban Sprawl and Physical and Mental Health, Public Health 118(7) 488-496. Trowbridge, M. J. and N. C. McDonald. 2008. Urban Sprawl and Miles Driven Daily by Teenagers in the United States. American Journal of Preventive Medicine 34(3): 202-206. Trowbridge, MJ, Gurka, MJ, O’Connor, R. 2009. Urban Sprawl and Delayed Ambulance Arrival in the United States. American Journal of Preventive Medicine. 37(5), 428-432. Zolnik E J, 2011, "The effect of sprawl on private-vehicle commuting outcomes" Environment and Planning A 43(8) 1875 – 1893.

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Appendix A. County Compactness Indices for 2010, 2000, and Changes New compactness index 2010 is the county compactness/sprawl index for 2010, using the six variables that make up the original county sprawl index. New compactness index 2000 is the analogous county sprawl index for 2000 obtained by applying component score coefficient values for 2010 to data for 2000. Change in new compactness index is the change in the index between 2000 and 2010 measured as above. Original compactness index is the county sprawl index for 2000 based on component score coefficient values for 2000. And change in original compactness index is the change in the index between 2000 and 2010 using the indices for each year respectively, based on component score coefficient values for each year. fips

county

1009 1015 1021 1033 1051 1055 1069 1073 1077 1079 1081 1083 1089 1097 1101 1103 1113 1115 1117 1125 1127 4005 4013 4015 4019 4021 4025 4027

Blount County, AL Calhoun County, AL Chilton County, AL Colbert County, AL Elmore County, AL Etowah County, AL Houston County, AL Jefferson County, AL Lauderdale County, AL Lawrence County, AL Lee County, AL Limestone County, AL Madison County, AL Mobile County, AL Montgomery County, AL Morgan County, AL Russell County, AL St. Clair County, AL Shelby County, AL Tuscaloosa County, AL Walker County, AL Coconino County, AZ Maricopa County, AZ Mohave County, AZ Pima County, AZ Pinal County, AZ Yavapai County, AZ Yuma County, AZ

new new change in compactness compactness new index 2010 index 2000 compactness index 76.9 93.9 88.8 5.0 74.2 64.4 9.8 103.6 97.6 5.9 89.9 79.4 10.5 92.9 89.0 3.9 89.0 83.0 6.0 113.9 108.9 5.0 90.5 80.4 10.1 75.7 64.9 10.8 93.6 85.4 8.3 85.2 75.2 9.9 108.9 89.2 19.7 109.9 98.8 11.0 105.3 101.6 3.7 100.0 89.6 10.5 92.9 86.2 6.7 89.7 82.2 7.5 96.1 85.4 10.7 105.0 94.4 10.5 89.2 83.1 6.1 87.3 74.0 13.4 116.5 111.8 4.7 91.3 104.0 103.2 0.9 100.7 79.8 21.0 93.5 83.1 10.4 104.7 92.6 12.1

original compactness index

change in original compactness index

95.0 74.1 103.2 84.6 94.7 90.1 113.1 87.3 72.2 91.1 81.6 95.4 103.4 107.3 95.1 92.8 87.5 91.5 99.9 89.1 88.5 119.4

-1.1 0.1 0.3 5.3 -1.8 -1.1 0.8 3.2 3.4 2.5 3.6 13.5 6.5 -1.9 4.9 0.1 2.1 4.6 5.0 0.1 -1.1 -2.8

106.6 87.5 90.5 101.1

-2.6 13.2 3.1 3.6

91

MEASURING URBAN SPRAWL AND VALIDATING SPRAWL MEASURES

5007 5031 5033 5035 5045 5051 5053 5069 5079 5085 5087 5091 5111 5119 5125 5131 5143 6001 6007 6013 6017 6019 6025 6029 6031 6037 6039 6041 6047 6053 6055 6059 6061 6065 6067 6069 6071 6073 6075 6077 6079 6081

Benton County, AR Craighead County, AR Crawford County, AR Crittenden County, AR Faulkner County, AR Garland County, AR Grant County, AR Jefferson County, AR Lincoln County, AR Lonoke County, AR Madison County, AR Miller County, AR Poinsett County, AR Pulaski County, AR Saline County, AR Sebastian County, AR Washington County, AR Alameda County, CA Butte County, CA Contra Costa County, CA El Dorado County, CA Fresno County, CA Imperial County, CA Kern County, CA Kings County, CA Los Angeles County, CA Madera County, CA Marin County, CA Merced County, CA Monterey County, CA Napa County, CA Orange County, CA Placer County, CA Riverside County, CA Sacramento County, CA San Benito County, CA San Bernardino County, CA San Diego County, CA San Francisco County, CA San Joaquin County, CA San Luis Obispo County, CA San Mateo County, CA

94.0 87.4 84.9 91.6 87.4 94.5 66.8 100.2 72.3 82.4 79.2 98.9 73.6 114.1 85.3 102.8 101.1 153.3 97.5 121.7 89.7 100.0 89.7 96.6 96.8 160.6 82.6 115.9 94.1 110.6 110.7 145.8 102.3 105.8 124.4 101.5

86.9 79.6 81.1 89.7 81.1 91.6 64.3 98.1 70.9 77.6

7.0 7.8 3.8 1.9 6.3 2.9 2.5 2.1 1.4 4.7

92.8 87.1 88.9 100.2 88.0 95.7 73.6 104.4 81.4 85.9

1.2 0.2 -4.0 -8.6 -0.6 -1.2 -6.8 -4.2 -9.1 -3.5

98.8 67.9 108.7 80.1 100.4 90.7 145.3 95.5 118.1 85.0 96.0 88.1 92.4 85.2 155.9 80.3 111.8 89.7 108.7 107.0 140.6 94.6 100.2 118.0 97.0

0.1 5.7 5.4 5.2 2.4 10.5 7.9 2.0 3.6 4.7 4.0 1.6 4.3 11.6 4.7 2.3 4.1 4.4 1.8 3.7 5.1 7.6 5.7 6.5 4.5

106.4 80.8 112.6 86.0 105.1 98.3 152.4 103.7 123.6 90.0 104.2 95.8 100.8 95.6 161.5 88.5 119.2 99.2 120.3 112.3 146.2 100.5 107.3 124.5 107.5

-7.5 -7.3 1.5 -0.7 -2.4 2.8 0.9 -6.2 -1.9 -0.3 -4.3 -6.1 -4.2 1.3 -0.9 -5.9 -3.3 -5.1 -9.7 -1.6 -0.4 1.7 -1.5 -0.1 -5.9

102.4 126.0 247.8 117.3

99.2 122.6 246.1 110.1

3.2 3.4 1.7 7.2

106.7 130.7 257.6 118.3

-4.2 -4.6 -9.8 -1.0

100.6 141.8

94.2 138.6

6.4 3.2

101.8 146.0

-1.2 -4.2

92

MEASURING URBAN SPRAWL AND VALIDATING SPRAWL MEASURES

6083 6085 6087 6089 6095 6097 6099 6101 6107 6111 6113 6115 8001 8005 8013 8014 8019 8031 8035 8039 8041 8059 8069 8077 8101 8119 8123 9001 9003 9007 9009 9011 9013 10001 10003 11001 12001 12003 12005 12009 12011 12015 12019 12021

Santa Barbara County, CA Santa Clara County, CA Santa Cruz County, CA Shasta County, CA Solano County, CA Sonoma County, CA Stanislaus County, CA Sutter County, CA Tulare County, CA Ventura County, CA Yolo County, CA Yuba County, CA Adams County, CO Arapahoe County, CO Boulder County, CO Broomfield County, CO Clear Creek County, CO Denver County, CO Douglas County, CO Elbert County, CO El Paso County, CO Jefferson County, CO Larimer County, CO Mesa County, CO Pueblo County, CO Teller County, CO Weld County, CO Fairfield County, CT Hartford County, CT Middlesex County, CT New Haven County, CT New London County, CT Tolland County, CT Kent County, DE New Castle County, DE District of Columbia, DC Alachua County, FL Baker County, FL Bay County, FL Brevard County, FL Broward County, FL Charlotte County, FL Clay County, FL Collier County, FL

124.8 138.1 113.1 94.1 114.9 104.2 111.0 92.6 99.0 119.6 108.8 93.6 117.7 122.3 114.7 117.9 96.0 144.4 104.1 68.4 117.7 115.0 105.0 104.8 107.6 97.1 97.3 115.0 107.5 93.1 112.2 95.7 84.3 93.0 119.4 193.3 106.6 72.7 102.1 108.4 133.0 100.3 98.7 104.5

117.3 133.5 111.9 82.3 110.9 102.1 108.6 86.0 91.7 112.5 105.8 89.5 127.9 117.3 111.3

7.5 4.6 1.3 11.7 4.0 2.1 2.4 6.6 7.3 7.1 3.0 4.2 -10.2 5.0 3.4

125.7 140.5 118.1 88.6 117.5 106.6 117.4 95.1 103.1 121.6 113.1 96.5 130.3 122.0 113.9

-0.9 -2.4 -5.0 5.4 -2.6 -2.4 -6.5 -2.5 -4.1 -1.9 -4.3 -2.9 -12.6 0.3 0.8

93.4

2.6

97.2

-1.2

95.7

8.4

99.0

5.1

107.4 115.9 100.2 97.5 103.1 91.9 88.9 111.3 104.5 90.0 108.9 93.0 80.9 85.9 117.4

10.3 -0.9 4.9 7.3 4.5 5.2 8.4 3.8 3.0 3.0 3.3 2.7 3.4 7.1 1.9

110.2 117.8 105.2 105.7 110.7 93.6 99.6 115.0 110.1 94.6 113.7 98.9 86.4 92.1 122.3

7.5 -2.9 -0.1 -0.9 -3.1 3.5 -2.3 0.0 -2.5 -1.6 -1.5 -3.2 -2.1 0.9 -2.9

105.7 70.0 103.0 105.1 131.6 96.1 85.2 93.9

0.9 2.7 -0.9 3.3 1.3 4.2 13.5 10.6

109.6 77.4 107.4 109.6 136.5 100.1 92.0 99.2

-2.9 -4.8 -5.3 -1.2 -3.6 0.2 6.7 5.2

93

MEASURING URBAN SPRAWL AND VALIDATING SPRAWL MEASURES

12031 12033 12035 12039 12053 12057 12061 12069 12071 12073 12081 12083 12085 12086 12089 12091 12095 12097 12099 12101 12103 12105 12109 12111 12113 12115 12117 12127 12129 13013 13015 13021 13029 13035 13045 13047 13051 13053 13057 13059 13063 13067 13073

Duval County, FL Escambia County, FL Flagler County, FL Gadsden County, FL Hernando County, FL Hillsborough County, FL Indian River County, FL Lake County, FL Lee County, FL Leon County, FL Manatee County, FL Marion County, FL Martin County, FL Miami-Dade County, FL Nassau County, FL Okaloosa County, FL Orange County, FL Osceola County, FL Palm Beach County, FL Pasco County, FL Pinellas County, FL Polk County, FL St. Johns County, FL St. Lucie County, FL Santa Rosa County, FL Sarasota County, FL Seminole County, FL Volusia County, FL Wakulla County, FL Barrow County, GA Bartow County, GA Bibb County, GA Bryan County, GA Butts County, GA Carroll County, GA Catoosa County, GA Chatham County, GA Chattahoochee County, GA Cherokee County, GA Clarke County, GA Clayton County, GA Cobb County, GA Columbia County, GA

117.4 106.4 99.2 91.0 98.7 119.1 111.1 108.0 107.0 102.8 116.3 93.5 108.1 152.1 92.9 101.3 119.4 106.9 114.2 110.8 133.1 110.0 104.1 110.1 84.6 111.0 116.8 107.4 83.9 81.6 86.6 105.2 80.9 79.9 75.3 88.5 113.3 92.0 94.9 100.6 107.2 111.8 88.1

116.0 106.3

1.4 0.2

120.3 111.0

-2.9 -4.5

86.6 94.1 115.9 103.6 101.6 100.5 97.0 114.2 92.1 100.6 146.4 79.3 102.9 115.8 105.1 112.4 110.1 131.6 104.9 99.9 100.0 77.5 110.2 113.8 105.0 73.8 75.8 79.0 103.6 70.0 76.9 71.9 84.4 113.0

4.4 4.6 3.2 7.5 6.5 6.5 5.8 2.2 1.4 7.4 5.7 13.7 -1.6 3.6 1.7 1.8 0.7 1.5 5.1 4.2 10.1 7.1 0.8 3.0 2.4 10.1 5.9 7.7 1.6 10.9 3.0 3.4 4.1 0.3

91.8 98.6 119.6 107.5 105.1 104.8 102.0 118.0 96.0 105.3

-0.9 0.1 -0.5 3.5 2.9 2.2 0.7 -1.7 -2.5 2.7

84.1 108.5 121.2 109.6 115.8 114.9 134.0 109.2 103.0 104.5 84.0 114.0 117.5 109.2 80.7 81.8 85.2 107.1 77.2 82.9 77.9 89.5 115.8

8.9 -7.2 -1.8 -2.7 -1.6 -4.0 -0.9 0.8 1.1 5.6 0.6 -3.1 -0.7 -1.8 3.2 -0.2 1.5 -1.9 3.7 -2.9 -2.6 -1.0 -2.5

84.0 96.6 101.2 102.8 87.2

11.0 4.1 6.0 9.0 0.9

90.0 100.4 105.1 106.4 91.5

4.9 0.2 2.1 5.4 -3.4

94

MEASURING URBAN SPRAWL AND VALIDATING SPRAWL MEASURES

13077 13083 13085 13089 13095 13097 13103 13113 13115 13117 13121 13127 13135 13139 13143 13145 13151 13153 13169 13171 13177 13179 13185 13189 13195 13199 13207 13213 13215 13217 13219 13221 13223 13227 13231 13245 13247 13255 13273 13295 13297 13313 13321 16001

Coweta County, GA Dade County, GA Dawson County, GA DeKalb County, GA Dougherty County, GA Douglas County, GA Effingham County, GA Fayette County, GA Floyd County, GA Forsyth County, GA Fulton County, GA Glynn County, GA Gwinnett County, GA Hall County, GA Haralson County, GA Harris County, GA Henry County, GA Houston County, GA Jones County, GA Lamar County, GA Lee County, GA Liberty County, GA Lowndes County, GA McDuffie County, GA Madison County, GA Meriwether County, GA Monroe County, GA Murray County, GA Muscogee County, GA Newton County, GA Oconee County, GA Oglethorpe County, GA Paulding County, GA Pickens County, GA Pike County, GA Richmond County, GA Rockdale County, GA Spalding County, GA Terrell County, GA Walker County, GA Walton County, GA Whitfield County, GA Worth County, GA Ada County, ID

84.0 81.5 80.4 112.0 102.7 86.3 83.2 85.2 91.7 84.9 112.3 99.2 104.0 93.3 85.1 78.6 87.2 100.2 73.6 77.0 77.8 97.8 95.4 80.3 74.7 74.1 76.4 77.9 108.0 88.9 79.2 67.7 85.6 76.5 71.5 105.7 94.5 87.7 76.8 83.6 73.0 94.7 73.3 109.3

79.7 75.1 72.5 107.4 95.3 79.5 79.6 74.8 89.5 70.6 107.6 96.7 94.5 87.6 76.4

4.2 6.4 7.9 4.6 7.4 6.7 3.6 10.4 2.2 14.4 4.7 2.5 9.5 5.6 8.7

84.7 81.3 79.9 110.3 99.9 84.4 85.8 79.8 94.3 76.2 111.3 100.0 98.9 91.7 82.5

-0.7 0.2 0.4 1.7 2.7 1.9 -2.7 5.4 -2.6 8.7 1.0 -0.8 5.1 1.6 2.6

72.5 95.9 70.8 69.7 74.0 88.9 92.7 75.7 62.8 69.2 73.3 74.8 108.3 78.7 72.6

14.7 4.3 2.8 7.3 3.8 8.9 2.6 4.7 11.9 4.9 3.0 3.1 -0.3 10.2 6.6

78.5 99.6 77.6 77.0 80.9 93.4 97.1 81.8 73.5 76.3 79.9 80.6 113.0 83.4 78.9

8.7 0.5 -4.0 0.0 -3.1 4.5 -1.7 -1.5 1.2 -2.2 -3.5 -2.7 -5.1 5.5 0.3

80.9 74.7

4.7 1.8

86.4 80.8

-0.8 -4.3

102.2 82.4 85.4 82.2 79.7 67.2 92.5 64.8 103.1

3.5 12.1 2.3 -5.4 3.8 5.7 2.2 8.5 6.2

106.9 86.9 88.9 90.7 85.1 74.0 97.4 73.8 108.0

-1.2 7.6 -1.2 -13.9 -1.6 -1.0 -2.7 -0.4 1.3

95

MEASURING URBAN SPRAWL AND VALIDATING SPRAWL MEASURES

16005 16019 16027 16045 16051 16055 16069 17003 17005 17007 17019 17027 17031 17037 17043 17053 17063 17073 17083 17089 17091 17093 17097 17111 17113 17115 17117 17119 17123 17129 17131 17133 17143 17147 17161 17163 17167 17179 17183 17197 17201 17203 18003 18005

Bannock County, ID Bonneville County, ID Canyon County, ID Gem County, ID Jefferson County, ID Kootenai County, ID Nez Perce County, ID Alexander County, IL Bond County, IL Boone County, IL Champaign County, IL Clinton County, IL Cook County, IL DeKalb County, IL DuPage County, IL Ford County, IL Grundy County, IL Henry County, IL Jersey County, IL Kane County, IL Kankakee County, IL Kendall County, IL Lake County, IL McHenry County, IL McLean County, IL Macon County, IL Macoupin County, IL Madison County, IL Marshall County, IL Menard County, IL Mercer County, IL Monroe County, IL Peoria County, IL Piatt County, IL Rock Island County, IL St. Clair County, IL Sangamon County, IL Tazewell County, IL Vermilion County, IL Will County, IL Winnebago County, IL Woodford County, IL Allen County, IN Bartholomew County, IN

112.3 105.1 104.6 94.7 83.0 101.1 98.3 89.3 83.4 92.4 109.0 83.2 167.6 97.8 118.8 67.3 100.4 79.9 82.2 113.1 92.0 95.5 112.9 100.9 102.8 93.3 93.2 105.5 88.9 78.8 74.9 87.9 104.3 76.4 107.8 104.7 103.1 103.0 98.1 103.9 110.1 88.7 101.3 106.2

100.2 95.0 92.2 72.4 63.6 94.9 101.7

12.1 10.0 12.4 22.3 19.4 6.2 -3.3

111.5 102.6 99.4 81.3 79.0 98.8 107.9

0.8 2.4 5.2 13.4 4.0 2.2 -9.6

77.9 89.7 101.1 82.6 165.2 90.8 117.0 70.1 84.7 79.2

5.6 2.7 7.8 0.6 2.4 7.0 1.8 -2.9 15.8 0.7

86.3 97.5 111.4 91.2 171.3 100.2 121.3 82.3 92.2 88.3

-2.9 -5.1 -2.4 -8.0 -3.6 -2.3 -2.5 -15.0 8.3 -8.4

109.9 90.4 87.8 110.6 99.9 100.8 97.4 88.6 101.2 77.7 78.6 72.2 81.6 104.6 75.8 107.0 103.4 101.3 94.7 85.1 98.7 106.1 77.5 96.4 92.7

3.2 1.6 7.7 2.3 1.0 2.0 -4.1 4.7 4.3 11.1 0.2 2.7 6.2 -0.2 0.6 0.8 1.3 1.8 8.3 13.1 5.2 4.0 11.1 4.8 13.5

115.1 98.7 95.2 114.5 104.7 110.1 104.3 99.1 107.4 87.7 97.4 82.5 90.2 110.0 87.8 111.9 109.0 108.8 100.8 93.8 103.5 111.3 87.3 102.8 99.0

-1.9 -6.7 0.3 -1.6 -3.8 -7.3 -11.0 -5.9 -1.9 1.1 -18.6 -7.7 -2.3 -5.6 -11.4 -4.1 -4.3 -5.8 2.2 4.3 0.4 -1.1 1.4 -1.6 7.2

96

MEASURING URBAN SPRAWL AND VALIDATING SPRAWL MEASURES

18011 18013 18015 18019 18021 18029 18035 18039 18043 18047 18051 18055 18057 18059 18061 18063 18067 18073 18081 18089 18091 18095 18097 18105 18109 18115 18119 18127 18129 18133 18141 18145 18153 18157 18159 18163 18165 18167 18173 18175 18179 18183 19011 19013

Boone County, IN Brown County, IN Carroll County, IN Clark County, IN Clay County, IN Dearborn County, IN Delaware County, IN Elkhart County, IN Floyd County, IN Franklin County, IN Gibson County, IN Greene County, IN Hamilton County, IN Hancock County, IN Harrison County, IN Hendricks County, IN Howard County, IN Jasper County, IN Johnson County, IN Lake County, IN LaPorte County, IN Madison County, IN Marion County, IN Monroe County, IN Morgan County, IN Ohio County, IN Owen County, IN Porter County, IN Posey County, IN Putnam County, IN St. Joseph County, IN Shelby County, IN Sullivan County, IN Tippecanoe County, IN Tipton County, IN Vanderburgh County, IN Vermillion County, IN Vigo County, IN Warrick County, IN Washington County, IN Wells County, IN Whitley County, IN Benton County, IA Black Hawk County, IA

91.8 80.8 81.3 105.1 86.2 94.1 107.3 104.8 105.2 82.2 90.0 78.3 101.1 87.5 76.1 94.2 105.1 66.8 104.9 115.4 92.9 101.5 116.5 105.5 99.4 88.0 90.0 94.4 84.0 75.5 115.2 98.8 74.1 106.1 66.4 109.1 110.8 109.6 92.2 89.6 70.5 68.6 80.1 104.1

73.4 81.0 67.2 102.0 76.4 81.9 98.9 92.2 100.5 78.0 78.2 79.2 93.2 79.1 71.7 83.9 93.2 61.7 96.0 112.5 90.4 99.8 114.6 104.3 86.4 80.1 71.0 93.1 82.3 72.3 106.0 84.7 71.9 104.1 64.5 107.2 102.0 101.3 83.6 77.4 65.3 67.4 66.9 101.6

18.4 -0.2 14.1 3.2 9.8 12.2 8.4 12.5 4.6 4.2 11.9 -1.0 7.9 8.3 4.4 10.4 11.9 5.1 8.9 2.9 2.5 1.7 1.9 1.1 13.1 7.9 19.0 1.4 1.7 3.1 9.2 14.1 2.2 2.1 1.9 1.9 8.8 8.3 8.6 12.2 5.2 1.2 13.3 2.5

84.0 86.1 76.9 107.5 86.8 89.1 105.5 98.7 102.1 87.0 88.2 87.3 98.5 87.3 78.8 90.0 101.1 72.8 101.1 116.9 96.8 105.7 119.2 106.3 93.0 89.0 81.8 98.6 91.6 80.8 112.6 93.7 80.8 110.0 77.1 111.5 109.5 107.3 91.1 87.9 76.1 77.7 78.2 108.1

7.8 -5.3 4.3 -2.3 -0.7 5.0 1.9 6.1 3.0 -4.8 1.8 -9.1 2.6 0.2 -2.8 4.2 3.9 -6.0 3.8 -1.6 -3.9 -4.1 -2.7 -0.8 6.4 -0.9 8.2 -4.2 -7.6 -5.3 2.6 5.1 -6.8 -3.9 -10.6 -2.4 1.3 2.2 1.1 1.7 -5.6 -9.1 1.9 -4.0

97

MEASURING URBAN SPRAWL AND VALIDATING SPRAWL MEASURES

19017 19049 19061 19085 19103 19105 19113 19121 19129 19153 19155 19163 19169 19181 19183 19193 20015 20045 20059 20061 20079 20085 20091 20103 20121 20139 20149 20161 20173 20177 20191 20209 21015 21017 21019 21029 21037 21047 21049 21059 21067 21081 21089

Bremer County, IA Dallas County, IA Dubuque County, IA Harrison County, IA Johnson County, IA Jones County, IA Linn County, IA Madison County, IA Mills County, IA Polk County, IA Pottawattamie County, IA Scott County, IA Story County, IA Warren County, IA Washington County, IA Woodbury County, IA Butler County, KS Douglas County, KS Franklin County, KS Geary County, KS Harvey County, KS Jackson County, KS Johnson County, KS Leavenworth County, KS Miami County, KS Osage County, KS Pottawatomie County, KS Riley County, KS Sedgwick County, KS Shawnee County, KS Sumner County, KS Wyandotte County, KS Boone County, KY Bourbon County, KY Boyd County, KY Bullitt County, KY Campbell County, KY Christian County, KY Clark County, KY Daviess County, KY Fayette County, KY Grant County, KY Greenup County, KY

75.5 87.0 102.0 69.0 97.8 83.9 100.1 77.7 81.6 108.5

72.4 76.2 101.7 65.3 94.0 70.1 99.0 64.5 68.2 105.1

3.1 10.8 0.3 3.6 3.8 13.9 1.2 13.3 13.4 3.3

83.4 87.1 108.2 76.7 102.6 81.6 106.0 76.6 81.0 110.9

-7.9 -0.1 -6.2 -7.8 -4.8 2.4 -5.8 1.2 0.7 -2.4

95.3 115.0 97.0 82.7 72.4 104.8 81.5 99.1 84.9 107.6 70.9 54.6 104.9 92.8 87.8 66.9 85.9 99.5 108.0 102.3 77.9 114.9 95.6 95.7 101.5 95.4 112.6 100.1 97.3 103.9 115.7 87.1 102.0

89.3 103.4 90.8 73.2 70.0 97.0 76.1 93.7 62.6

6.0 11.7 6.2 9.4 2.4 7.8 5.4 5.4 22.3

99.6 109.5 102.2 86.5 82.9 105.4 84.8 100.8 72.3

-4.3 5.5 -5.2 -3.8 -10.5 -0.7 -3.2 -1.7 12.7

69.1 47.4 103.1 91.4 65.7 64.1

1.8 7.2 1.8 1.3 22.1 2.8

79.3 61.5 108.9 100.2 76.1 74.2

-8.4 -7.0 -4.0 -7.4 11.7 -7.3

106.4 99.5 61.6 111.7 92.0 80.3 101.0 86.1 110.7 86.2 91.0 99.3 105.6 78.3 97.3

1.6 2.8 16.3 3.2 3.6 15.4 0.5 9.3 1.9 13.9 6.4 4.7 10.1 8.8 4.7

111.3 105.4 73.3 116.4 96.3 88.7 106.0 90.3 115.5 93.5 97.3 107.7 109.2 84.0 103.3

-3.4 -3.1 4.6 -1.5 -0.7 7.0 -4.4 5.1 -2.9 6.6 0.0 -3.8 6.5 3.1 -1.3

98

MEASURING URBAN SPRAWL AND VALIDATING SPRAWL MEASURES

21093 21101 21103 21111 21113 21117 21123 21163 21179 21185 21209 21211 21215 21227 21239 22005 22015 22017 22019 22031 22033 22043 22047 22051 22055 22057 22063 22071 22073 22075 22077 22079 22087 22089 22095 22099 22103 22109 22111 22121 23001

Hardin County, KY Henderson County, KY Henry County, KY Jefferson County, KY Jessamine County, KY Kenton County, KY Larue County, KY Meade County, KY Nelson County, KY Oldham County, KY Scott County, KY Shelby County, KY Spencer County, KY Warren County, KY Woodford County, KY Ascension Parish, LA Bossier Parish, LA Caddo Parish, LA Calcasieu Parish, LA De Soto Parish, LA East Baton Rouge Parish, LA Grant Parish, LA Iberville Parish, LA Jefferson Parish, LA Lafayette Parish, LA Lafourche Parish, LA Livingston Parish, LA Orleans Parish, LA Ouachita Parish, LA Plaquemines Parish, LA Pointe Coupee Parish, LA Rapides Parish, LA St. Bernard Parish, LA St. Charles Parish, LA St. John the Baptist Parish, LA St. Martin Parish, LA St. Tammany Parish, LA Terrebonne Parish, LA Union Parish, LA West Baton Rouge Parish, LA Androscoggin County,

95.9 93.9 89.9 118.4 94.9 118.4 76.9 88.9 90.0 90.9 99.4 94.7 86.5 106.1 91.2 91.4 94.9 105.5 95.4 81.2

85.7 87.1 75.0 112.4 87.0 111.0

10.3 6.8 14.9 5.9 7.9 7.4

92.4 94.9 87.5 115.9 91.3 114.2

3.6 -1.1 2.4 2.4 3.6 4.3

78.5 78.4 85.3 88.9 85.4

10.4 11.6 5.6 10.5 9.3

83.7 84.5 87.9 96.3 92.1

5.2 5.5 3.0 3.1 2.6

94.7 82.5 85.7 93.7 102.6 94.0 78.1

11.4 8.7 5.6 1.2 2.9 1.3 3.1

101.3 89.0 91.2 100.0 107.9 101.5 85.2

4.8 2.2 0.2 -5.1 -2.4 -6.2 -4.0

110.9 76.4 94.1 130.5 105.3 95.7 87.5 144.7 102.9 92.2 83.2 95.0 112.5 100.6

106.1

4.8

111.0

-0.1

95.8 128.0 101.3 92.0 80.6 160.3 99.8 86.4 79.0 96.4 116.5 88.8

-1.7 2.5 4.1 3.7 6.9 -15.6 3.1 5.9 4.2 -1.4 -4.0 11.9

101.8 132.7 105.9 98.7 87.1 165.7 104.8 92.6 86.4 103.4 119.9 95.8

-7.7 -2.2 -0.6 -3.0 0.4 -21.0 -1.8 -0.3 -3.2 -8.4 -7.4 4.8

105.3 88.9 103.8 102.1 75.6

96.7 81.2 96.3 101.8 67.1

8.5 7.7 7.6 0.3 8.5

103.0 87.8 100.5 106.8 76.8

2.3 1.2 3.4 -4.7 -1.2

95.6 96.0

90.5 90.7

5.1 5.3

95.3 95.9

0.3 0.1

99

MEASURING URBAN SPRAWL AND VALIDATING SPRAWL MEASURES

23005 23019 23023 23031 24001 24003 24005 24009 24013 24015 24017 24021 24025 24027 24031 24033 24035 24039 24043 24045 24510 25001 25003 25005 25009 25011 25013 25015 25017 25021 25023 25025 25027 26015 26017 26021 26025 26027 26037 26045

ME Cumberland County, ME Penobscot County, ME Sagadahoc County, ME York County, ME Allegany County, MD Anne Arundel County, MD Baltimore County, MD Calvert County, MD Carroll County, MD Cecil County, MD Charles County, MD Frederick County, MD Harford County, MD Howard County, MD Montgomery County, MD Prince George's County, MD Queen Anne's County, MD Somerset County, MD Washington County, MD Wicomico County, MD Baltimore city, MD Barnstable County, MA Berkshire County, MA Bristol County, MA Essex County, MA Franklin County, MA Hampden County, MA Hampshire County, MA Middlesex County, MA Norfolk County, MA Plymouth County, MA Suffolk County, MA Worcester County, MA Barry County, MI Bay County, MI Berrien County, MI Calhoun County, MI Cass County, MI Clinton County, MI Eaton County, MI

100.3 84.9 90.5 88.4 106.7

95.4 80.1 82.8 84.9 98.9

4.9 4.8 7.7 3.5 7.7

100.5 89.1 88.8 91.3 103.1

-0.1 -4.2 1.7 -2.9 3.6

115.6 121.6 104.4 97.1 99.2 108.6 103.1 105.8 113.7 122.7

109.3 109.3 90.1 81.5 85.6 88.4 85.5 91.6 94.7 116.8

6.3 12.3 14.2 15.6 13.6 20.2 17.6 14.2 19.0 5.9

113.3 113.4 95.3 86.4 91.1 94.6 92.2 97.6 98.9 120.8

2.3 8.2 9.1 10.6 8.0 14.0 10.9 8.3 14.8 2.0

125.4

114.7

10.7

120.5

4.9

83.7 99.4 99.1 106.7 179.6 102.2 97.6 121.5 128.3 91.5 116.5 101.9 132.5 121.6 107.3 217.1 107.4 79.8 96.5 95.7 92.6 79.5 75.3 82.5

74.9 83.7 92.7 92.2 182.0 96.6 86.9 117.5 123.4 83.5 110.4 87.1 127.7 116.8 101.6 206.5 100.4 68.9 92.5 90.4 87.7 72.6 61.7 74.6

8.8 15.7 6.4 14.5 -2.4 5.6 10.7 4.0 4.9 8.0 6.1 14.7 4.8 4.8 5.6 10.6 7.0 10.8 4.0 5.3 4.9 6.8 13.6 7.9

81.4 89.8 98.7 98.0 187.6 100.0 93.6 121.6 127.7 90.0 114.5 92.6 132.3 121.9 105.8 212.7 105.8 76.5 100.2 96.9 94.9 81.3 72.0 82.2

2.3 9.6 0.4 8.8 -8.1 2.2 4.0 -0.1 0.6 1.5 2.0 9.3 0.2 -0.3 1.4 4.4 1.6 3.3 -3.7 -1.2 -2.3 -1.8 3.3 0.3

100

MEASURING URBAN SPRAWL AND VALIDATING SPRAWL MEASURES

26049 26065 26067 26075 26077 26081 26087 26093 26099 26115 26121 26123 26125 26139 26145 26147 26159 26161 26163 27003 27009 27013 27017 27019 27025 27027 27037 27039 27053 27055 27059 27103 27109 27119 27123 27137 27139 27141 27145 27157 27163 27171 28029 28033

Genesee County, MI Ingham County, MI Ionia County, MI Jackson County, MI Kalamazoo County, MI Kent County, MI Lapeer County, MI Livingston County, MI Macomb County, MI Monroe County, MI Muskegon County, MI Newaygo County, MI Oakland County, MI Ottawa County, MI Saginaw County, MI St. Clair County, MI Van Buren County, MI Washtenaw County, MI Wayne County, MI Anoka County, MN Benton County, MN Blue Earth County, MN Carlton County, MN Carver County, MN Chisago County, MN Clay County, MN Dakota County, MN Dodge County, MN Hennepin County, MN Houston County, MN Isanti County, MN Nicollet County, MN Olmsted County, MN Polk County, MN Ramsey County, MN St. Louis County, MN Scott County, MN Sherburne County, MN Stearns County, MN Wabasha County, MN Washington County, MN Wright County, MN Copiah County, MS DeSoto County, MS

101.4 110.4 82.2 91.7 96.4 101.6 76.4 88.5 112.6 83.9 100.7 80.2 108.3 92.2 96.9 89.6 77.1 102.8 126.3 106.7 92.3 90.0 83.3 98.4 84.0 84.2 106.2 80.3 123.7 88.3 84.9 93.8 100.1 61.1 128.9 93.1 91.2 84.3 94.2 93.8 109.1 81.6 80.6 89.1

98.7 103.5 73.0 84.2 94.0 97.7 68.8 80.9 108.1 80.6 98.3 65.3 106.2 85.0 93.6 87.1 72.3 100.2 126.6 96.9 93.3

2.7 6.9 9.2 7.4 2.5 3.9 7.6 7.6 4.5 3.2 2.4 14.9 2.1 7.2 3.2 2.6 4.7 2.6 -0.3 9.8 -1.0

103.7 110.3 82.3 90.6 100.3 103.4 76.1 86.8 113.6 87.9 104.1 74.6 111.2 92.3 101.5 92.9 80.1 106.1 132.0 100.5 102.3

-2.3 0.2 -0.2 1.1 -3.8 -1.7 0.3 1.7 -1.0 -4.1 -3.4 5.6 -2.8 -0.1 -4.7 -3.3 -3.1 -3.3 -5.7 6.2 -10.0

75.8 84.3 77.0 79.8 98.5 61.6 124.0 82.9 65.4

7.5 14.1 7.0 4.5 7.7 18.7 -0.3 5.4 19.5

83.4 90.0 83.6 93.2 103.6 73.2 128.0 91.5 75.2

-0.1 8.4 0.5 -9.0 2.7 7.1 -4.3 -3.3 9.6

90.1

10.0

97.2

2.9

126.2 92.6 89.2 76.9 85.9 84.4 97.6 77.8 64.5 80.2

2.7 0.5 2.0 7.4 8.3 9.4 11.5 3.8 16.1 8.9

131.6 99.3 94.8 83.1 94.2 93.5 101.5 84.1 76.6 86.3

-2.7 -6.2 -3.6 1.2 0.0 0.3 7.5 -2.5 4.1 2.7

101

MEASURING URBAN SPRAWL AND VALIDATING SPRAWL MEASURES

28035 28039 28045 28047 28049 28059 28073 28089 28093 28121 28127 28131 28137 28143 29003 29013 29019 29021 29027 29031 29037 29043 29047 29049 29051 29055 29071 29077 29095 29097 29099 29107 29113 29135 29145 29165 29177 29183 29189 29219 29221 29225

Forrest County, MS George County, MS Hancock County, MS Harrison County, MS Hinds County, MS Jackson County, MS Lamar County, MS Madison County, MS Marshall County, MS Rankin County, MS Simpson County, MS Stone County, MS Tate County, MS Tunica County, MS Andrew County, MO Bates County, MO Boone County, MO Buchanan County, MO Callaway County, MO Cape Girardeau County, MO Cass County, MO Christian County, MO Clay County, MO Clinton County, MO Cole County, MO Crawford County, MO (pt.)* Franklin County, MO Greene County, MO Jackson County, MO Jasper County, MO Jefferson County, MO Lafayette County, MO Lincoln County, MO Moniteau County, MO Newton County, MO Platte County, MO Ray County, MO St. Charles County, MO St. Louis County, MO Warren County, MO Washington County, MO Webster County, MO

99.0 82.9 93.6 106.1 103.4 100.5 80.2 92.8 84.7 87.6 83.3 84.6 87.1 74.7 77.4 84.6 103.6 115.7 84.9

91.1 63.7 84.9 98.4 96.7 92.2 66.5 76.7 71.3 79.2 69.5 64.9 64.1 65.5 61.4 72.1 93.5 107.2 71.6

7.9 19.2 8.7 7.7 6.7 8.3 13.7 16.1 13.3 8.4 13.8 19.8 23.0 9.2 16.0 12.5 10.1 8.5 13.4

97.5 73.5 90.2 102.8 102.7 97.2 74.1 85.4 78.7 86.3 78.5 73.4 73.6 74.9 72.8 83.9 99.4 112.8 79.8

1.4 9.4 3.4 3.3 0.7 3.3 6.1 7.4 6.0 1.3 4.8 11.2 13.5 -0.1 4.6 0.8 4.2 2.8 5.1

98.0 87.3 92.0 100.2 88.7 89.4

80.5 79.9 97.3 79.0 88.3

6.9 12.1 2.9 9.7 1.1

88.5 87.2 103.0 86.5 95.4

-1.2 4.8 -2.9 2.2 -5.9

82.6 91.8 108.1 115.1 96.2 101.6 87.9 92.4 75.4 90.5 97.4 69.4 116.8 116.9 89.8 83.3 91.3

82.2 96.1 113.4 95.8 94.5 80.8 78.7 70.8 76.9 88.6 70.5 108.6 121.0 77.8 76.7 69.4

9.6 12.0 1.7 0.4 7.1 7.1 13.7 4.6 13.6 8.8 -1.1 8.2 -4.1 12.0 6.6 21.9

87.9 101.6 119.7 101.3 98.2 89.5 86.3 80.4 87.7 94.7 79.2 113.7 124.2 83.8 83.8 79.9

3.9 6.5 -4.6 -5.1 3.4 -1.6 6.0 -5.0 2.7 2.6 -9.8 3.1 -7.3 6.0 -0.5 11.4

102

MEASURING URBAN SPRAWL AND VALIDATING SPRAWL MEASURES

29510 30009 30013 30063 30111 31025 31043 31055 31109 31153 31155 31159 31177 32003 32031 32510 33011 33015 33017 34001 34003 34005 34007 34009 34011 34013 34015 34017 34019 34021 34023 34025 34027 34029 34031 34033 34035 34037 34039 34041 35001 35013 35043 35045

St. Louis city, MO Carbon County, MT Cascade County, MT Missoula County, MT Yellowstone County, MT Cass County, NE Dakota County, NE Douglas County, NE Lancaster County, NE Sarpy County, NE Saunders County, NE Seward County, NE Washington County, NE Clark County, NV Washoe County, NV Carson City, NV Hillsborough County, NH Rockingham County, NH Strafford County, NH Atlantic County, NJ Bergen County, NJ Burlington County, NJ Camden County, NJ Cape May County, NJ Cumberland County, NJ Essex County, NJ Gloucester County, NJ Hudson County, NJ Hunterdon County, NJ Mercer County, NJ Middlesex County, NJ Monmouth County, NJ Morris County, NJ Ocean County, NJ Passaic County, NJ Salem County, NJ Somerset County, NJ Sussex County, NJ Union County, NJ Warren County, NJ Bernalillo County, NM Dona Ana County, NM Sandoval County, NM San Juan County, NM

145.7 77.9 101.9 101.7 103.5 84.7 104.9 122.1 112.1 113.6 75.2 73.2 86.8 123.5 106.6 112.6 103.8 90.4 93.6 112.9 140.1 104.1 130.0 115.6 101.2 168.9 105.6 228.8 87.0 123.8 130.4 117.5 107.2 119.0 158.1 94.0 106.1 95.0 151.0 98.6 120.2 102.1 91.5 84.0

72.9 99.6 97.5 99.8 70.4 97.8 120.9 106.9 101.4 61.5 62.6 71.7 117.8 103.0 111.2 102.3 87.0 87.7 112.2 138.8 101.4 129.2 114.1 93.1 170.0 102.5 225.1 80.6 121.4 127.1 113.5 103.3 114.1 154.0 88.1 98.1 92.2 146.8 97.0 116.3 95.3 86.7 76.8

5.0 2.4 4.3 3.6 14.3 7.1 1.2 5.2 12.2 13.7 10.6 15.2 5.7 3.6 1.3 1.5 3.3 5.9 0.7 1.3 2.7 0.9 1.6 8.1 -1.1 3.1 3.7 6.4 2.4 3.3 4.0 3.9 4.9 4.1 5.9 8.0 2.8 4.2 1.7 3.9 6.8 4.8 7.2

86.9 106.0 102.6 105.4 81.2 105.1 124.5 112.8 107.0 72.4 112.0 81.6 123.4 113.4 120.0 106.8 92.3 93.5 116.7 142.1 106.6 132.2 116.4 99.0 175.4 106.2 230.1 85.3 124.9 130.3 116.6 106.0 117.6 157.1 94.1 102.0 95.9 150.7 100.7 117.7 100.3 95.6 84.5

-9.0 -4.0 -0.9 -1.9 3.5 -0.2 -2.4 -0.7 6.6 2.9 -38.8 5.2 0.1 -6.8 -7.5 -3.0 -2.0 0.1 -3.8 -2.0 -2.5 -2.2 -0.8 2.2 -6.5 -0.5 -1.3 1.7 -1.1 0.1 0.9 1.2 1.4 1.0 -0.1 4.1 -0.8 0.3 -2.1 2.5 1.8 -4.1 -0.5

103

MEASURING URBAN SPRAWL AND VALIDATING SPRAWL MEASURES

35049 35061 36001 36005 36007 36015 36027 36029 36043 36047 36051 36053 36055 36059 36061 36063 36065 36067 36069 36071 36073 36075 36079 36081 36083 36085 36087 36091 36093 36095 36103 36107 36109 36111 36113 36115 36117 36119 37001 37003 37007 37019 37021 37023

Santa Fe County, NM Valencia County, NM Albany County, NY Bronx County, NY Broome County, NY Chemung County, NY Dutchess County, NY Erie County, NY Herkimer County, NY Kings County, NY Livingston County, NY Madison County, NY Monroe County, NY Nassau County, NY New York County, NY Niagara County, NY Oneida County, NY Onondaga County, NY Ontario County, NY Orange County, NY Orleans County, NY Oswego County, NY Putnam County, NY Queens County, NY Rensselaer County, NY Richmond County, NY Rockland County, NY Saratoga County, NY Schenectady County, NY Schoharie County, NY Suffolk County, NY Tioga County, NY Tompkins County, NY Ulster County, NY Warren County, NY Washington County, NY Wayne County, NY Westchester County, NY Alamance County, NC Alexander County, NC Anson County, NC Brunswick County, NC Buncombe County, NC Burke County, NC

97.6 86.8 111.2 331.5 102.0 99.9 94.0 107.6 88.5 341.4 73.9 77.8 107.5 144.2 463.9 100.3 96.8 106.9 80.7 101.7 73.7 84.8 95.1 272.1 99.6 190.1 123.6 91.0 111.8 75.9 113.7 83.3 92.6 92.5 94.2 78.0 74.8 140.2 97.5 73.6 67.9 84.8 97.8 82.8

93.7 82.8 106.5 328.8 98.4 97.0 87.2 108.8 85.4 341.3 71.8 73.6 105.5 143.4 459.5 99.1 94.2 103.3 77.0 98.1 76.1 82.6 93.2 269.1 99.5 188.2 113.3 87.2 109.9 77.2 111.2 80.2 90.5 88.8 92.1 75.1 72.0 138.0 88.9 72.3 64.0 79.7 93.6 82.8

3.9 3.9 4.8 2.7 3.6 2.8 6.8 -1.1 3.2 0.1 2.1 4.2 2.0 0.9 4.4 1.2 2.7 3.6 3.7 3.6 -2.5 2.2 1.8 2.9 0.1 1.9 10.3 3.8 1.9 -1.3 2.6 3.1 2.1 3.8 2.1 2.9 2.8 2.2 8.6 1.3 4.0 5.1 4.2 0.1

97.9 86.9 111.8 322.7 103.6 103.2 92.2 114.9 92.5 341.5 82.2 80.4 110.2 149.4 478.8 104.5 98.5 107.8 84.8 104.2 85.1 89.0 96.6 272.6 104.9 188.4 117.3 93.8 115.4 85.5 115.5 86.6 96.7 94.0 97.5 83.7 79.6 141.6 94.0 78.8 72.7 84.9 98.4 87.4

-0.4 -0.1 -0.6 8.8 -1.6 -3.3 1.8 -7.2 -4.0 0.0 -8.3 -2.7 -2.7 -5.2 -14.9 -4.1 -1.6 -0.9 -4.0 -2.5 -11.4 -4.2 -1.6 -0.5 -5.4 1.7 6.3 -2.8 -3.6 -9.6 -1.8 -3.3 -4.1 -1.4 -3.2 -5.7 -4.8 -1.4 3.6 -5.1 -4.8 -0.1 -0.6 -4.6

104

MEASURING URBAN SPRAWL AND VALIDATING SPRAWL MEASURES

37025 37027 37035 37037 37051 37053 37059 37063 37065 37067 37069 37071 37079 37081 37087 37089 37093 37101 37115 37119 37127 37129 37133 37135 37141 37145 37147 37151 37157 37169 37179 37183 37191 37197 38015 38017 38035 38059 39003 39013 39015 39017 39019 39023

Cabarrus County, NC Caldwell County, NC Catawba County, NC Chatham County, NC Cumberland County, NC Currituck County, NC Davie County, NC Durham County, NC Edgecombe County, NC Forsyth County, NC Franklin County, NC Gaston County, NC Greene County, NC Guilford County, NC Haywood County, NC Henderson County, NC Hoke County, NC Johnston County, NC Madison County, NC Mecklenburg County, NC Nash County, NC New Hanover County, NC Onslow County, NC Orange County, NC Pender County, NC Person County, NC Pitt County, NC Randolph County, NC Rockingham County, NC Stokes County, NC Union County, NC Wake County, NC Wayne County, NC Yadkin County, NC Burleigh County, ND Cass County, ND Grand Forks County, ND Morton County, ND Allen County, OH Belmont County, OH Brown County, OH Butler County, OH Carroll County, OH Clark County, OH

94.2 86.8 92.1 75.2 99.7 87.0 76.1 105.5 85.2 98.7 78.3 96.3 63.3 101.6 95.4 93.7 78.3 77.7 93.1 107.0 83.2 113.3 90.2 91.5 75.5 74.8 95.1 75.4 82.7 78.4 91.8 103.6 88.4 69.7 92.6 95.6 92.3 85.0 104.8 103.6 82.0 103.9 76.0 100.8

88.5 86.1 90.0 73.1 96.8

5.7 0.7 2.1 2.1 2.9

93.6 90.8 93.9 79.6 102.3

0.6 -4.0 -1.9 -4.4 -2.7

68.0 99.5 85.8 95.9 73.5 92.6 60.3 96.9 95.5 92.6 76.2 78.1 92.4 97.4 81.1 106.0 91.2 85.8 71.3 73.3 93.9 75.5 81.6 67.7 73.8 96.6 86.7 65.1 85.1 76.8 78.0 80.3 89.5 93.8 75.7 101.2 75.0 95.0

8.1 6.0 -0.6 2.8 4.8 3.7 3.0 4.6 -0.1 1.1 2.1 -0.4 0.8 9.6 2.1 7.3 -1.0 5.6 4.3 1.5 1.2 0.0 1.1 10.7 17.9 7.0 1.7 4.6 7.5 18.8 14.3 4.7 15.3 9.8 6.3 2.8 1.0 5.8

75.4 104.0 91.7 101.2 80.9 97.2 70.2 101.8 99.5 96.5 82.8 84.0 99.6 101.7 87.8 108.7 96.4 90.7 79.5 80.6 98.7 81.5 87.4 75.9 80.4 100.3 92.9 73.9 93.5 89.4 90.8 89.4 96.4 100.7 83.5 108.1 83.8 100.9

0.7 1.5 -6.5 -2.5 -2.6 -0.9 -6.9 -0.2 -4.1 -2.9 -4.5 -6.3 -6.4 5.3 -4.5 4.6 -6.2 0.8 -4.0 -5.8 -3.6 -6.1 -4.7 2.5 11.4 3.3 -4.5 -4.1 -0.9 6.2 1.6 -4.4 8.5 2.9 -1.5 -4.1 -7.9 -0.2

105

MEASURING URBAN SPRAWL AND VALIDATING SPRAWL MEASURES

39025 39035 39041 39043 39045 39049 39051 39055 39057 39061 39081 39085 39087 39089 39093 39095 39097 39099 39103 39109 39113 39117 39123 39129 39133 39135 39139 39151 39153 39155 39159 39165 39167 39173 40017 40027 40031 40037 40051 40079 40083 40087 40109 40111

Clermont County, OH Cuyahoga County, OH Delaware County, OH Erie County, OH Fairfield County, OH Franklin County, OH Fulton County, OH Geauga County, OH Greene County, OH Hamilton County, OH Jefferson County, OH Lake County, OH Lawrence County, OH Licking County, OH Lorain County, OH Lucas County, OH Madison County, OH Mahoning County, OH Medina County, OH Miami County, OH Montgomery County, OH Morrow County, OH Ottawa County, OH Pickaway County, OH Portage County, OH Preble County, OH Richland County, OH Stark County, OH Summit County, OH Trumbull County, OH Union County, OH Warren County, OH Washington County, OH Wood County, OH Canadian County, OK Cleveland County, OK Comanche County, OK Creek County, OK Grady County, OK Le Flore County, OK Logan County, OK McClain County, OK Oklahoma County, OK Okmulgee County, OK

95.6 114.5 95.7 101.1 93.0 123.2 88.4 70.0 97.7 116.5 104.0 100.4 99.3 102.4 98.2 113.3 86.3 101.7 78.3 91.1 111.3 63.4 92.3 86.9 99.7 91.9 99.5 108.5 108.4 95.8 92.6 96.9 90.2 88.5 94.8 106.3 104.0 94.3 87.9 88.9 84.6 83.1 111.9 93.3

86.9 119.6 79.9 94.0 84.7 119.5 62.7 60.1 89.2 115.3 99.7 96.2 96.1 83.3 93.2 112.8 79.9 97.2 74.7 84.9 109.1 61.3 88.4 82.0 82.1 72.4 84.5 106.5 107.2 92.4 76.1 89.2 83.8 81.4 77.2 94.3 94.2 88.6 78.4 81.8 76.9 76.2 107.2 86.1

8.7 -5.1 15.8 7.0 8.4 3.8 25.7 10.0 8.5 1.1 4.3 4.2 3.2 19.2 4.9 0.6 6.4 4.6 3.6 6.2 2.1 2.1 3.9 4.9 17.6 19.5 15.0 2.0 1.3 3.4 16.6 7.8 6.5 7.1 17.7 12.0 9.8 5.7 9.6 7.1 7.7 6.9 4.6 7.2

91.6 125.5 86.3 99.4 90.2 123.5 71.8 67.6 95.8 119.1 105.9 101.8 101.6 89.0 99.7 117.2 87.7 103.2 81.1 91.5 113.9 71.2 93.7 89.6 88.7 81.1 90.7 111.3 111.8 98.1 84.5 94.2 91.0 89.2 86.9 100.1 102.5 95.4 89.1 90.0 85.5 84.5 111.4 92.8

4.0 -11.0 9.4 1.7 2.8 -0.3 16.6 2.4 1.9 -2.6 -1.9 -1.4 -2.3 13.5 -1.6 -3.8 -1.4 -1.5 -2.8 -0.5 -2.6 -7.8 -1.5 -2.7 11.0 10.8 8.8 -2.8 -3.4 -2.3 8.1 2.7 -0.8 -0.7 7.9 6.2 1.5 -1.1 -1.2 -1.1 -0.9 -1.4 0.5 0.4

106

MEASURING URBAN SPRAWL AND VALIDATING SPRAWL MEASURES

40113 40117 40131 40135 40143 40145 41003 41005 41009 41017 41029 41039 41047 41051 41053 41067 41071 42003 42005 42007 42011 42013 42017 42019 42021 42025 42027 42029 42041 42043 42045 42049 42051 42069 42071 42075 42077 42079 42081 42085 42091 42095 42099 42101

Osage County, OK Pawnee County, OK Rogers County, OK Sequoyah County, OK Tulsa County, OK Wagoner County, OK Benton County, OR Clackamas County, OR Columbia County, OR Deschutes County, OR Jackson County, OR Lane County, OR Marion County, OR Multnomah County, OR Polk County, OR Washington County, OR Yamhill County, OR Allegheny County, PA Armstrong County, PA Beaver County, PA Berks County, PA Blair County, PA Bucks County, PA Butler County, PA Cambria County, PA Carbon County, PA Centre County, PA Chester County, PA Cumberland County, PA Dauphin County, PA Delaware County, PA Erie County, PA Fayette County, PA Lackawanna County, PA Lancaster County, PA Lebanon County, PA Lehigh County, PA Luzerne County, PA Lycoming County, PA Mercer County, PA Montgomery County, PA Northampton County, PA Perry County, PA Philadelphia County, PA

97.1 87.8 93.7 92.0 110.5 95.3 99.2 103.6 90.1 86.7 96.8 104.1 106.1 138.5 91.5 115.9 97.1 125.3 88.9 103.0 116.5 111.2 105.9 87.0 105.7 95.2 109.1 96.7 109.3 116.1 132.5 103.0 101.2 114.8 103.5 105.8 127.5 108.5 105.0 91.0 112.1 115.9 83.2 216.8

95.9 79.4 84.9 79.7 109.6 86.4 97.6 98.1 86.8 77.1 90.1 99.2 99.9 134.4 87.0 109.1 94.5 124.4 85.8 105.4 108.3 110.0 100.8 85.2 105.3 93.3 101.4 90.1 97.0 115.2 131.9 99.8 96.4 113.9 94.8 101.6 124.0 107.6 96.0 87.1 108.4 112.0 80.3 217.1

1.2 8.4 8.9 12.4 0.9 8.9 1.6 5.4 3.3 9.5 6.7 4.9 6.2 4.1 4.5 6.7 2.7 0.8 3.1 -2.4 8.2 1.1 5.1 1.8 0.4 1.9 7.7 6.5 12.3 0.9 0.6 3.2 4.8 0.9 8.7 4.1 3.5 0.9 9.0 3.9 3.6 3.8 2.9 -0.4

104.3 88.8 91.0 88.1 113.6 93.2 105.7 103.8 95.8 85.5 96.2 106.0 105.8 140.8 95.4 114.7 104.0 128.1 93.0 109.7 113.4 115.4 105.3 91.0 109.4 97.6 108.2 94.4 102.4 120.3 135.0 104.7 103.4 116.0 100.4 106.8 126.9 112.1 102.7 93.7 113.4 116.4 86.9 225.0

-7.1 -1.0 2.7 3.9 -3.1 2.1 -6.5 -0.3 -5.7 1.2 0.5 -1.9 0.2 -2.3 -3.9 1.1 -6.9 -2.9 -4.1 -6.8 3.1 -4.2 0.6 -4.0 -3.7 -2.4 1.0 2.3 6.9 -4.2 -2.6 -1.6 -2.2 -1.2 3.1 -1.0 0.5 -3.5 2.3 -2.7 -1.3 -0.5 -3.7 -8.2

107

MEASURING URBAN SPRAWL AND VALIDATING SPRAWL MEASURES

42103 Pike County, PA 42125 Washington County, PA Westmoreland County, 42129 PA 42131 Wyoming County, PA 42133 York County, PA 44001 Bristol County, RI 44003 Kent County, RI 44005 Newport County, RI 44007 Providence County, RI 44009 Washington County, RI 45003 Aiken County, SC 45007 Anderson County, SC 45015 Berkeley County, SC 45019 Charleston County, SC 45031 Darlington County, SC 45035 Dorchester County, SC 45037 Edgefield County, SC 45039 Fairfield County, SC 45041 Florence County, SC 45045 Greenville County, SC 45051 Horry County, SC 45055 Kershaw County, SC 45059 Laurens County, SC 45063 Lexington County, SC 45077 Pickens County, SC 45079 Richland County, SC 45083 Spartanburg County, SC 45085 Sumter County, SC 45091 York County, SC 46083 Lincoln County, SD 46093 Meade County, SD 46099 Minnehaha County, SD 46103 Pennington County, SD 47001 Anderson County, TN 47009 Blount County, TN 47011 Bradley County, TN 47019 Carter County, TN 47021 Cheatham County, TN 47023 Chester County, TN 47037 Davidson County, TN 47043 Dickson County, TN 47047 Fayette County, TN 47057 Grainger County, TN

91.2 98.1

84.2 99.3

7.0 -1.2

88.0 105.8

3.1 -7.8

103.9 84.2 101.2 124.8 117.9 111.1 137.5 98.2 92.3 86.4 93.9 113.6 81.6 98.1 75.5 78.2 88.6 97.2 96.5 72.8 82.7 89.9 86.9 107.2 91.8 90.3 88.9 81.5 89.1 102.9 95.5 92.0 94.4 94.4 97.3 80.6 65.4 112.1 83.4 70.7 80.5

100.4 74.7 95.0 121.7 116.9 108.1 136.5 91.4 85.3 80.4 88.9 111.5 82.9 87.0

3.5 9.5 6.2 3.1 0.9 3.0 1.0 6.8 7.0 6.0 5.1 2.1 -1.4 11.2

104.9 83.5 100.3 125.3 121.7 111.0 141.5 96.8 90.3 85.8 95.0 115.3 88.3 92.8

-1.0 0.7 0.9 -0.5 -3.9 0.0 -4.0 1.4 2.0 0.6 -1.0 -1.6 -6.7 5.3

77.6 84.5 93.9 92.1 73.1 77.2 85.2 82.9 102.0 85.8 84.9 83.0

0.6 4.1 3.2 4.5 -0.3 5.4 4.7 4.0 5.2 6.0 5.4 5.9

84.8 91.0 98.9 97.8 79.0 84.3 90.6 87.8 106.6 90.7 90.0 88.1

-6.6 -2.4 -1.8 -1.3 -6.2 -1.6 -0.7 -0.9 0.6 1.1 0.3 0.8

85.9 100.0 94.1 88.8 89.3 90.3 97.3 74.5 58.6 102.6 78.2

3.2 2.9 1.4 3.3 5.1 4.1 0.0 6.1 6.7 9.5 5.2

91.3 106.6 99.5 94.1 93.4 95.9 102.2 79.0 68.4 106.1 85.4

-2.2 -3.7 -3.9 -2.1 1.1 -1.5 -4.8 1.5 -3.1 5.9 -2.0

76.3

4.2

84.5

-4.0

108

MEASURING URBAN SPRAWL AND VALIDATING SPRAWL MEASURES

47063 47065 47073 47089 47093 47105 47111 47113 47115 47125 47147 47149 47153 47157 47159 47163 47165 47167 47169 47171 47173 47179 47187 47189 48007 48013 48015 48019 48021 48027 48029 48037 48039 48041 48051 48055 48057 48061 48071 48077 48085 48091 48099 48113

Hamblen County, TN Hamilton County, TN Hawkins County, TN Jefferson County, TN Knox County, TN Loudon County, TN Macon County, TN Madison County, TN Marion County, TN Montgomery County, TN Robertson County, TN Rutherford County, TN Sequatchie County, TN Shelby County, TN Smith County, TN Sullivan County, TN Sumner County, TN Tipton County, TN Trousdale County, TN Unicoi County, TN Union County, TN Washington County, TN Williamson County, TN Wilson County, TN Aransas County, TX Atascosa County, TX Austin County, TX Bandera County, TX Bastrop County, TX Bell County, TX Bexar County, TX Bowie County, TX Brazoria County, TX Brazos County, TX Burleson County, TX Caldwell County, TX Calhoun County, TX Cameron County, TX Chambers County, TX Clay County, TX Collin County, TX Comal County, TX Coryell County, TX Dallas County, TX

98.4 103.8 86.0 86.6 100.8 93.3 68.3 91.0 84.8 89.3 77.7 93.6 72.3 111.6 82.9 97.8 91.3 79.8 76.4 106.3 82.7 96.5 94.7 83.8 101.2 87.2 82.8 85.6 88.3 106.8 117.6 92.9 99.2 110.1 90.5 88.1 108.0 107.4 84.1 82.5 116.4 94.1 95.4 126.9

95.0 99.8 85.0 81.0 99.0 84.7

3.4 4.0 1.0 5.6 1.8 8.7

99.5 104.5 90.8 86.4 103.9 90.2

-1.1 -0.7 -4.8 0.2 -3.1 3.1

79.6

11.5

87.6

3.5

83.9 72.8 84.4

5.4 4.9 9.1

90.2 82.5 89.6

-0.9 -4.8 3.9

105.2 81.7 93.1 86.2 75.7 73.9 103.5 81.2 92.2 81.8 77.3 100.9 85.3

6.4 1.2 4.7 5.1 4.1 2.5 2.8 1.5 4.3 12.8 6.5 0.2 1.8

109.7 89.5 97.3 91.5 81.8 81.2 108.1 88.5 96.4 87.4 82.7 104.4 93.4

2.0 -6.6 0.5 -0.2 -1.9 -4.8 -1.8 -5.8 0.1 7.3 1.1 -3.2 -6.3

80.7 86.8 99.8 113.9 89.5 95.7 106.7 83.6 82.2 98.5 101.1 85.7 75.5 101.4 91.3 92.6 119.4

4.9 1.6 7.1 3.7 3.4 3.5 3.5 6.8 5.9 9.5 6.3 -1.6 7.0 15.0 2.8 2.8 7.5

85.9 92.4 105.0 118.8 95.3 99.8 110.9 89.8 90.6 103.9 107.9 91.1 82.9 106.3 96.7 100.4 122.9

-0.2 -4.0 1.8 -1.3 -2.4 -0.6 -0.8 0.7 -2.5 4.1 -0.5 -6.9 -0.4 10.1 -2.6 -5.0 4.0

109

MEASURING URBAN SPRAWL AND VALIDATING SPRAWL MEASURES

48119 48121 48135 48139 48141 48157 48167 48181 48183 48187 48199 48201 48209 48215 48231 48245 48251 48257 48259 48281 48291 48303 48309 48325 48329 48339 48355 48361 48367 48375 48381 48397 48401 48409 48423 48439 48451 48453 48459 48469 48473 48479 48485 48491

Delta County, TX Denton County, TX Ector County, TX Ellis County, TX El Paso County, TX Fort Bend County, TX Galveston County, TX Grayson County, TX Gregg County, TX Guadalupe County, TX Hardin County, TX Harris County, TX Hays County, TX Hidalgo County, TX Hunt County, TX Jefferson County, TX Johnson County, TX Kaufman County, TX Kendall County, TX Lampasas County, TX Liberty County, TX Lubbock County, TX McLennan County, TX Medina County, TX Midland County, TX Montgomery County, TX Nueces County, TX Orange County, TX Parker County, TX Potter County, TX Randall County, TX Rockwall County, TX Rusk County, TX San Patricio County, TX Smith County, TX Tarrant County, TX Tom Green County, TX Travis County, TX Upshur County, TX Victoria County, TX Waller County, TX Webb County, TX Wichita County, TX Williamson County, TX

92.3 115.1 101.9 94.8 118.1 113.4 115.7 93.9 99.4 98.0 83.4 126.4 93.6 106.1 91.7 114.8 93.7 95.4 87.5 85.4 82.0 106.0 101.8 81.2 109.3 91.9 112.8 97.3 83.6 110.2 104.8 98.8 75.3 94.2 99.5 120.0 97.3 114.6 81.0 102.3 99.8 105.9 100.4 105.2

88.4 99.2 100.3 87.3 112.8 101.0 111.1 92.3 95.1 88.2 77.9 116.2 88.6 98.8 86.4 111.6 88.8 85.6 97.5 85.9 82.8 104.7 99.6

3.8 15.8 1.6 7.5 5.3 12.4 4.6 1.6 4.3 9.8 5.4 10.3 4.9 7.3 5.3 3.2 4.9 9.8 -10.0 -0.5 -0.8 1.3 2.2

97.3 103.6 103.2 92.6 116.2 105.7 114.2 97.8 100.0 95.6 84.0 120.4 92.7 105.7 92.4 115.6 94.0 92.8 101.9 91.5 88.6 111.3 106.4

-5.1 11.4 -1.3 2.2 1.9 7.7 1.5 -3.9 -0.6 2.5 -0.6 6.1 0.9 0.5 -0.7 -0.8 -0.4 2.6 -14.4 -6.1 -6.6 -5.2 -4.6

109.9 87.0 109.8 93.2 79.1 108.3 105.9 90.2 72.1 92.0 93.4 113.0 91.3 109.6 73.1 103.0 94.2 103.8 98.6 97.1

-0.6 4.9 3.1 4.0 4.4 1.9 -1.1 8.7 3.1 2.2 6.1 7.0 6.0 5.0 7.8 -0.7 5.6 2.0 1.8 8.1

113.7 91.8 114.9 97.4 84.6 111.7 111.6 95.1 79.2 97.1 98.7 116.3 98.5 112.6 81.2 108.3 98.6 114.0 103.5 103.5

-4.4 0.1 -2.0 -0.1 -1.0 -1.5 -6.8 3.8 -3.9 -2.8 0.8 3.7 -1.2 2.0 -0.3 -6.0 1.2 -8.1 -3.1 1.6

110

MEASURING URBAN SPRAWL AND VALIDATING SPRAWL MEASURES

48493 48497 49005 49011 49023 49035 49043 49045 49049 49053 49057 50007 50011 50013 51003 51009 51011 51013 51019 51023 51031 51033 51041 51043 51053 51059 51061 51065 51067 51069 51073 51075 51079 51085 51087 51093 51095 51101 51107 51115 51121 51127 51143 51145

Wilson County, TX Wise County, TX Cache County, UT Davis County, UT Juab County, UT Salt Lake County, UT Summit County, UT Tooele County, UT Utah County, UT Washington County, UT Weber County, UT Chittenden County, VT Franklin County, VT Grand Isle County, VT Albemarle County, VA Amherst County, VA Appomattox County, VA Arlington County, VA Bedford County, VA Botetourt County, VA Campbell County, VA Caroline County, VA Chesterfield County, VA Clarke County, VA Dinwiddie County, VA Fairfax County, VA Fauquier County, VA Fluvanna County, VA Franklin County, VA Frederick County, VA Gloucester County, VA Goochland County, VA Greene County, VA Hanover County, VA Henrico County, VA Isle of Wight County, VA James City County, VA King William County, VA Loudoun County, VA Mathews County, VA Montgomery County, VA New Kent County, VA Pittsylvania County, VA Powhatan County, VA

79.6 81.8 91.5 110.9 72.2 118.3 83.4 86.9 114.4 94.3 109.7 103.4 88.3 91.4 90.0 82.7 73.8 176.8 84.1 86.4 101.9 83.8 105.0 87.5 79.3 120.5 84.7 81.0 87.2 92.7 97.2 84.6 85.6 91.3 114.6 82.2 101.7 88.9 112.8 90.4 101.0 82.8 79.5 82.4

73.3 79.9 86.5 106.5 67.4 118.0 77.9 82.4 107.3 88.9 107.1 96.8 81.0 72.4 75.2 75.3

6.3 1.9 5.0 4.3 4.8 0.4 5.5 4.5 7.1 5.4 2.6 6.6 7.4 19.1 14.8 7.3

82.2 87.4 93.1 113.5 81.7 121.5 84.2 93.1 116.5 95.5 111.7 103.1 89.5 79.5 82.0 83.6

-2.6 -5.6 -1.6 -2.7 -9.4 -3.2 -0.8 -6.2 -2.1 -1.1 -2.0 0.3 -1.2 12.0 8.0 -0.9

59.2 80.1 73.0 70.6 94.3

24.9 6.3 28.9 13.2 10.8

66.5 88.1 79.3 78.4 98.2

17.6 -1.7 22.6 5.5 6.8

66.7

12.6

77.9

1.4

68.5 71.8 73.9 69.5 82.6 64.2 72.2 73.8 102.1 70.4 90.2 81.1 93.4 78.3 74.3 73.6 66.1 72.8

16.2 9.3 13.3 23.2 14.6 20.4 13.4 17.5 12.5 11.8 11.5 7.8 19.4 12.0 26.7 9.2 13.4 9.6

75.4 77.6 80.3 75.6 86.8 71.9 78.2 79.0 105.9 79.7 93.9 87.4 99.6 82.0 80.8 80.3 73.1 76.3

9.3 3.4 6.9 17.1 10.4 12.7 7.4 12.3 8.7 2.5 7.9 1.5 13.2 8.3 20.2 2.5 6.4 6.1

111

MEASURING URBAN SPRAWL AND VALIDATING SPRAWL MEASURES

51149 51153 51155 51161 51165 51169 51177 51179 51183 51187 51191 51199 51510 51515 51520 51540 51550 51570 51590 51600 51610 51630 51650 51660 51670 51680 51683 51685 51700 51710 51730 51735 51740 51750 51760 51770 51775 51800 51810 51830 51840 53003

Prince George County, VA Prince William County, VA Pulaski County, VA Roanoke County, VA Rockingham County, VA Scott County, VA Spotsylvania County, VA Stafford County, VA Sussex County, VA Warren County, VA Washington County, VA York County, VA Alexandria city, VA Bedford city, VA Bristol city, VA Charlottesville city, VA Chesapeake city, VA Colonial Heights city, VA Danville city, VA Fairfax city, VA Falls Church city, VA Fredericksburg city, VA Hampton city, VA Harrisonburg city, VA Hopewell city, VA Lynchburg city, VA Manassas city, VA Manassas Park city, VA Newport News city, VA Norfolk city, VA Petersburg city, VA Poquoson city, VA Portsmouth city, VA Radford city, VA Richmond city, VA Roanoke city, VA Salem city, VA Suffolk city, VA Virginia Beach city, VA Williamsburg city, VA Winchester city, VA Asotin County, WA

84.4

105.5

-21.1

108.6

-24.2

116.4 98.9 101.0 89.5 88.9 99.2 98.7 87.2 96.5 92.0 105.1 181.3 100.5 120.4 138.2 108.6 121.8 109.0 115.8 134.6 137.2 127.5 132.6 135.0 118.6 125.9 134.2 125.3 148.1 118.0 106.4 130.6 126.4 135.9 127.7 121.8 97.2 124.5 123.3 128.4 113.9

101.8 89.1 76.8 71.3 86.6 72.2 87.3

14.6 9.8 24.2 18.1 2.3 27.0 11.4

94.5 82.0 78.4 93.5 78.0 92.4

4.4 19.0 11.0 -4.6 21.2 6.3

90.1 92.7 111.0

6.5 -0.7 -5.8

96.1 97.7 112.2

0.4 -5.7 -7.1

104.7

3.9

108.0

0.6

138.4

9.7

141.6

6.5

128.0

2.5

132.4

-1.8

132.1

3.8

135.8

0.1

87.0 117.4

10.2 7.1

93.8 120.6

3.4 3.9

107.6

6.2

113.0

0.8

112

MEASURING URBAN SPRAWL AND VALIDATING SPRAWL MEASURES

53005 53007 53011 53015 53017 53021 53033 53035 53053 53057 53061 53063 53067 53073 53077 54003 54005 54009 54011 54029 54037 54039 54051 54057 54061 54065 54069 54077 54079 54099 54107 55009 55015 55017 55021 55025 55031 55035 55039 55049 55059 55061 55063 55073

Benton County, WA Chelan County, WA Clark County, WA Cowlitz County, WA Douglas County, WA Franklin County, WA King County, WA Kitsap County, WA Pierce County, WA Skagit County, WA Snohomish County, WA Spokane County, WA Thurston County, WA Whatcom County, WA Yakima County, WA Berkeley County, WV Boone County, WV Brooke County, WV Cabell County, WV Hancock County, WV Jefferson County, WV Kanawha County, WV Marshall County, WV Mineral County, WV Monongalia County, WV Morgan County, WV Ohio County, WV Preston County, WV Putnam County, WV Wayne County, WV Wood County, WV Brown County, WI Calumet County, WI Chippewa County, WI Columbia County, WI Dane County, WI Douglas County, WI Eau Claire County, WI Fond du Lac County, WI Iowa County, WI Kenosha County, WI Kewaunee County, WI La Crosse County, WI Marathon County, WI

99.5 98.4 107.2 96.6 97.5 103.2 127.1 100.9 112.3 98.1 107.2 111.9 100.6 98.6 94.2 97.1 102.4 104.2 113.0 107.3 95.5 113.5 105.9 96.6 110.8 83.3 114.8 84.7 100.7 102.2 111.9 95.2 87.6 84.6 85.7 109.9 90.2 97.1 94.3 74.8 110.2 67.8 108.2 85.3

93.7 91.6 103.1 88.3 91.0 94.5 120.5 96.7 107.1 91.9 99.9 109.9 93.6 91.3 90.4 89.2 99.5 99.1 110.2 108.2 89.8 108.6 98.1 90.3 105.6 77.4 111.3 75.3 95.8 98.4 102.7 94.2 74.5 79.1 74.2 102.4 87.2 99.7 85.6 73.7 106.5 65.3 105.2 81.9

5.8 6.7 4.1 8.2 6.5 8.6 6.6 4.2 5.2 6.1 7.3 2.0 7.0 7.3 3.9 7.9 2.9 5.1 2.8 -0.9 5.6 4.8 7.9 6.3 5.2 6.0 3.6 9.4 5.0 3.8 9.2 1.0 13.2 5.5 11.5 7.5 2.9 -2.6 8.6 1.1 3.7 2.5 3.0 3.3

99.2 104.0 108.5 96.9 102.1 102.1 126.0 102.3 113.0 98.3 106.3 113.8 99.3 97.9 100.4 92.4 105.1 104.0 115.5 112.2 94.4 113.7 104.1 96.9 110.6 83.2 116.2 82.5 100.7 108.4 108.8 99.8 83.1 86.5 82.7 109.7 95.1 106.8 92.6 82.6 111.2 76.4 110.5 90.7

0.3 -5.6 -1.3 -0.3 -4.7 1.1 1.0 -1.4 -0.7 -0.3 0.9 -1.9 1.3 0.8 -6.2 4.7 -2.7 0.2 -2.5 -4.9 1.1 -0.2 1.8 -0.3 0.2 0.1 -1.4 2.2 0.1 -6.2 3.1 -4.7 4.5 -1.9 3.0 0.2 -4.9 -9.7 1.6 -7.8 -1.0 -8.6 -2.3 -5.4

113

MEASURING URBAN SPRAWL AND VALIDATING SPRAWL MEASURES

55079 55083 55087 55089 55093 55101 55105 55109 55117 55131 55133 55139 56021 56025

Milwaukee County, WI Oconto County, WI Outagamie County, WI Ozaukee County, WI Pierce County, WI Racine County, WI Rock County, WI St. Croix County, WI Sheboygan County, WI Washington County, WI Waukesha County, WI Winnebago County, WI Laramie County, WY Natrona County, WY

139.8 72.1 99.5 92.1 87.5 105.5 97.7 77.5 97.5 86.2 99.5 107.0 99.4 105.3

141.2 68.6 96.7 86.7 80.5 103.7 95.2 72.1 92.1 79.0 90.3 104.6 95.7 94.6

-1.4 3.5 2.8 5.4 7.1 1.8 2.5 5.4 5.4 7.1 9.2 2.4 3.7 10.7

145.3 77.1 102.9 93.0 90.5 108.1 100.5 81.4 98.2 85.3 94.5 109.7 102.0 102.4

-5.5 -4.9 -3.4 -0.9 -3.0 -2.6 -2.9 -3.9 -0.7 0.8 5.0 -2.8 -2.7 2.8

114

MEASURING URBAN SPRAWL AND VALIDATING SPRAWL MEASURES

Appendix B. County Compactness Factors and Composite Indices for 2010 fips

county

1009

Blount County

1015 1021 1033 1051 1055 1069 1073 1077 1079 1081 1083 1089 1097 1101 1103 1113 1115 1117 1125 1127 4005 4013 4015 4019 4021 4025 4027 5007 5031 5033 5035 5045 5051

Calhoun County Chilton County Colbert County Elmore County Etowah County Houston County Jefferson County Lauderdale County Lawrence County Lee County Limestone County Madison County Mobile County Montgomery County Morgan County Russell County St. Clair County Shelby County, AL Tuscaloosa County, AL Walker County, AL Coconino County, AZ Maricopa County, AZ Mohave County, AZ Pima County, AZ Pinal County, AZ Yavapai County, AZ Yuma County, AZ Benton County, AR Craighead County, AR Crawford County, AR Crittenden County, AR Faulkner County, AR Garland County, AR

density mix centering street composite factor factor factor factor index 90.36 37.85 74.28 60.14 56.6 91.58 89.98 95.11 91.59 93.78 94.83 99.01 94.46 89.38 96.48 91.62 97.61 99.06 102.14 96.47 94.83 91.04 94.43 96.71 90.6 95.58 110.5 96.2 102.91 96.42 96 99.68 95.22 95.83 92.25 96.93 95.11 92.69

86.7 52.55 104.27 60.63 91.28 102.37 110.72 84.43 51.74 87.9 58.45 98.59 108.17 120.67 95.35 90.91 55.96 91.33 101.44 65.74 105.89 118.07 90.76 109.55 74.63 89.71 105.56 95.05 97.46 90.19 115.43 92.1 89.51

117.7 81.61 76.99 86.59 116.86 98.64 122.44 105.63 86.98 104.17 89.78 103.31 93.94 118.34 116.51 78.65 81.95 88.2 136.82 86.66 159.7 118.48 97.35 129.25 93.08 88.28 142.91 104.81 113.68 82.88 79.24 83.67 116.53

104.38 62.37 124.68 85.71 93.1 88.97 126.81 88.5 66.67 84.55 82.64 114.82 113.78 105.98 101.04 93.54 84.47 92.91 110.56 92.5 80.11 118.04 95.37 101.54 100.74 86.4 107.38 89.33 76.68 80.03 89.18 74.78 103.18

100.11 64.14 100.33 76.15 98.43 95.2 118.64 91.48 66.75 91.5 75.51 104.53 104.72 114.89 102.96 86.71 72.65 89.53 114.39 79.62 113.04 120.56 93.58 113.66 88.9 87.49 117.54 95.07 94.83 82.74 93.93 82.83 100.6

115

MEASURING URBAN SPRAWL AND VALIDATING SPRAWL MEASURES

5053 5069 5079 5085 5087 5091 5111 5119 5125 5131 5143 6001 6007 6013 6017 6019 6025 6029 6031 6037 6039 6041 6047 6053 6055 6059 6061 6065 6067 6069 6071 6073 6075 6077 6079 6081 6083 6085 6087

Grant County, AR Jefferson County, AR Lincoln County, AR Lonoke County, AR Madison County, AR Miller County, AR Poinsett County, AR Pulaski County, AR Saline County, AR Sebastian County, AR Washington County, AR Alameda County, CA Butte County, CA Contra Costa County, CA El Dorado County, CA Fresno County, CA Imperial County, CA Kern County, CA Kings County, CA Los Angeles County, CA Madera County, CA Marin County, CA Merced County, CA Monterey County, CA Napa County, CA Orange County, CA Placer County, CA Riverside County, CA Sacramento County, CA San Benito County, CA San Bernardino County, CA San Diego County, CA San Francisco County, CA San Joaquin County, CA San Luis Obispo County, CA San Mateo County, CA Santa Barbara County, CA Santa Clara County, CA Santa Cruz County, CA

89.11 94.66 88.97 91.76 88.44 97.29 89.31 100.95 92.78 97.44 98.58 137.65 99.2 112.02 96.18 103.35 99.38 102.91 100.77 152.55 96.68 109.25 100.54 109.05 102.69 134.15 101.97 105.36 115.28 103.1 106.82 118.35 250.84 106.5 97.52 130.72 116.62 131.02 104.2

79.34 97.82 51.59 79.64 61.16 106.83 105.78 111.48 80.99 103.71 104.46 143.4 121.87 128.7 88.17 127.85 132.78 121.33 115.21 145.2 110.34 141.52 122.04 122.36 135.45 142.55 116.93 117.55 128.54 115.79 122.13 129.64 153.79 132.92 124.79 144.53 139.7 139.68 138.71

77.98 96.55 72.47 91.84 73.67 82.03 77.99 116.72 106.43 93.42 109.89 115.28 106.28 100.81 84.58 104.03 99.61 99.62 108.98 121.62 104.67 96.85 112.8 110.26 131.01 95.13 90.93 108.49 135.7 78.56 95.87 121.82 258.47 104.79 111.43 93.82 112.02 107.58 114.16

60.72 113.66 62.71 75.65 72.44 115.58 71.03 127.01 75.8 108.24 91.83 151.09 91.9 121.28 77.8 94.25 82.71 92.21 90.98 141.02 69.69 111.15 85.94 101.72 110.28 144.21 98.05 98.38 129.68 105.1 92.42 116.14 215.72 118.62 102.74 131.35 116.13 132.85 107.34

70.67 100.85 60.74 80.69 67.05 100.54 82.34 117.74 86.1 100.89 101.5 146.57 106.08 119.84 83.17 109.31 104.58 105.08 105.04 150.67 94.12 118.57 106.74 113.71 125.09 136.66 102.49 109.41 134.5 100.81 105.45 127.15 251.27 119.85 111.53 131.72 126.69 135.11 120.35

116

MEASURING URBAN SPRAWL AND VALIDATING SPRAWL MEASURES

6089 6095 6097 6099 6101 6107 6111 6113 6115 8001 8005 8013 8014 8019 8031 8035 8039 8041 8059 8069 8077 8101 8119 8123 9001 9003 9007 9009 9011 9013 10001 10003 11001 12001 12003 12005 12009 12011 12015

Shasta County, CA Solano County, CA Sonoma County, CA Stanislaus County, CA Sutter County, CA Tulare County, CA Ventura County, CA Yolo County, CA Yuba County, CA Adams County, CO Arapahoe County, CO Boulder County, CO Broomfield County, CO Clear Creek County, CO Denver County, CO Douglas County, CO Elbert County, CO El Paso County, CO Jefferson County, CO Larimer County, CO Mesa County, CO Pueblo County, CO Teller County, CO Weld County, CO Fairfield County, CT Hartford County, CT Middlesex County, CT New Haven County, CT New London County, CT Tolland County, CT Kent County, DE New Castle County, DE District of Columbia, DC Alachua County, FL Baker County, FL Bay County, FL Brevard County, FL Broward County, FL Charlotte County, FL

96 106.86 100.37 107.86 98.92 100.44 110.13 107.3 97.57 106.63 114.44 107.71 105.87 90.58 129.34 102.77 88.27 104.62 106.94 100.68 101.69 100.43 94.68 97.29 110.88 107.85 95.74 107.16 96.76 96.05 94.72 108.44 193.52 100.66 89.21 99.21 102.39 120.61 94.98

110.79 130.6 131.12 135.71 119.22 117.82 131.48 126.92 95.43 122.25 124.3 122 113.8 67.38 137.67 97.61 44.14 119.18 125.25 117.76 113.73 112.15 82.25 114.35 131.47 126.56 116.02 128.91 106.51 89.61 97.37 126.15 138.05 110.17 63.21 105.55 103.2 133.24 97.96

114.25 103.94 101.87 94.54 126.45 102.53 99.8 98.5 82.17 82.26 102.43 111.33 83.11 174.54 92.17 72.69 95.89 90.89 111.95 124.35 112.96 81.88 111.18 125.41 138.02 98.9 137.15 131.52 97.77 102.26 111.75 219.97 115.43 89.68 93.7 86.39 95.43 103.74

88.66 114.95 97.67 107.84 82.89 93.41 114.98 110.1 89.37 122.37 134.2 115.52 129.14 117.81 181.54 97.77 50.26 123.96 112.99 103.05 107.33 121.67 108.04 95.06 101.99 92.46 81.98 102.88 85.24 63.29 89.82 121.39 185.15 107.74 61.02 115.16 110.4 148.86 114.83

103.07 117.8 109.81 114.52 108.68 104.49 117.82 113.53 88.8 110.59 123.81 117.87 110.09 170.48 96.94 54.3 113.79 111.4 110.57 114.88 114.91 89.53 105.65 122.04 120.5 97.68 124.04 106.33 83.17 95 121.4 206.37 110.74 69.39 104.31 100.75 131.01 103.64

117

MEASURING URBAN SPRAWL AND VALIDATING SPRAWL MEASURES

12019 12021 12031 12033 12035 12039 12053 12057 12061 12069 12071 12073 12081 12083 12085 12086 12089 12091 12095 12097 12099 12101 12103 12105 12109 12111 12113 12115 12117 12127 12129 13013 13015 13021 13029 13035 13045 13047 13051

Clay County, FL Collier County, FL Duval County, FL Escambia County, FL Flagler County, FL Gadsden County, FL Hernando County, FL Hillsborough County, FL Indian River County, FL Lake County, FL Lee County, FL Leon County, FL Manatee County, FL Marion County, FL Martin County, FL Miami-Dade County, FL Nassau County, FL Okaloosa County, FL Orange County, FL Osceola County, FL Palm Beach County, FL Pasco County, FL Pinellas County, FL Polk County, FL St. Johns County, FL St. Lucie County, FL Santa Rosa County, FL Sarasota County, FL Seminole County, FL Volusia County, FL Wakulla County, FL Barrow County, GA Bartow County, GA Bibb County, GA Bryan County, GA Butts County, GA Carroll County, GA Catoosa County, GA Chatham County, GA

97.16 99.42 106.31 99.94 96.82 90.27 96.2 106.16 97.1 95.53 98.87 102.05 102.17 93.51 98.62 137.38 93.25 100.2 108.01 98.45 107.77 99.18 114.66 96.76 97.43 100.74 92.28 101.61 105.12 99.33 89.66 92.36 90.76 98.07 89.84 91.1 92.24 93.34 99.64

92.55 104.7 113.1 109.08 82.32 57.12 80.29 115.63 101.81 87.32 104.6 106.83 114.33 83.3 110.16 132.85 78.04 113.18 110.76 86.64 125.08 100.48 132.11 90.29 86.85 97.46 93.99 116.04 116.39 107.91 45.54 70.78 77.69 113.15 61.04 82.26 80.47 79.45 117.03

98.14 83.67 118.71 100.14 79.96 83.72 108.25 127.6 112.72 121.33 119.36 149.96 112.33 140.38 106.69 131.33 98.01 109.67 118.48 87.23 107.06 84.02 93.74 115.86 85.06 102.45 81.78 113.62 81.81 100.7 78.68 85.3 86.6 103.59 81.95 87.09 108.64 88.25 126.17

95.4 105.06 125.06 116.67 99.05 95.13 102.08 128.18 132.01 116.84 121.83 99.11 129.01 98.85 113.84 156.48 97.21 105.87 124.47 114.77 118.32 117.84 163.76 120.94 106.86 120.07 80.59 124.42 121.13 115.72 79.41 72.18 80.47 112.7 71.54 67.51 59.41 78.55 126.88

94.71 97.74 119.96 108.16 86.78 76.69 95.84 124.51 113.79 106.64 114.11 118.31 118.27 105.07 109.26 149.93 89.42 109.14 119.5 95.92 118.4 100.48 132.94 107.53 92.48 106.54 83.78 117.59 107.72 107.47 66.29 74.92 79.63 108.69 69.79 77.24 81.28 80.91 122.03

118

MEASURING URBAN SPRAWL AND VALIDATING SPRAWL MEASURES

13053 13057 13059 13063 13067 13073 13077 13083 13085 13089 13095 13097 13103 13113 13115 13117 13121 13127 13135 13139 13143 13145 13151 13153 13169 13171 13177 13179 13185 13189 13195 13199 13207 13213 13215 13217 13219 13221 13223

Chattahoochee County, GA Cherokee County, GA Clarke County, GA Clayton County, GA Cobb County, GA Columbia County, GA Coweta County, GA Dade County, GA Dawson County, GA DeKalb County, GA Dougherty County, GA Douglas County, GA Effingham County, GA Fayette County, GA Floyd County, GA Forsyth County, GA Fulton County, GA Glynn County, GA Gwinnett County, GA Hall County, GA Haralson County, GA Harris County, GA Henry County, GA Houston County, GA Jones County, GA Lamar County, GA Lee County, GA Liberty County, GA Lowndes County, GA McDuffie County, GA Madison County, GA Meriwether County, GA Monroe County, GA Murray County, GA Muscogee County, GA Newton County, GA Oconee County, GA Oglethorpe County, GA Paulding County, GA

97.14 97.06 100.91 106.35 106.99 96.83 92.69 89.57 89.94 111.99 97.65 95.83 91.03 93.23 92.92 96.31 107.63 92.87 106.36 94.45 90.08 89.51 95.26 99.67 90.26 90.01 90.74 96.95 95.78 89.94 89.81 89.17 89.72 90.63 103.92 94.48 90.84 88.61 93.49

100.48 94.58 115.76 106.15 116.91 95.43 85.33 56.36 63.53 120.73 109.27 89.53 60.74 94.36 90.67 91.93 122.6 102 111.94 89.1 73.41 34.28 81.75 97.7 80.32 68.75 63.81 85.66 102.08 68.85 53.09 52.92 49.47 57.18 119.01 61.24 85.05 22.76 68.19

70.87 80.91 98.31 84.62 91.39 80.24 81.74 80.64 86.08 96.18 95.6 103.33 84.13 100.88 103.37 97.11 146.48 95.73 88.7 139.3 78.3 71.89 86.07 89.66 81.59 79.24 80.13 100.72 106.87 78.49 73.41 79.4 77.43 84.75 133.98 123.65 74.86 70.81 83.49

98.62 83.44 92.89 98.1 107.76 72.04 72.61 69.91 69.43 100.65 107.9 70.96 75.9 78.34 89.35 68.48 108.57 111.38 89.68 87.59 82.15 62.25 74.28 91.56 59.82 69.42 67.38 88.85 91.72 72.18 61.79 65.55 66.44 68.86 108.41 77.77 69.72 45.28 74.96

89.61 86.1 102.49 98.49 107.28 82.48 78.64 67.3 71.24 109.34 103.3 87.25 72.13 89.51 92.52 85.41 126.94 100.62 98.95 103.3 75.97 55.12 80.21 93.23 72.19 70.75 69.06 91.21 98.88 71.4 61.49 64.31 63.06 68.85 120.64 86.46 74.87 45.49 74.76

119

MEASURING URBAN SPRAWL AND VALIDATING SPRAWL MEASURES

13227 13231 13245 13247 13255 13273 13295 13297 13313 13321 16001 16005 16019 16027 16045 16051 16055 16069 17003 17005 17007 17019 17027 17031 17037 17043 17053 17063 17073 17083 17089 17091 17093 17097 17111 17113 17115 17117 17119

Pickens County, GA Richmond County, GA Rockdale County, GA Spalding County, GA Terrell County, GA Walker County, GA Walton County, GA Whitfield County, GA Worth County, GA Ada County, ID Bannock County, ID Bonneville County, ID Canyon County, ID Gem County, ID Jefferson County, ID Kootenai County, ID Nez Perce County, ID Alexander County, IL Bond County, IL Boone County, IL Champaign County, IL Clinton County, IL Cook County, IL DeKalb County, IL DuPage County, IL Ford County, IL Grundy County, IL Henry County, IL Jersey County, IL Kane County, IL Kankakee County, IL Kendall County, IL Lake County, IL McHenry County, IL McLean County, IL Macon County, IL Macoupin County, IL Madison County, IL

90.19

68.61

81.67

61.08

68.89

99.09 95.92 93.04 88.84 91.84 91.96 94.64 88.76 103.58 101.28 98.84 98.64 92.23 89.1 97.55 99.34 89.05 91.76 96.36 109.28 89.17 151.4 99.94 111.41 90 92.99 90.62 89.46 108.34 95.65 94.3 103.98 98.53 104.94 95.56 92.2 96.83

111.4 93.91 83.74 78.95 77.95 71.8 87.29 52.25 124.6 123.06 118.52 112.28 83.41 69.82 113.96 116.89

124.13 82.64 102.12 78.22 88.88 87.33 115.72 84.69 102.02 128.18 99.62 90.6 76.44 83.29 122.32 92.82 70.12 129.58 81.63 141.54 82.04 155.66 84.27 88.41 78.31 86.63 84.59 85.72 90.86 105.98 82.01 97.08 83.23 110.85 112.75 78.1 103.17

104.91 86.78 85.73 74.53 75.62 54.96 88.51 68.22 108.68 124.04 109.57 106.1 113.29 88.98 101.44 113.12 121.33 109.49 85.74 107.66 94.5 170.12 93.39 126.48 83.16 110.27 81.22 85.66 109.06 97.47 95.42 118.15 95.57 102.41 97.28 115.16 114.28

112.49 87.13 88.83 74.9 79.24 70.32 95.63 66.48 112.28 124.18 108.39 102.41 89.06 78.26 111.14 107

87.79 95.37 127.58 87.01 141.34 111.36 135.96 136.48 101.16 116.08 78.12 120.57 119.77 90.54 121.02 105.24 120.63 114.15 111.71 119.34

105.89 87.08 127.19 85.06 169.04 96.51 119.67 96.19 97.17 91.31 80.72 109.11 105.96 88.08 112.71 94.49 112.27 106.24 99.1 110.62

120

MEASURING URBAN SPRAWL AND VALIDATING SPRAWL MEASURES

17123 17129 17131 17133 17143 17147 17161 17163 17167 17179 17183 17197 17201 17203 18003 18005 18011 18013 18015 18019 18021 18029 18035 18039 18043 18047 18051 18055 18057 18059 18061 18063 18067 18073 18081 18089 18091 18095 18097

Marshall County, IL Menard County, IL Mercer County, IL Monroe County, IL Peoria County, IL Piatt County, IL Rock Island County, IL St. Clair County, IL Sangamon County, IL Tazewell County, IL Vermilion County, IL Will County, IL Winnebago County, IL Woodford County, IL Allen County, IN Bartholomew County, IN Boone County, IN Brown County, IN Carroll County, IN Clark County, IN Clay County, IN Dearborn County, IN Delaware County, IN Elkhart County, IN Floyd County, IN Franklin County, IN Gibson County, IN Greene County, IN Hamilton County, IN Hancock County, IN Harrison County, IN Hendricks County, IN Howard County, IN Jasper County, IN Johnson County, IN Lake County, IN LaPorte County, IN Madison County, IN Marion County, IN

89.56 88.81 88.81 89.84 100.95 88.83 101.09 96.6 97.54 96.01 91.84 101.35 100.8 89.23 100.69 96.38 94.39 92.73 89.42 97.57 91.51 91.96 103.15 94.95 101.1 90.85 92.92 90.44 99.85 93.31 91.11 95.72 98.37 89.52 98.31 102.28 95.04 96.4 108.62

95.57 90.2 97.3 90.63 120.84 107.89 128.28 114.62 115.25 107.55 99.84 114.01 123.79 111.21 113.3 101.42 103.9 36.11 86.26 113.96 101.15 82.67 118.8 104.81 121.02 54.82 109.39 93.15 104.3 95.1 56.7 91.32 114.28 90.18 116.23 124.13 104.81 113.83 123.19

68.03 83.8 71.15 77.62 143.87 81.61 104.97 90.19 157.52 85.37 112.75 92.55 117.91 85.84 110.06 108.25 79.83 76.3 86.24 86.06 76.58 89.51 91.63 89.66 86.15 78.33 77.46 82.02 81.69 82.93 85.5 79.42 95.94 73.22 81.08 124.4 108.11 107.92 125.02

113.51 84.09 95.19 91.7 112.87 83.39 116.1 113.08 108.44 110.59 117.88 100.58 120.01 94.01 100.51 114.65 90.61 63.42 85.98 107.2 109.38 96.29 109.13 114.82 99.15 95.48 124.54 88.86 94.95 84.8 61.31 89.16 109.61 51.82 102.48 126.26 96.11 112.32 127.04

89.47 83.22 84.98 84.14 124.81 87.9 115.93 104.58 124.88 99.85 107.05 102.68 119.75 93.77 107.76 106.54 90.12 58.47 83.54 101.51 93.25 87.5 107.18 101.34 102.35 74.56 101.36 85.62 93.93 86.14 66.71 85.98 105.75 69.9 99.4 124.35 101.29 109.63 126.5

121

MEASURING URBAN SPRAWL AND VALIDATING SPRAWL MEASURES

18105 18109 18115 18119 18127 18129 18133 18141 18145 18153 18157 18159 18163 18165 18167 18173 18175 18179 18183 19011 19013 19017 19049 19061 19085 19103 19105 19113 19121 19129 19153 19155 19163 19169 19181 19183 19193 20015 20045

Monroe County, IN Morgan County, IN Ohio County, IN Owen County, IN Porter County, IN Posey County, IN Putnam County, IN St. Joseph County, IN Shelby County, IN Sullivan County, IN Tippecanoe County, IN Tipton County, IN Vanderburgh County, IN Vermillion County, IN Vigo County, IN Warrick County, IN Washington County, IN Wells County, IN Whitley County, IN Benton County, IA Black Hawk County, IA Bremer County, IA Dallas County, IA Dubuque County, IA Harrison County, IA Johnson County, IA Jones County, IA Linn County, IA Madison County, IA Mills County, IA Polk County, IA Pottawattamie County, IA Scott County, IA Story County, IA Warren County, IA Washington County, IA Woodbury County, IA Butler County, KS Douglas County, KS

104.36 94.61 91.06 91.06 96.95 92.19 91.01 100.67 98.24 89.97 104.58 89.55 101.79 103.23 96.9 99.66 94.15 89.98 90.31 88.87 99.1 89 95.45 100.57 89.16 103.02 89.77 100.19 90.62 89.93 102.96 97.53 100.21 96.6 93.98 90 97.33 95.93 100.21

112.59 85.99 97.13 35.65 108.4 75.2 96.03 117.65 116 94.33 112.14 85.73 119.7 90.48 111.19 102.11 67.81 90.1 89.14 108.97 129.91 112.79 106.94 130.56 113.13 124.12 115.53 118.29 124.56 84.78 129.31 120.78 128.03 115.01 105.61 104.89 125.17 116.69 127.37

163.85 85.6 78.9 78.62 88.88 81.37 82.78 124.8 82.26 85.42 101.52 80.1 120.43 79.32 114.75 81.65 80.3 83.04 84.12 90.6 94.2 82.24 79.89 115.08 76.21 157.95 71.55 121.29 70.25 77.08 116.94 95.92 85.19 125.73 82.31 78.56 117.13 81.59 99.68

98.52 99.46 99.39 99.32 87.95 81.92 73.04 131.2 97.84 79.03 96 62.84 116.35 155.06 128.65 82.32 87.16 70.18 56.3 97.81 118.5 77.7 91.67 106.99 76.79 85.78 95.83 103.21 103.16 92.04 112.82 99.22 130.22 97.63 83.56 86.53 122.41 76.86 98.22

125.06 89.15 89.41 69.87 94.37 78.1 81.95 123.48 98.21 83.81 104.5 74.17 118.41 108.87 116.27 89.18 77.7 78.93 74.69 95.65 113.18 87.91 91.77 116.81 85.87 122.39 91.37 113.58 96.4 82.25 119.6 104.25 113.79 111.05 89.09 87.36 119.6 90.86 108.05

122

MEASURING URBAN SPRAWL AND VALIDATING SPRAWL MEASURES

20059 20061 20079 20085 20091 20103 20121 20139 20149 20161 20173 20177 20191 20209 21015 21017 21019 21029 21037 21047 21049 21059 21067 21081 21089 21093 21101 21103 21111 21113 21117 21123 21163 21179 21185 21209 21211 21215 21227

Franklin County, KS Geary County, KS Harvey County, KS Jackson County, KS Johnson County, KS Leavenworth County, KS Miami County, KS Osage County, KS Pottawatomie County, KS Riley County, KS Sedgwick County, KS Shawnee County, KS Sumner County, KS Wyandotte County, KS Boone County, KY Bourbon County, KY Boyd County, KY Bullitt County, KY Campbell County, KY Christian County, KY Clark County, KY Daviess County, KY Fayette County, KY Grant County, KY Greenup County, KY Hardin County, KY Henderson County, KY Henry County, KY Jefferson County, KY Jessamine County, KY Kenton County, KY Larue County, KY Meade County, KY Nelson County, KY Oldham County, KY Scott County, KY Shelby County, KY Spencer County, KY Warren County, KY

89.92 96.96 90.56 88.64 104.45 95.13 89.17 89.37 89 98.61 102.93 98.59 88.32 101.91 99.7 97.22 94.45 95.94 102.73 97.34 93.45 99.18 110.05 90.59 94.52 95.48 99.09 89.37 109.11 94.35 104.06 89.43 93.39 91.95 94.48 95.24 95.85 91.13 101.86

101.1 115.17 77.77 125.43 99.39 87.98 97.03

118.91 111.59 98.41 113.88 101.93 93.99 98.55 83.26 124.27 94.37 102 109.86 128.66 52.57 87.52 90.76 105.95 76.6 119.34 102.5 117.51 63.3 46.63 66.86 74.42 97.32 91.76 31.97 102.72

85.19 84.76 75.64 79.63 86.47 87.24 79.03 68.66 81.55 93.38 117.57 125.79 84.72 103.1 95.37 80.83 126.68 81.17 85.29 87.11 79.27 121.56 134.26 80.01 78.55 131.65 76.39 77.64 118.64 84.93 88.49 84.72 84.9 78.24 80.9 80.79 112.29 75.02 124.59

101.84 128.69 73.36 44.65 101.88 93.72 102.93 75.02 95.3 105.56 112.3 108.8 92.96 127.92 84.83 92.96 104.55 86.62 109.72 104.06 98.84 106.12 116.37 76.95 112.22 93.87 103.24 85.73 123.85 91.02 119.32 65.93 78.41 89.54 81.7 97.28 86.78 76.42 100.77

93.07 85.7 65.47 105.76 92.25 87.08 77.91

116.34 114.14 88.76 114.79 94.26 88.94 107.65 83.25 106.95 94.59 91.64 111.6 128.22 68.44 91.41 103.72 95.15 77.68 122.42 91.41 109.28 69.47 69.46 76.81 78.36 90.72 95.79 60.36 109.46

123

MEASURING URBAN SPRAWL AND VALIDATING SPRAWL MEASURES

21239 22005 22015 22017 22019 22031 22033 22043 22047 22051 22055 22057 22063 22071 22073 22075 22077 22079 22087 22089 22095 22099 22103 22109 22111 22121 23001 23005 23019 23023 23031 24001 24003 24005 24009 24013 24015 24017 24021

Woodford County, KY Ascension Parish, LA Bossier Parish, LA Caddo Parish, LA Calcasieu Parish, LA De Soto Parish, LA East Baton Rouge Parish, LA Grant Parish, LA Iberville Parish, LA Jefferson Parish, LA Lafayette Parish, LA Lafourche Parish, LA Livingston Parish, LA Orleans Parish, LA Ouachita Parish, LA Plaquemines Parish, LA Pointe Coupee Parish, LA Rapides Parish, LA St. Bernard Parish, LA St. Charles Parish, LA St. John the Baptist Parish, LA St. Martin Parish, LA St. Tammany Parish, LA Terrebonne Parish, LA Union Parish, LA West Baton Rouge Parish, LA Androscoggin County, ME Cumberland County, ME Penobscot County, ME Sagadahoc County, ME York County, ME Allegany County, MD Anne Arundel County, MD Baltimore County, MD Calvert County, MD Carroll County, MD Cecil County, MD Charles County, MD Frederick County, MD

93.43 92.32 95.13 98.39 95.68 89.07 103.91 88.67 93.41 113.17 99.95 95.04 93.18 121.91 95.23 90.01 91.55 93.23 100.03 93.42 97.39 90.6 95.66 96.62 89.87 92.8 94.76 98.75 92.4 91.37 92.68 94.56 105.04 109.47 95.09 95.33 93.63 97.94 97.32

105.61 90.2 94.84 108.22 105.58 61.88 113.92 34.23 93.69 132.12 114.45 99.35 62.05 137.94 94.61 91.73 71.09 98.11 121.48 97.97 101.63 70.42 94.37 103.72 71.18 93.51 103.78 114.38 98.83 75.85 89.8 117.81 115.29 130.43 73.94 95.07 88.61 88.84 108.73

79.51 93.22 83.39 98.44 123.81 140.34 97.85 66.17 84.62 84.47 110.96 143.72 84.88 153.63 111.6 81.72 100.74 80.94 81.23 88.78 94.32 97.06 99.01 70.25 81.41 136.26 138.89 131.29 95.72 93.72 106.32 100.72 100.71 82.27 100.64 89.42 83.65 104.01

90.95 86.92 90.35 110.2 94.14 77.66 114.04 64.67 92.02 148.19 106.53 98.05 75.38 214.43 108.52 104.87 98.29 101.17 130.72 108.41 109.44 86.13 109.33 107.65 78.43 106.35 91.39 90.26 77.32 87.89 78.52 116.79 118.53 118.19 107.81 94.25 100.5 107.96 100.82

90.36 88.2 88.54 104.82 106.07 90.19 109.39 53.79 88.54 124.62 110.08 111.43 73.3 172.01 103.15 90 97.87 110.48 94.01 99.13 81.51 98.87 102.21 71.48 91.81 108.27 113.36 99.95 84.47 85.7 111.21 112.5 118.58 87.08 95.35 91.2 93.17 103.44

124

MEASURING URBAN SPRAWL AND VALIDATING SPRAWL MEASURES

24025 24027 24031 24033 24035 24039 24043 24045 24510 25001 25003 25005 25009 25011 25013 25015 25017 25021 25023 25025 25027 26015 26017 26021 26025 26027 26037 26045 26049 26065 26067 26075 26077 26081 26087 26093 26099 26115 26121

Harford County, MD Howard County, MD Montgomery County, MD Prince George's County, MD Queen Anne's County, MD Somerset County, MD Washington County, MD Wicomico County, MD Baltimore city, MD Barnstable County, MA Berkshire County, MA Bristol County, MA Essex County, MA Franklin County, MA Hampden County, MA Hampshire County, MA Middlesex County, MA Norfolk County, MA Plymouth County, MA Suffolk County, MA Worcester County, MA Barry County, MI Bay County, MI Berrien County, MI Calhoun County, MI Cass County, MI Clinton County, MI Eaton County, MI Genesee County, MI Ingham County, MI Ionia County, MI Jackson County, MI Kalamazoo County, MI Kent County, MI Lapeer County, MI Livingston County, MI Macomb County, MI Monroe County, MI Muskegon County, MI

100.16 104.93 117.8 112.7 91.01 91.18 97.32 96 163.61

109.82 128.35 129.94 124.13 67.98 73.8 110.91 106.22 143.97

96.6 97.95 123.29 90.27 77.17 82.53 127.59 124.92 183.84

33.82 36.98 32.99 38.77 34.74

90.18 96.11 94.04 95.5 89.45 91.92 94.44 97.37 109.11 92.27 94.83 97.5 99.67 92.22 92.3 107.83 92.58 96.94

53.29 30.9 57.23 112.33 108.26 103.98 65.94 77.85 101.46 109.34 118.48 71.44 98.29 106.35 119.56 70.09 81.87 131.48 95.56 110.29

87.88 108.4 90.63 103.91 94.7 131.4 85.64 123.51 141.89 96.34 137.01 113.21 128.07 131.99 104.2 92.09 109.24 96.74

99.78 107.27 116.7 125.16 76.61 110.34 95.52 114.15 196.44 119.45 95.18 120.97 122.2 83.51 112.97 85.5 122.51 117.59 104.2 201.99 98.17 75.47 104.1 99.01 94.09 73.69 63.62 72.87 103.52 104.33 76.97 86.66 90.33 96.76 63.03 80.88 106.26 75.47 107.62

102.01 112.17 127.72 116.51 72.44 86.69 109.9 113.05 190.94

71.8 106.61 97.45 99.21 75.91 88.88 85.6 110.66 123.32 80.1 105.3 102.33 113.92 86.52 87.13 111.9 91.42 103.66

125

MEASURING URBAN SPRAWL AND VALIDATING SPRAWL MEASURES

26123 26125 26139 26145 26147 26159 26161 26163 27003 27009 27013 27017 27019 27025 27027 27037 27039 27053 27055 27059 27103 27109 27119 27123 27137 27139 27141 27145 27157 27163 27171 28029 28033 28035 28039 28045 28047 28049 28059

Newaygo County, MI Oakland County, MI Ottawa County, MI Saginaw County, MI St. Clair County, MI Van Buren County, MI Washtenaw County, MI Wayne County, MI Anoka County, MN Benton County, MN Blue Earth County, MN Carlton County, MN Carver County, MN Chisago County, MN Clay County, MN Dakota County, MN Dodge County, MN Hennepin County, MN Houston County, MN Isanti County, MN Nicollet County, MN Olmsted County, MN Polk County, MN Ramsey County, MN St. Louis County, MN Scott County, MN Sherburne County, MN Stearns County, MN Wabasha County, MN Washington County, MN Wright County, MN Copiah County, MS DeSoto County, MS Forrest County, MS George County, MS Hancock County, MS Harrison County, MS Hinds County, MS Jackson County, MS

89.64 103.79 96.62 96.26 95.48 90.64 105.17 112.5 101.07 99.34 97.06 89.72 94.8 91.23 101.35 104.83 90.15 114.74 89.84 91.07 97.81 98.99 89.65 117.31 95.96 96.04 92.57 95.49 89.66 100.91 92.03 90.59 95.25 95.34 90.76 92.04 97.88 100.02 95.32

63.71 122.43 104.73 111.36 93.49 78.99 117.06 126.5 111.72 111.8 89.44 100.1 72.57 118.95 115.9 114.35 127.82 94.39 89.01 108.08 106.65 135.35 113.02 104.74 80.55 112.29 101.77 108.44 88.12 89.53 88.58 105.53 69.74 77.68 105.23 107.02 88.99

82.85 99.39 106.96 121.05 115.33 85.3 155.39 136.09 98.03 83.26 81.38 86.19 82.7 80.16 84.41 86.85 78.13 151.96 70.75 80.16 77.6 166.15 85.6 105.13 140.27 81.51 85.4 109.13 80.16 82.51 85.17 72.41 99.48 96.31 77.91 80.99 107.35 141.59 120.77

79.68 107.48 84.83 101.28 87.56 71.88 87.03 148.34 105.23 89.21 83.73 89.97 100.41 79.33 81.24 107.32 95.81 129.69 100.51 86.9 107.27 100.7 58.59 148.75 103.63 85.26 79.35 96.54 119.28 109.35 74.14 81.93 78.18 100.75 92.68 112.7 113.32 102.57 104.57

73.43 110.46 97.83 109.46 97.42 76.88 120.43 139 105.07 94.82 85.88 93.05 75.77 95.56 104.71 93.19 139.24 85.94 83.3 123.35 81.2 133.66 116.7 89.75 80.37 104.25 97.11 100.38 80.87 79.29 87.83 99.35 78.23 88.44 107.51 116.18 103.05

126

MEASURING URBAN SPRAWL AND VALIDATING SPRAWL MEASURES

28073 28089 28093 28121 28127 28131 28137 28143 29003 29013 29019 29021 29027 29031 29037 29043 29047 29049 29051 29055 29071 29077 29095 29097 29099 29107 29113 29135 29145 29165 29177 29183 29189 29219 29221 29225 29510 30009 30013

Lamar County, MS Madison County, MS Marshall County, MS Rankin County, MS Simpson County, MS Stone County, MS Tate County, MS Tunica County, MS Andrew County, MO Bates County, MO Boone County, MO Buchanan County, MO Callaway County, MO Cape Girardeau County, MO Cass County, MO Christian County, MO Clay County, MO Clinton County, MO Cole County, MO Crawford County, MO (pt.)* Franklin County, MO Greene County, MO Jackson County, MO Jasper County, MO Jefferson County, MO Lafayette County, MO Lincoln County, MO Moniteau County, MO Newton County, MO Platte County, MO Ray County, MO St. Charles County, MO St. Louis County, MO Warren County, MO Washington County, MO Webster County, MO St. Louis city, MO Carbon County, MT Cascade County, MT

90.94 96.21 89.58 94.27 89.83 90.38 92.63 88.41 88.73 89.22 98.98 101.7 90.4 95.78 94.15 91.93 97.62 90.37 94.77 89.11 91.1 100.74 105.14 94.9 96.02 89.16 90.59 90.4 92.11 98.15 89.65 104.37 107.75 90.25 89.88 89.7 126.98 88.78 97.85

85.24 91.29 45.7 82.77 72.44 88.05 63.13 60.42 86.17 111.73 107.9 120.56 82.96 94.94 89.25 113.96 103.72 101.06 94.49 119.9 126.53 113.72 87.54 87.92 52.94 117.93 83.25 104.96 108.59 118.4 126.19 65.09 65.15 58.65 137.55 68.92 123.74

82.62 91.18 77.07 81.61 81.01 70.63 71.62 81.24 72.6 80.53 126.76 95.28 97.28 114.42 79.62 81.1 88.28 78.89 122.96 71.96 82.43 88.95 136.74 88.44 85.42 74.98 85.39 68.41 102.74 79.77 73.35 86.54 95.35 88.5 71.89 78.35 194.29 85.23 127.17

69.99 87.79 80.95 77.7 94.49 94.96 95.88 70.41 76.11 106.69 103.07 141.17 84.65 102.52 83.45 90.63 98.64 114.83 85.07 88.13 93.59 115.29 127.96 114.86 99.04 94.53 93.02 89.59 93.49 94.12 65.04 121.39 120.59 88.94 94.61 95.58 185.95 93.01 118.61

77.5 89.4 66.29 79.89 80.34 82.31 75.76 68.56 75.86 96.26 111.6 118.55 85.87 84.89 85.12 99.52 96.15 101.22 87.87 107.86 130.44 103.76 89.9 83.13 75.34 89.37 91.02 92.73 79.98 109.7 115.76 78.76 75.21 75.45 177.33 79.76 121.28

127

MEASURING URBAN SPRAWL AND VALIDATING SPRAWL MEASURES

30063 30111 31025 31043 31055 31109 31153 31155 31159 31177 32003 32031 32510 33011 33015 33017 34001 34003 34005 34007 34009 34011 34013 34015 34017 34019 34021 34023 34025 34027 34029 34031 34033 34035 34037 34039 34041 35001 35013

Missoula County, MT Yellowstone County, MT Cass County, NE Dakota County, NE Douglas County, NE Lancaster County, NE Sarpy County, NE Saunders County, NE Seward County, NE Washington County, NE Clark County, NV Washoe County, NV Carson City, NV Hillsborough County, NH Rockingham County, NH Strafford County, NH Atlantic County, NJ Bergen County, NJ Burlington County, NJ Camden County, NJ Cape May County, NJ Cumberland County, NJ Essex County, NJ Gloucester County, NJ Hudson County, NJ Hunterdon County, NJ Mercer County, NJ Middlesex County, NJ Monmouth County, NJ Morris County, NJ Ocean County, NJ Passaic County, NJ Salem County, NJ Somerset County, NJ Sussex County, NJ Union County, NJ Warren County, NJ Bernalillo County, NM Dona Ana County, NM

98.92 103.87 89.1 98.92 110.08 109.75 101.37 88.71 89.14 89.99 119.01 103.05 104.88 101.22 94 95.77 103 128.56 100.52 115.67 97.81 99.51 161.02 100.59 223.23 93.84 114.81 118.29 105.74 103 105.44 143.82 94.41 101.83 95.74 140.17 95.86 110.26 99.2

119.3 120.17 86.96 114.43 132.45 133.02 112.49 95.5 99.79 86.51 116.44 110.72 133.53 116.91 101.41 105.8 114.8 150.29 120.12 137.68 117.44 113.21 146.99 121.22 156.67 90.14 128.87 135.37 133.26 125.29 110.28 148.45 98 120.78 89.17 153.96 119.17 122.46 106.04

111.04 119.97 86.25 75.16 125.37 115.33 87.29 88.74 77.47 117.82 140.45 131.45 80.1 121.07 97.51 88.23 142.81 86.86 99.61 105.55 101.22 119.51 128.46 87.46 92.82 95.2 109.53 114.47 84.28 87.76 91.35 101.63 80.11 86.24 86.54 89.87 85.21 113.45 114.72

110.74 115.07 95.22 122.4 138.38 121.45 118.08 85.06 81.06 94.88 122.06 103.68 118.62 97.04 82.02 82.45 120.73 143.25 99.94 141.06 145.73 98.78 148.71 104.71 176.49 74 119.34 132.03 121.16 100.05 129.32 135.66 92.91 103.35 87.85 148.9 97.52 131.01 103.66

112.64 118.66 86.59 103.44 133.58 125.13 106.08 86.74 83.4 96.59 130.94 115.45 111.73 111.45 92.08 91.23 125.7 134.43 106.38 131.58 119.65 109.8 158.5 104.41 178.73 85.21 122.92 131.64 114.04 105.09 111.5 140.93 89.08 103.86 87.14 141.99 99.29 124.38 107.46

128

MEASURING URBAN SPRAWL AND VALIDATING SPRAWL MEASURES

35043 35045 35049 35061 36001 36005 36007 36015 36027 36029 36043 36047 36051 36053 36055 36059 36061 36063 36065 36067 36069 36071 36073 36075 36079 36081 36083 36085 36087 36091 36093 36095 36103 36107 36109 36111 36113 36115 36117

Sandoval County, NM San Juan County, NM Santa Fe County, NM Valencia County, NM Albany County, NY Bronx County, NY Broome County, NY Chemung County, NY Dutchess County, NY Erie County, NY Herkimer County, NY Kings County, NY Livingston County, NY Madison County, NY Monroe County, NY Nassau County, NY New York County, NY Niagara County, NY Oneida County, NY Onondaga County, NY Ontario County, NY Orange County, NY Orleans County, NY Oswego County, NY Putnam County, NY Queens County, NY Rensselaer County, NY Richmond County, NY Rockland County, NY Saratoga County, NY Schenectady County, NY Schoharie County, NY Suffolk County, NY Tioga County, NY Tompkins County, NY Ulster County, NY Warren County, NY Washington County, NY Wayne County, NY

97.97 93.52 99.91 94.94 107.1 336.7 99.92 98.96 97.07 109.71 96.91 355.5 93.13 94.67 106.45 128.98 654.01 100.04 101.65 104.46 94.36 101.31 94.19 96.64 94.19 266.34 99.2 175.08 117.77 95.36 107.32 90.59 105.86 94.68 102.44 95.12 94.99 92.47 92.68

91.24 88.26 106.29 85.92 128.39 143.95 115.8 117.49 110.29 131.45 100.82 142.16 102.59 96.7 123.67 149.38 144.57 115.62 107.32 122.19 101.34 113.59 97.46 90.83 95.77 147.42 109.08 131.67 134.18 98.37 130.66 78.79 126.74 75.76 95.84 96.8 105.93 80.23 85.72

110.1 135.96 116.83 108.47 135.96 100.25 121.53 130.79 128.55 111.78 82.72 199.99 78.75 85.84 121.06 111.6 400.25 92.59 112.12 142.75 91.19 90.33 78.22 108.43 83.82 91.93 97.62 78.94 81.37 102.26 104.18 84.01 94.53 82.48 144.53 124.18 183.56 80.51 85.91

85.16 78.81 88.05 76.38 104.63 211.61 93.89 99.06 81.19 93.59 80.37 225.25 53.09 57.89 93.28 160.85 230.33 94.32 84.48 96.45 62.58 87.33 53.47 70.57 88.92 224.01 92.25 179.98 105.52 80.9 110.94 56.05 115.53 64.79 72.43 81.42 89.94 59.21 55.37

95.09 98.91 103.5 89.17 124.04 224.01 109.84 114.63 105.4 114.7 87.62 265.2 77.11 79.49 114.04 147.65 425.15 100.81 101.76 120.8 84.03 97.65 75.78 89.4 88.21 204.16 99.41 152.34 112.27 92.7 116.78 71.39 113.48 74 104.82 99.22 123.51 72.33 74.62

129

MEASURING URBAN SPRAWL AND VALIDATING SPRAWL MEASURES

36119 37001 37003 37007 37019 37021 37023 37025 37027 37035 37037 37051 37053 37059 37063 37065 37067 37069 37071 37079 37081 37087 37089 37093 37101 37115 37119 37127 37129 37133 37135 37141 37145 37147 37151 37157 37169 37179 37183

Westchester County, NY Alamance County, NC Alexander County, NC Anson County, NC Brunswick County, NC Buncombe County, NC Burke County, NC Cabarrus County, NC Caldwell County, NC Catawba County, NC Chatham County, NC Cumberland County, NC Currituck County, NC Davie County, NC Durham County, NC Edgecombe County, NC Forsyth County, NC Franklin County, NC Gaston County, NC Greene County, NC Guilford County, NC Haywood County, NC Henderson County, NC Hoke County, NC Johnston County, NC Madison County, NC Mecklenburg County, NC Nash County, NC New Hanover County, NC Onslow County, NC Orange County, NC Pender County, NC Person County, NC Pitt County, NC Randolph County, NC Rockingham County, NC Stokes County, NC Union County, NC Wake County, NC

129.24 95.78 91.03 89.44 90.81 95.14 90.8 96.2 92.41 93.56 91.14 100.01 90.42 91.08 102.68 91.45 98.47 91.13 95.33 90.47 100.36 91.09 92.12 91.51 93.03 89.4 105.91 91.58 102.34 94.97 99.4 91.15 91.24 98.36 92.22 90.85 90.59 94.98 103.07

146.99 102.85 78.52 65.32 69.18 101.18 78.73 97.46 74.22 91.54 56.42 104.64 69.81 61.13 108.43 83.77 107.56 52.43 103.37 47.46 113.56 79.15 98.21 57.98 70.6 44.18 115.35 88.78 118.86 82.72 106.99 64.41 74.11 104.23 84.74 72.36 52.98 81.73 115.17

93.74 94.52 79.96 80.36 88.65 126.22 87.53 88.76 123.75 85.36 79.76 91.45 77.63 81.22 103.83 99.4 110.15 78.63 110.64 83.61 102.77 80.84 84.83 83.07 103.97 77.93 135.51 88.52 107.7 104.59 120.04 81.67 81.98 117.55 100.63 83.7 81.84 100.88 134.61

123.66 96.28 55.54 52.48 85.96 94.85 75.57 88 80.6 88.36 62.63 90.81 76.98 60.37 103.7 93.79 95.01 63.74 94.2 40.96 95.45 102.68 93.59 70.19 64.44 90.45 101.84 79.45 121.5 82.75 75.56 60.61 61.12 87.14 57.18 76.47 64.72 84.45 96.6

129.58 96.66 70 64.49 79.34 105.5 78.72 90.65 90.83 86.99 65.23 95.86 73.1 66.45 105.89 90.02 103.53 63.96 101.12 56.56 103.84 85.39 90.13 69.27 78.53 69.03 118.52 83.68 115.92 88.95 100.63 67.72 71.08 102.3 79.39 75.79 65.29 88.01 115.62

130

MEASURING URBAN SPRAWL AND VALIDATING SPRAWL MEASURES

37191 37197 38015 38017 38035 38059 39003 39013 39015 39017 39019 39023 39025 39035 39041 39043 39045 39049 39051 39055 39057 39061 39081 39085 39087 39089 39093 39095 39097 39099 39103 39109 39113 39117 39123 39129 39133 39135 39139

Wayne County, NC Yadkin County, NC Burleigh County, ND Cass County, ND Grand Forks County, ND Morton County, ND Allen County, OH Belmont County, OH Brown County, OH Butler County, OH Carroll County, OH Clark County, OH Clermont County, OH Cuyahoga County, OH Delaware County, OH Erie County, OH Fairfield County, OH Franklin County, OH Fulton County, OH Geauga County, OH Greene County, OH Hamilton County, OH Jefferson County, OH Lake County, OH Lawrence County, OH Licking County, OH Lorain County, OH Lucas County, OH Madison County, OH Mahoning County, OH Medina County, OH Miami County, OH Montgomery County, OH Morrow County, OH Ottawa County, OH Pickaway County, OH Portage County, OH Preble County, OH Richland County, OH

93.55 90.06 96.52 99.52 104.24 91.13 95.85 92.89 90.42 101.42 89.77 96.98 98.23 112.92 97.21 96.77 95.2 111.37 90.59 90.84 97.09 110.12 95.1 100.55 93.75 95.01 98.61 105.01 92.38 98.98 96.03 92.97 102.99 89.85 93.01 95.16 94.89 90.05 94.98

78.79 70.68 118.46 125.9 124.99 108.21 114.27 98.58 54.19 116.84 69.05 111.55 97.66 133.64 109.37 121.77 100.29 131.41 113.35 82.83 114.93 134.12 103.84 123.58 81.53 99.59 117.13 131.81 85.12 121.53 105.54 103.49 130.21 49.6 98.23 82.72 103.8 70.46 105.89

130.76 79.45 128.76 113.31 97.01 82.17 117.83 83.73 85.62 94.22 94.41 97.15 83.05 119.54 84.07 104.84 89.76 124.87 82.43 86.85 85.08 141.56 109.52 82.99 83.82 98.19 93.18 114.29 84.52 107.96 93.2 85.25 114.82 83.41 86.34 83.74 90.32 86.69 118.65

84.88 49.29 90.68 97.15 96.71 85.86 118.07 112.11 78.68 101.13 68.25 102.52 84.14 109.64 87.68 102.29 89.15 127.88 93.65 50.2 94.01 113.68 107.8 88.29 104.35 106.48 95.05 116.4 84.97 102.09 57.23 95.62 117.4 46.82 94.39 78.2 100.22 100.99 103.59

96.2 65.08 110.87 111.34 107.25 89.69 114.54 95.99 71.22 104.3 75.19 102.6 88.34 123.93 93.15 108.11 91.91 130.18 93.69 71.79 97.19 131.43 105.14 98.55 88.45 99.77 101.26 121.33 83.25 109.66 84.83 92.84 120.67 58.82 91.15 80.99 96.6 83.63 107.3

131

MEASURING URBAN SPRAWL AND VALIDATING SPRAWL MEASURES

39151 39153 39155 39159 39165 39167 39173 40017 40027 40031 40037 40051 40079 40083 40087 40109 40111 40113 40117 40131 40135 40143 40145 41003 41005 41009 41017 41029 41039 41047 41051 41053 41067 41071 42003 42005 42007 42011 42013

Stark County, OH Summit County, OH Trumbull County, OH Union County, OH Warren County, OH Washington County, OH Wood County, OH Canadian County, OK Cleveland County, OK Comanche County, OK Creek County, OK Grady County, OK Le Flore County, OK Logan County, OK McClain County, OK Oklahoma County, OK Okmulgee County, OK Osage County, OK Pawnee County, OK Rogers County, OK Sequoyah County, OK Tulsa County, OK Wagoner County, OK Benton County, OR Clackamas County, OR Columbia County, OR Deschutes County, OR Jackson County, OR Lane County, OR Marion County, OR Multnomah County, OR Polk County, OR Washington County, OR Yamhill County, OR Allegheny County, PA Armstrong County, PA Beaver County, PA Berks County, PA Blair County, PA

98.73 101.67 95.85 94.04 97.43 93.06 94.89 97.03 101.04 99.03 90.09 91.37 89.15 89.7 89.63 103.44 89.76 93.63 88.73 92.33 89.78 102.6 93.2 100.72 101.8 93.28 95.73 97.76 101.73 101.62 120.53 94.97 110.39 99.08 109.54 92.89 95.17 108.58 97.22

120.66 125.68 111.81 77.41 106.62 88.2 111.78 97.68 107.98 118.45 85.48 75.37 67.37 68.27 80.94 120.48 90.51 66.07 75.14 79.74 72.88 121.46 77.7 123.18 126.17 102.74 115.65 122.2 127.48 130.36 142.82 105.79 132.91 122.85 133.89 85.75 110.16 126.11 121.95

98.8 109.41 91.49 81.94 84.37 86.67 91.96 82.74 106.44 98.2 84.46 86.82 83.45 90.56 81.73 122.5 83.84 86.07 77.53 87.59 91.9 117.13 83.08 126.52 90.03 80.42 115.3 122.65 138.05 123.77 150.58 80.13 85.02 81.32 145.4 101.54 84.42 116 124.31

120.61 114.42 95.52 86.51 88.63 83.86 82.11 92.01 102.24 116.33 104.69 102.85 99.19 98.34 88.92 117.89 122.81 96.84 99.62 95.45 101.22 113.15 102.13 95.34 96.25 84.73 80.19 91.71 98.88 101.1 166.68 83.85 113.1 93.49 135.7 84.86 111.13 110.71 123.01

112.26 116.17 98.31 81.01 92.75 84.77 93.91 90.35 105.59 110.11 88.85 86.23 80.78 83.21 81.43 120.32 95.86 81.87 81.37 85.82 86.03 117.17 86.14 114.46 104.5 87.73 102.17 110.84 120.9 117.96 157.06 88.86 113.09 98.97 139.34 88.95 100.28 119.4 121.01

132

MEASURING URBAN SPRAWL AND VALIDATING SPRAWL MEASURES

42017 42019 42021 42025 42027 42029 42041 42043 42045 42049 42051 42069 42071 42075 42077 42079 42081 42085 42091 42095 42099 42101 42103 42125 42129 42131 42133 44001 44003 44005 44007 44009 45003 45007 45015 45019 45031 45035 45037

Bucks County, PA Butler County, PA Cambria County, PA Carbon County, PA Centre County, PA Chester County, PA Cumberland County, PA Dauphin County, PA Delaware County, PA Erie County, PA Fayette County, PA Lackawanna County, PA Lancaster County, PA Lebanon County, PA Lehigh County, PA Luzerne County, PA Lycoming County, PA Mercer County, PA Montgomery County, PA Northampton County, PA Perry County, PA Philadelphia County, PA Pike County, PA Washington County, PA Westmoreland County, PA Wyoming County, PA York County, PA Bristol County, RI Kent County, RI Newport County, RI Providence County, RI Washington County, RI Aiken County, SC Anderson County, SC Berkeley County, SC Charleston County, SC Darlington County, SC Dorchester County, SC Edgefield County, SC

102.39 93.68 95.43 93.36 110.1 98.81 98.59 104.58 119.69 102.74 93.03 101.86 102.63 96.31 111.48 99.44 97.09 95.34 107.67 103.88 89.79 206.38 91.08 95.07 95.84 90.4 99.69 109.79 103.82 99.45 121.1 94.03 93.29 92.29 98.3 103.2 91.78 103.61 89.95

126.03 105.26 107.43 98.43 115.7 117.12 111.24 124.71 141.69 130.88 102.25 133.13 119.9 122.77 134.36 121.47 120.85 106.25 136.32 133.01 63.67 144.48 56.19 106.69 111.77 51.38 112.24 144.16 122.09 121.07 142.01 102.13 79.37 82.54 88.34 119.32 86.08 98.38 55.96

79.87 120.02 120.16 90.96 149.49 91.2 85.52 129.24 83.25 122.48 96.86 134.53 128.6 84.72 115.73 93.27 113.98 83.44 85.84 101.8 91.33 178.43 144.75 93.55 104.88 86.24 115.21 83.56 81.7 99.03 141.75 88.56 103.25 110.42 80.72 138.48 84.55 81.02 76.27

99.58 79.27 119.48 97.65 91.83 89.11 112.72 125.68 137.9 102.4 108.42 123.5 94.47 116.98 137.75 114.55 117.91 87.04 109.26 124.28 79.02 209.98 90.61 102.25 108.5 74.76 96.33 135.16 122.57 118.74 134.74 97.1 96.65 81.7 78.85 116.56 73.08 84.79 60.96

102.49 99.44 113.43 93.81 121.21 98.81 102.55 126.61 126.07 118.48 100.17 129.39 114.41 106.56 131.38 109.08 115.74 91.17 112.35 119.89 75.93 207.19 94.51 99.23 106.63 69.28 107.42 122.96 109.54 112.1 144.11 94.26 91.33 89.56 83 124.5 79.62 89.83 63.08

133

MEASURING URBAN SPRAWL AND VALIDATING SPRAWL MEASURES

45039 45041 45045 45051 45055 45059 45063 45077 45079 45083 45085 45091 46083 46093 46099 46103 47001 47009 47011 47019 47021 47023 47037 47043 47047 47057 47063 47065 47073 47089 47093 47105 47111 47113 47115 47125 47147 47149 47153

Fairfield County, SC Florence County, SC Greenville County, SC Horry County, SC Kershaw County, SC Laurens County, SC Lexington County, SC Pickens County, SC Richland County, SC Spartanburg County, SC Sumter County, SC York County, SC Lincoln County, SD Meade County, SD Minnehaha County, SD Pennington County, SD Anderson County, TN Blount County, TN Bradley County, TN Carter County, TN Cheatham County, TN Chester County, TN Davidson County, TN Dickson County, TN Fayette County, TN Grainger County, TN Hamblen County, TN Hamilton County, TN Hawkins County, TN Jefferson County, TN Knox County, TN Loudon County, TN Macon County, TN Madison County, TN Marion County, TN Montgomery County, TN Robertson County, TN Rutherford County, TN Sequatchie County, TN

89.55 96.07 98.68 94.78 90.43 89.91 94.92 92.45 101.53 93.37 93.59 95.01 92.75 89.23 102.86 96.18 92.32 94.52 94.75 93.3 93.65 91.73 104.68 91.19 89.34 89.49 95.73 98.48 90.78 91.49 99.46 90.6 90.08 95.08 89.77 97.02 91.68 97.98 90.25

49.53 90.47 106.59 90.85 61.7 59.53 94.04 92.02 109.51 97.98 86.69 95.83 107.03 75.07 120.06 101.49 81.1 79.63 85.38 77.41 56.61 79.08 111.86 65.43 50.43 45.66 85 101.33 69.01 63.63 102.38 74.46 45.11 104.99 69.94 80.87 72.06 90.6 76.45

76.12 109.63 100.39 112.78 129.24 87.21 88 97.27 144.33 112.28 119.72 94.28 82.73 81.4 105.9 117.26 121.37 87.08 114.48 129.08 86.41 69.11 121.78 90.57 89.51 74.08 142.29 119.36 90.1 91.38 136.24 83.62 73.25 108.51 73.16 113.11 85.62 108.29 78.98

74.02 83.71 91.07 101.88 61.49 79.89 80.44 82.26 110.91 90.54 90.32 80.22 77.53 103.16 107.25 95.04 89.51 89.16 87.22 96.48 61.81 55.42 111.57 73.7 51.46 70.51 95.5 103.4 81.51 79.72 96.83 96.59 47.03 91.26 87.72 75.99 63.1 83.25 57.33

65 93.64 98.97 100.09 81.95 73.63 86.54 88.63 120.94 98.16 96.94 89.05 87.38 83.84 111.4 103.15 95.04 84.33 94.26 98.82 67.92 66.93 115.76 75.01 62.32 62.01 105.85 107.13 78.33 76.69 111.03 82.71 54.34 99.95 74.91 89.57 72.35 93.72 69.36

134

MEASURING URBAN SPRAWL AND VALIDATING SPRAWL MEASURES

47157 47159 47163 47165 47167 47169 47171 47173 47179 47187 47189 48007 48013 48015 48019 48021 48027 48029 48037 48039 48041 48051 48055 48057 48061 48071 48077 48085 48091 48099 48113 48119 48121 48135 48139 48141 48157 48167 48181

Shelby County, TN Smith County, TN Sullivan County, TN Sumner County, TN Tipton County, TN Trousdale County, TN Unicoi County, TN Union County, TN Washington County, TN Williamson County, TN Wilson County, TN Aransas County, TX Atascosa County, TX Austin County, TX Bandera County, TX Bastrop County, TX Bell County, TX Bexar County, TX Bowie County, TX Brazoria County, TX Brazos County, TX Burleson County, TX Caldwell County, TX Calhoun County, TX Cameron County, TX Chambers County, TX Clay County, TX Collin County, TX Comal County, TX Coryell County, TX Dallas County, TX Delta County, TX Denton County, TX Ector County, TX Ellis County, TX El Paso County, TX Fort Bend County, TX Galveston County, TX Grayson County, TX

105.33 90.53 93.76 97.36 92.75 90.52 94.94 89.52 94.93 97 93.71 91.9 89.05 88.89 89.19 89.76 99.9 107.69 93.73 96.54 105.72 89.32 89.63 97.89 100.34 88.91 88.03 106.24 93.66 97.23 116.03 88.85 104.96 101.41 92.65 109.16 104.19 100.94 93.05

109.94 70.87 86.37 86.46 59.76 71.81 90.3 50.58 91.12 85.43 71.92 104.27 79.5 64.78 38.15 76.25 110.3 116.02 106.36 96.26 112.86 100.91 89.32 104.62 102.76 43.66 67.28 114.06 86.53 77.14 123.21 80.3 107.37 123.37 86.97 113.33 96.2 113.67 103.96

122.61 66.08 119.66 115.6 87.84 67.37 80.78 82.78 94.03 133.03 85.24 84.03 85.77 86.07 69.25 87.26 106.9 115.57 80.75 92.15 101.13 77.93 84.6 74.17 87.93 75.63 76.56 85.45 108.62 87.93 125.52 68.73 91.25 112.23 84.65 102.45 101.96 106.27 92.14

114.9 83.13 101.34 76.15 64.39 64.82 113.03 73.69 93.77 87.19 70.33 122.27 94.63 82.34 101.83 96.1 110.75 118.94 99.24 97.38 110.13 109.68 100.93 145.39 110.32 77.45 111.02 118.59 88.26 86.13 139.21 127.14 114.16 111.89 100.12 125.22 111.59 130.51 102.59

116.68 71.76 100.36 92.28 69.9 66.68 93.38 67.32 91.74 100.84 75.1 100.78 83.87 75.38 67.91 84.01 108.8 118.4 93.71 94.42 109.43 93 88.78 106.98 100.42 63.87 81.95 107.69 92.76 83.7 132.85 88.95 105.61 115.45 88.75 115.85 104.41 116.24 97.39

135

MEASURING URBAN SPRAWL AND VALIDATING SPRAWL MEASURES

48183 48187 48199 48201 48209 48215 48231 48245 48251 48257 48259 48281 48291 48303 48309 48325 48329 48339 48355 48361 48367 48375 48381 48397 48401 48409 48423 48439 48451 48453 48459 48469 48473 48479 48485 48491 48493 48497 49005

Gregg County, TX Guadalupe County, TX Hardin County, TX Harris County, TX Hays County, TX Hidalgo County, TX Hunt County, TX Jefferson County, TX Johnson County, TX Kaufman County, TX Kendall County, TX Lampasas County, TX Liberty County, TX Lubbock County, TX McLennan County, TX Medina County, TX Midland County, TX Montgomery County, TX Nueces County, TX Orange County, TX Parker County, TX Potter County, TX Randall County, TX Rockwall County, TX Rusk County, TX San Patricio County, TX Smith County, TX Tarrant County, TX Tom Green County, TX Travis County, TX Upshur County, TX Victoria County, TX Waller County, TX Webb County, TX Wichita County, TX Williamson County, TX Wilson County, TX Wise County, TX Cache County, UT

96.1 96.53 89.38 112.9 95.58 100.21 91.85 99.99 94.62 91.56 94.46 89.18 89.41 101.82 96.64 88.53 103.45 95.68 104.85 90.28 90.72 101.4 101.51 97.13 89.28 93.48 95.5 108.94 97.73 108.45 90.15 103.1 95.59 101.78 98.04 101.28 89.22 89.07 100.03

114.14 93.38 75.62 122.96 87.83 101.69 76.8 118.66 85 77.63 97.53 74.92 54.79 123.12 112.13 55.51 123.85 87.52 127.12 87.97 77.89 118.2 122.09 97.42 80.54 114.78 100.31 119.35 119.81 120.81 67.18 120.55 60.29 122.77 121.94 106.24 46.7 68.46 120.88

103.02 84.13 84.66 115.12 131.77 104.76 100.17 127.39 88.74 83.06 79.63 86.25 90.7 97.75 100.28 85.3 110.9 111.61 106.59 84.52 87.88 99.33 78.97 79.27 82.05 84.07 119.02 100.17 103.96 148.98 79.57 119.38 82.16 102.69 121.17 98.74 88.44 80.23 128.98

99.15 94.73 83.17 138.63 84.13 109.1 94.77 137.42 91.72 108.05 72.72 95.76 83.18 110.77 109.99 81.66 119.62 84.05 121.3 104.13 79 132.71 110.72 94.18 67.69 111.29 100.6 128.9 106.9 110.66 86.71 119.7 92.14 121.89 110.29 101.69 72.24 80.04 82.21

103.92 90.14 78.78 128.31 99.78 104.98 88.5 126.37 87.39 87.46 82.42 82.98 74.12 110.57 106.02 71.88 118.27 93.32 118.91 89.54 79.62 116.32 104.2 89.89 74.59 101.14 104.88 118.12 108.97 128.09 75.86 119.82 77.94 115.53 116.25 102.51 67.33 74.03 110.14

136

MEASURING URBAN SPRAWL AND VALIDATING SPRAWL MEASURES

49011 49023 49035 49043 49045 49049 49053 49057 50007 50011 50013 51003 51009 51011 51013 51019 51023 51031 51033 51041 51043 51053 51059 51061 51065 51067 51069 51073 51075 51079 51085 51087 51093 51095 51101 51107 51115 51121 51127

Davis County, UT Juab County, UT Salt Lake County, UT Summit County, UT Tooele County, UT Utah County, UT Washington County, UT Weber County, UT Chittenden County, VT Franklin County, VT Grand Isle County, VT Albemarle County, VA Amherst County, VA Appomattox County, VA Arlington County, VA Bedford County, VA Botetourt County, VA Campbell County, VA Caroline County, VA Chesterfield County, VA Clarke County, VA Dinwiddie County, VA Fairfax County, VA Fauquier County, VA Fluvanna County, VA Franklin County, VA Frederick County, VA Gloucester County, VA Goochland County, VA Greene County, VA Hanover County, VA Henrico County, VA Isle of Wight County, VA James City County, VA King William County, VA Loudoun County, VA Mathews County, VA Montgomery County, VA New Kent County, VA

103.45 88.62 112.04 90.7 97.75 108.21 95.06 105.74 101.56 92.87 89.13 95.3 89.69 89.68 174.41 89.97 89.85 91.88 89.04 100.63 89.87 90.02 117.83 90.61 92.01 91.3 93.79 92.66 90.23 90.55 94.37 105.97 90.76 93.7 90.95 102.68 92.2 95.29 89.75

125.21 93.3 129.1 90.55 102.75 127.19 98.96 124.44 121.65 95.99 86.07 102.67 70.62 39.87 153.2 55.41 72 77.31 40.8 98.15 79.72 49.1 123.7 73.98 71.24 47.21 81.33 69.24 55.11 59.72 84.41 114.27 75.64 97.02 56.69 116.85 52.08 95.57 43.95

80.47 78.14 106.26 91.28 79.12 89.82 84.85 97.16 152.59 82.45 69.37 87.34 84.6 90.05 95.54 91.02 83.63 83.38 74.87 114.36 79.01 78.23 113.17 90.24 75.82 88.85 87.14 89.69 75.26 70.1 82.56 86.41 77.65 79.6 79.27 81.49 72.32 85.4 80.36

105.19 83.59 116.3 75.6 75.88 106.36 91.6 108.01 89.97 75.67 90.87 78.58 75.08 58.37 177.13 73.51 88.06 109.02 77.09 102.77 86.65 71.08 114.82 80.5 69.22 77.48 85.85 99.14 78.66 78.44 88.35 123.03 79.82 106.28 102.1 113.55 78.22 102.18 72.4

104.52 82.2 120.12 83.61 85.94 109.98 90.67 111.17 120.78 83.25 79.6 88.59 74.72 61.45 163.28 71.54 79 87.87 62.65 105.03 79.55 64.75 121.96 79.57 71.02 69.94 83.61 84.43 68.17 68.03 84.1 109.38 75.95 92.61 77.57 104.6 66.77 93.19 64.13

137

MEASURING URBAN SPRAWL AND VALIDATING SPRAWL MEASURES

51143 51145 51149 51153 51155 51161 51165 51169 51177 51179 51183 51187 51191 51199 51510 51515 51520 51540 51550 51570 51590 51600 51610 51630 51650 51660 51670 51680 51683 51685 51700 51710 51730 51735 51740 51750 51760 51770 51775

Pittsylvania County, VA Powhatan County, VA Prince George County, VA Prince William County, VA Pulaski County, VA Roanoke County, VA Rockingham County, VA Scott County, VA Spotsylvania County, VA Stafford County, VA Sussex County, VA Warren County, VA Washington County, VA York County, VA Alexandria city, VA Bedford city, VA Bristol city, VA Charlottesville city, VA Chesapeake city, VA Colonial Heights city, VA Danville city, VA Fairfax city, VA Falls Church city, VA Fredericksburg city, VA Hampton city, VA Harrisonburg city, VA Hopewell city, VA Lynchburg city, VA Manassas city, VA Manassas Park city, VA Newport News city, VA Norfolk city, VA Petersburg city, VA Poquoson city, VA Portsmouth city, VA Radford city, VA Richmond city, VA Roanoke city, VA Salem city, VA

89.61 94.07 90.96 106.28 91.55 96.03 90.09 89.25 97.94 98.78 102.08 93.5 90.49 97.29 176.94 94.78 105 128.8 103.4 108.95 99.84 116.97 127.12 120.16 110.55 122.83 112.29 104.8 115.54 129.66 112.21 129.98 101.48 97.09 111.16 105.79 120.46 109.84 107.3

42.72 44.51 66.68 106.57 84.58 110.04 73.51 50.38 84.86 84.11 63.8 92.21 77.47 99 154.32 123.63 130.6 148.33 108.24 135.66 126.2 152.84 177.53 145.13 123.19 143.99 124.58 130.42 140.36 128.88 121.94 131.46 127 105.92 129.35 135.4 133.06 129.71 128.88

80.8 74.52 75.53 94.52 83.02 80.69 86.01 78.01 88.47 81.07

66.85 65.38 81.97 115.14 103.9 98.89 88.97 92.28 92.55 88.85

62.08 61.61 73.19 107.11 88.33 95.46 80.6 71.54 88.57 85.09

88.78 81.92 86.14 115.16 72.04 82.35 210.83 88.28 77.65 121.82 73 72.72 97.72 114.92 144.42 79.39 104.85 76.57 82.19 86.53 210.96 104.35 77.55 88.86 81.24 160.69 120.97 76.93

94.07 90.19 108.5 173.76 113.62 145.26 152.37 109.52 153.6 120.33 131.05 164.07 154.28 150.96 131.8 185.81 132.31 150.36 133.5 137.18 179.44 144.23 104.32 163.76 156.21 172.23 155.62 140.41

90.07 81.06 97.13 169.56 101.29 119.97 175.93 102.98 123.97 121.54 123.34 144.69 137.06 131.48 145.19 132.25 122.87 126.17 123.45 118.28 179.57 124.34 95.22 129.42 124.84 158.9 136.69 116.91

138

MEASURING URBAN SPRAWL AND VALIDATING SPRAWL MEASURES

51800 51810 51830 51840 53003 53005 53007 53011 53015 53017 53021 53033 53035 53053 53057 53061 53063 53067 53073 53077 54003 54005 54009 54011 54029 54037 54039 54051 54057 54061 54065 54069 54077 54079 54099 54107 55009 55015 55017

Suffolk city, VA Virginia Beach city, VA Williamsburg city, VA Winchester city, VA Asotin County, WA Benton County, WA Chelan County, WA Clark County, WA Cowlitz County, WA Douglas County, WA Franklin County, WA King County, WA Kitsap County, WA Pierce County, WA Skagit County, WA Snohomish County, WA Spokane County, WA Thurston County, WA Whatcom County, WA Yakima County, WA Berkeley County, WV Boone County, WV Brooke County, WV Cabell County, WV Hancock County, WV Jefferson County, WV Kanawha County, WV Marshall County, WV Mineral County, WV Monongalia County, WV Morgan County, WV Ohio County, WV Preston County, WV Putnam County, WV Wayne County, WV Wood County, WV Brown County, WI Calumet County, WI Chippewa County, WI

95.77 111.75 108.92 114.03 106.62 98.56 97.97 102.63 96.07 103.94 101.59 114.85 98.92 103.02 96.68 103.47 101.37 97.83 95.83 98.64 94.85 90.83 91.02 98.52 94.13 91.79 96.1 92.36 90.81 98.42 89.5 95.76 88.93 93.37 93.73 96.66 99.46 94.95 92.19

99.14 123.1 118.37 135.13 134.33 118.73 126.31 123.4 103.4 116.98 119.22 128.93 107.82 117.02 112.71 116.86 122.39 103.71 110.62 124.46 90.23 61.03 93.32 112.81 110.72 75.67 108.14 89.16 75.55 117.16 67.7 115.77 44.98 87.87 81.82 116.84 115.4 80.84 85.15

103.14 86.61 158.9 133.91 77 109.61 120.3 89.55 128.01 82.17 82.23 159.34 115.62 126.32 101.76 122.73 122.32 132.9 115.26 128.18 97.7 87.28 183.48 86.79 87.64 147.64 137.78 159.67 120.1 90 150.91 90.63 78.21 84.99 107.75 101.3 87.75 89.4

98.02 137.93 136.03 150.19 134.97 97.28 99.04 105.28 99 91.3 111.14 131.7 96.04 119.43 99.87 100.03 127.12 95.16 99 89.38 94.03 123.52 116.81 119.12 118.07 98.81 125.6 120.37 111.67 115.01 74.66 129.79 80.67 99.34 106.16 121.08 91.01 80.59 88.5

98.76 118.77 138.61 142.1 116.72 107.64 113.78 106.59 108.37 98.23 104.48 142.6 105.81 120.78 103.48 113.62 123.13 109.35 106.54 112.84 92.67 96.34 135.99 103.07 85.44 124.48 112.53 111.91 116.02 75.31 129.14 70.06 86.98 89.48 113.37 102.26 82.35 85.86

139

MEASURING URBAN SPRAWL AND VALIDATING SPRAWL MEASURES

55021 55025 55031 55035 55039 55049 55059 55061 55063 55073 55079 55083 55087 55089 55093 55101 55105 55109 55117 55131 55133 55139 56021

Columbia County, WI Dane County, WI Douglas County, WI Eau Claire County, WI Fond du Lac County, WI Iowa County, WI Kenosha County, WI Kewaunee County, WI La Crosse County, WI Marathon County, WI Milwaukee County, WI Oconto County, WI Outagamie County, WI Ozaukee County, WI Pierce County, WI Racine County, WI Rock County, WI St. Croix County, WI Sheboygan County, WI Washington County, WI Waukesha County, WI Winnebago County, WI Laramie County, WY

90.01 106.96 95.01 98.55 95.54 89.19 100.8 92.15 98.49 94.14 128.75 88.82 99.06 95.11 94.38 100.48 97.51 92.02 97.6 94.74 96.89 100.65 100.71

92.46 126.2 99.68 115.5 109.78 78 119.03 103.67 119.38 102.58 139.35 49.35 120.79 116.53 92.07 122.63 113.9 87.72 115.59 96.05 112.13 118.29 112.98

87.63 153.67 81.91 116.85 153.06 83.48 123.52 77.23 88.95 121.29 178.96 77.77 164.21 106.77 143.31 111.62 108.04 93.45 94.01 128.67 147.79 97.48 132.64

90.9 106.96 108.53 96.62 94.09 83.09 118.9 79.49 117.4 83.21 155.69 66.91 97.96 87.76 81.67 107.68 98.59 67.27 98.77 75.35 101.06 113.49 114.68

87.68 129.63 95.3 108.7 116.57 79.07 119.67 85.01 107.65 100.38 164.06 62.99 125.91 101.95 103.61 113.4 105.7 81.19 101.88 98.36 118.28 109.45 119.28

140

MEASURING URBAN SPRAWL AND VALIDATING SPRAWL MEASURES

Appendix C. 2010 Metropolitan Indices MSAc Code Geoid Name 10420 10420 Akron, OH MSA Albany-Schenectady-Troy, NY 10580 10580 MSA 10740 10740 Albuquerque, NM MSA Allentown-Bethlehem-Easton, 10900 10900 PA-NJ MSA 11100 11100 Amarillo, TX MSA 11460 11460 Ann Arbor, MI MSA 11540 11540 Appleton, WI MSA 11700 11700 Asheville, NC MSA Atlanta-Sandy Springs12060 12060 Marietta, GA MSA Atlantic City-Hammonton, NJ 12100 12100 MSA Augusta-Richmond County, GA12260 12260 SC MSA Austin-Round Rock-San 12420 12420 Marcos, TX MSA 12540 12540 Bakersfield-Delano, CA MSA 12580 12580 Baltimore-Towson, MD MSA 12940 12940 Baton Rouge, LA MSA 13140 13140 Beaumont-Port Arthur, TX MSA 13380 13380 Bellingham, WA MSA 13780 13780 Binghamton, NY MSA 13820 13820 Birmingham-Hoover, AL MSA 14260 14260 Boise City-Nampa, ID MSA 14500 14500 Boulder, CO MSA 14740 14740 Bremerton-Silverdale, WA MSA Bridgeport-Stamford-Norwalk, 14860 14860 CT MSA 15180 15180 Brownsville-Harlingen, TX MSA 15380 15380 Buffalo-Niagara Falls, NY MSA Burlington-South Burlington, 15540 15540 VT MSA 15940 15940 Canton-Massillon, OH MSA 15980 15980 Cape Coral-Fort Myers, FL MSA 16300 16300 Cedar Rapids, IA MSA 16580 16580 Champaign-Urbana, IL MSA

Lsad density 10 factor M1 94.55

mix centering factor factor 113.13 90.69

street composite factor index 106.81 103.15

M1 M1

95.40 103.60

105.96 102.57

108.19 99.36

86.04 97.51

95.12 98.07

M1 M1 M1 M1 M1

98.76 96.16 103.27 90.65 80.71

128.59 109.27 105.04 99.81 64.12

101.10 76.98 123.11 156.72 97.61

135.97 91.56 89.95 79.92 88.53

124.40 107.49 122.76 132.69 76.52

M1

97.80

85.47

89.89

75.92

40.99

M1

96.33

100.10

154.52

130.71

150.36

M1

85.25

60.69

88.47

73.85

59.18

M1 M1 M1 M1 M1 M1 M1 M1 M1 M1 M1

100.42 101.29 115.97 91.27 85.37 85.29 89.70 86.67 95.80 106.89 90.48

99.66 114.13 123.21 72.03 88.45 92.75 88.92 67.88 110.45 115.32 87.55

138.78 76.82 123.12 69.74 112.62 113.43 102.07 99.52 75.15 100.09 112.87

102.88 73.14 136.35 80.40 113.76 96.89 69.84 105.21 91.88 118.95 86.20

102.44 81.78 115.62 55.60 111.54 118.01 95.97 73.55 91.06 133.68 108.86

M1 M1 M1

110.63 90.92 107.94

132.86 77.74 127.67

118.02 51.43 102.46

100.81 105.96 95.10

121.64 74.69 106.36

M1 M1 M1 M1 M1

88.32 90.54 91.87 92.94 100.00

102.21 106.64 81.41 105.64 123.27

168.79 76.45 91.52 104.67 153.64

70.68 117.92 126.34 81.25 82.81

135.06 106.99 99.22 111.81 145.16

141

MEASURING URBAN SPRAWL AND VALIDATING SPRAWL MEASURES

16620 16700 16740 16820 16860 17020 17140 17300 17460 17780 17820 17900 17980 18140 18580 19340 19380 19660 19740 19780 20260 20500 21340 21500 21660 21780 22020 22180 22220 22420 22500 22660

16620 Charleston, WV MSA Charleston-North Charleston16700 Summerville, SC MSA Charlotte-Gastonia-Rock Hill, 16740 NC-SC MSA 16820 Charlottesville, VA MSA 16860 Chattanooga, TN-GA MSA 17020 Chico, CA MSA Cincinnati-Middletown, OH-KY17140 IN MSA 17300 Clarksville, TN-KY MSA Cleveland-Elyria-Mentor, OH 17460 MSA 17780 College Station-Bryan, TX MSA 17820 Colorado Springs, CO MSA 17900 Columbia, SC MSA 17980 Columbus, GA-AL MSA 18140 Columbus, OH MSA 18580 Corpus Christi, TX MSA Davenport-Moline-Rock Island, 19340 IA-IL MSA 19380 Dayton, OH MSA Deltona-Daytona Beach19660 Ormond Beach, FL MSA Denver-Aurora-Broomfield, CO 19740 MSA Des Moines-West Des Moines, 19780 IA MSA 20260 Duluth, MN-WI MSA 20500 Durham-Chapel Hill, NC MSA 21340 El Paso, TX MSA 21500 Erie, PA MSA 21660 Eugene-Springfield, OR MSA 21780 Evansville, IN-KY MSA 22020 Fargo, ND-MN MSA 22180 Fayetteville, NC MSA Fayetteville-Springdale-Rogers, 22220 AR-MO MSA 22420 Flint, MI MSA 22500 Florence, SC MSA 22660 Fort Collins-Loveland, CO MSA

M1

83.81

67.01

136.80

112.05

115.68

M1

95.29

89.19

108.94

99.03

98.53

M1 M1 M1 M1

94.55 91.16 86.14 91.18

84.71 86.08 61.15 114.46

103.05 141.81 94.27 88.79

86.93 71.77 72.90 79.93

70.45 119.08 63.63 109.94

M1 M1

98.75 84.48

107.80 39.67

98.95 74.47

93.67 60.83

80.75 41.49

M1 M1 M1 M1 M1 M1 M1

105.11 102.49 102.94 89.63 94.45 101.58 98.68

123.72 94.65 108.37 69.14 84.78 112.24 118.31

95.54 91.03 75.94 108.38 125.19 95.56 90.15

84.96 91.47 121.76 66.63 77.79 112.19 110.41

85.62 111.72 106.33 67.45 108.38 93.00 117.29

M1 M1

91.78 93.65

121.21 114.40

70.03 95.13

102.95 105.55

105.59 101.48

M1

91.35

88.02

66.48

116.35

89.68

M1

118.31

119.44

109.11

125.16

107.10

M1 M1 M1 M1 M1 M1 M1 M1 M1

97.68 85.24 91.59 114.90 97.73 95.35 91.57 99.18 91.13

120.63 89.56 74.84 99.42 130.61 125.70 92.59 118.65 71.69

99.46 117.03 80.27 73.41 113.69 116.84 86.07 106.96 72.57

82.83 77.22 84.98 128.66 88.92 91.29 84.34 73.56 71.77

104.90 103.14 73.84 105.64 130.39 125.63 91.67 121.82 66.02

M1 M1 M1 M1

84.55 89.57 81.22 94.53

67.95 90.58 51.13 106.30

80.67 114.82 87.85 96.44

81.81 97.49 61.44 100.59

66.26 106.48 61.06 115.15

142

MEASURING URBAN SPRAWL AND VALIDATING SPRAWL MEASURES

22900 23060 23420 23540

22900 23060 23420 23540

24340 24540 24580

24340 24540 24580

24660

24660

24860 25060

24860 25060

25180 25420

25180 25420

25540

25540

25860 26100

25860 26100

26380

26380

26420

26420

26580 26620 26900 27140 27260 28020 28140

26580 26620 26900 27140 27260 28020 28140

28420

28420

28660

28660

28700 28940 29140 29180

28700 28940 29140 29180

Fort Smith, AR-OK MSA Fort Wayne, IN MSA Fresno, CA MSA Gainesville, FL MSA Grand Rapids-Wyoming, MI MSA Greeley, CO MSA Green Bay, WI MSA Greensboro-High Point, NC MSA Greenville-Mauldin-Easley, SC MSA Gulfport-Biloxi, MS MSA Hagerstown-Martinsburg, MDWV MSA Harrisburg-Carlisle, PA MSA Hartford-West Hartford-East Hartford, CT MSA Hickory-Lenoir-Morganton, NC MSA Holland-Grand Haven, MI MSA Houma-Bayou Cane-Thibodaux, LA MSA Houston-Sugar Land-Baytown, TX MSA Huntington-Ashland, WV-KYOH MSA Huntsville, AL MSA Indianapolis-Carmel, IN MSA Jackson, MS MSA Jacksonville, FL MSA Kalamazoo-Portage, MI MSA Kansas City, MO-KS MSA Kennewick-Pasco-Richland, WA MSA Killeen-Temple-Fort Hood, TX MSA Kingsport-Bristol-Bristol, TN-VA MSA Knoxville, TN MSA Lafayette, IN MSA Lafayette, LA MSA

M1 M1 M1 M1

80.74 92.42 101.75 94.58

56.78 93.70 126.18 87.63

75.30 89.90 81.45 102.79

86.02 73.85 82.42 99.45

64.84 86.67 92.24 111.36

M1 M1 M1

91.39 87.33 89.90

91.78 99.05 90.49

99.15 94.05 66.77

74.75 85.82 53.34

79.18 103.61 65.35

M1

88.22

80.57

84.94

70.70

63.50

M1 M1

86.69 86.03

72.89 69.80

81.15 80.53

71.40 97.52

58.98 87.61

M1 M1

84.10 93.54

74.10 102.14

112.54 99.29

78.51 119.17

94.13 111.40

M1

100.12

113.10

119.54

72.59

93.50

M1 M1

78.64 86.45

40.46 81.52

67.00 78.64

56.95 71.71

24.86 78.17

M1

83.73

75.47

106.77

86.11

100.13

M1

108.30

102.66

92.56

129.43

76.74

M1 M1 M1 M1 M1 M1 M1

84.25 86.18 98.11 87.35 96.81 85.55 96.84

67.73 58.29 99.65 64.41 82.50 75.00 109.49

142.77 89.43 98.42 105.46 90.17 85.58 80.45

108.91 99.31 102.31 73.80 111.76 64.97 103.52

118.43 78.02 83.89 72.30 80.85 70.32 77.60

M1

92.84

108.63

81.96

85.86

105.03

M1

89.16

79.86

78.17

94.80

83.12

M1 M1 M1 M1

78.73 88.10 95.46 90.03

40.53 60.62 90.63 87.35

89.67 100.77 94.82 115.90

82.87 82.53 83.10 92.72

60.00 68.22 106.55 111.44

143

MEASURING URBAN SPRAWL AND VALIDATING SPRAWL MEASURES

29420

29420

29460 29540 29620 29700 29740 29820 30460 30700

29460 29540 29620 29700 29740 29820 30460 30700

30780 30980

30780 30980

31140 31180 31340 31420 31540 31700

31140 31180 31340 31420 31540 31700

32580 32780 32820 32900

32580 32780 32820 32900

33340

33340

33460 33660 33700 33860

33460 33660 33700 33860

34820 34940

34820 34940

34980 35300 35380

34980 35300 35380

Lake Havasu City-Kingman, AZ MSA Lakeland-Winter Haven, FL MSA Lancaster, PA MSA Lansing-East Lansing, MI MSA Laredo, TX MSA Las Cruces, NM MSA Las Vegas-Paradise, NV MSA Lexington-Fayette, KY MSA Lincoln, NE MSA Little Rock-North Little RockConway, AR MSA Longview, TX MSA Louisville/Jefferson County, KYIN MSA Lubbock, TX MSA Lynchburg, VA MSA Macon, GA MSA Madison, WI MSA Manchester-Nashua, NH MSA McAllen-Edinburg-Mission, TX MSA Medford, OR MSA Memphis, TN-MS-AR MSA Merced, CA MSA Milwaukee-Waukesha-West Allis, WI MSA Minneapolis-St. PaulBloomington, MN-WI MSA Mobile, AL MSA Modesto, CA MSA Montgomery, AL MSA Myrtle Beach-North Myrtle Beach-Conway, SC MSA Naples-Marco Island, FL MSA Nashville-Davidson-Murfreesboro--Franklin, TN MSA New Haven-Milford, CT MSA New Orleans-Metairie-Kenner,

M1

85.24

55.15

73.04

65.97

60.13

M1 M1 M1 M1 M1 M1 M1 M1

87.51 95.61 101.03 104.20 89.33 142.12 99.56 111.55

54.24 110.05 92.21 117.12 84.27 105.02 110.42 132.99

95.32 124.31 141.56 99.89 108.16 136.42 115.34 96.74

128.15 84.74 72.80 106.87 89.06 114.29 95.11 96.78

87.64 112.64 111.61 131.25 109.17 121.20 116.76 131.95

M1 M1

88.00 81.66

75.36 71.62

93.55 81.06

90.35 68.46

76.08 73.06

M1 M1 M1 M1 M1 M1

98.44 97.23 81.51 84.72 101.00 95.10

89.48 116.70 57.07 71.90 115.83 104.38

93.12 87.56 76.38 86.32 168.11 114.15

102.87 90.44 77.42 74.47 94.85 89.28

82.92 113.41 63.97 79.92 136.69 112.19

M1 M1 M1 M1

94.43 89.67 96.60 93.90

76.78 115.31 77.76 114.76

90.99 128.06 94.23 96.48

104.60 80.42 90.62 66.25

83.89 128.86 70.77 105.86

M1

113.31

126.73

153.40

130.35

134.18

M1 M1 M1 M1

105.92 92.43 109.91 90.01

110.34 88.23 140.69 85.97

111.41 78.79 62.32 98.71

108.60 112.30 102.89 80.50

88.69 97.48 113.28 91.20

M1 M1

83.43 91.57

54.95 81.95

104.88 55.19

95.40 90.69

88.70 75.23

M1 M1 M1

91.54 106.86 104.84

63.92 127.52 117.83

96.17 113.51 96.09

77.00 97.82 149.94

51.74 116.29 119.74

144

MEASURING URBAN SPRAWL AND VALIDATING SPRAWL MEASURES

35840 35980 36100 36260 36420 36500

35840 35980 36100 36260 36420 36500

36540

36540

36740

36740

37100

37100

37340

37340

37860 37900

37860 37900

38060 38300

38060 38300

38860

38860

38900 38940

38900 38940

39100 39140

39100 39140

39300 39340 39580 39740 39900 40060

39300 39340 39580 39740 39900 40060

40140 40220 40380 40420

40140 40220 40380 40420

LA MSA North Port-BradentonSarasota, FL MSA Norwich-New London, CT MSA Ocala, FL MSA Ogden-Clearfield, UT MSA Oklahoma City, OK MSA Olympia, WA MSA Omaha-Council Bluffs, NE-IA MSA Orlando-Kissimmee-Sanford, FL MSA Oxnard-Thousand OaksVentura, CA MSA Palm Bay-Melbourne-Titusville, FL MSA Pensacola-Ferry Pass-Brent, FL MSA Peoria, IL MSA Phoenix-Mesa-Glendale, AZ MSA Pittsburgh, PA MSA Portland-South PortlandBiddeford, ME MSA Portland-Vancouver-Hillsboro, OR-WA MSA Port St. Lucie, FL MSA Poughkeepsie-NewburghMiddletown, NY MSA Prescott, AZ MSA Providence-New Bedford-Fall River, RI-MA MSA Provo-Orem, UT MSA Raleigh-Cary, NC MSA Reading, PA MSA Reno-Sparks, NV MSA Richmond, VA MSA Riverside-San BernardinoOntario, CA MSA Roanoke, VA MSA Rochester, NY MSA Rockford, IL MSA

M1 M1 M1 M1 M1 M1

97.45 87.22 80.80 100.96 94.64 89.23

101.45 84.71 41.30 120.39 96.26 80.87

84.95 137.44 105.49 62.22 89.86 121.00

126.69 71.04 91.78 103.52 100.38 98.73

105.49 108.85 74.67 99.58 82.07 114.63

M1

102.64

120.53

99.67

103.54

108.42

M1

102.40

85.79

89.29

129.14

83.97

M1

107.91

133.35

78.01

118.31

113.87

M1

96.94

79.64

60.02

105.42

77.91

M1 M1

88.54 88.93

81.12 100.39

75.12 109.76

88.65 97.72

76.84 110.49

M1 M1

111.60 96.16

102.36 115.14

96.37 107.78

111.33 119.33

78.32 95.45

M1

86.06

79.09

157.47

80.24

107.72

M1 M1

111.14 92.74

136.12 77.05

100.81 62.73

124.98 106.43

109.85 80.75

M1 M1

89.38 82.33

95.38 53.19

97.49 58.15

70.30 69.96

79.51 48.96

M1 M1 M1 M1 M1 M1

105.40 104.53 96.99 102.22 100.78 96.36

83.28 123.55 87.30 121.83 93.69 78.08

112.77 77.37 109.43 129.72 137.29 101.95

141.95 100.08 88.16 113.76 94.06 92.83

104.34 108.45 84.25 137.90 120.85 76.41

M1 M1 M1 M1

103.72 90.65 96.12 94.78

111.18 85.88 103.86 110.04

77.03 83.67 96.77 91.83

80.33 93.21 62.00 107.05

56.25 93.77 74.50 114.98

145

MEASURING URBAN SPRAWL AND VALIDATING SPRAWL MEASURES

40900

40900

40980 41180 41420 41500 41620

40980 41180 41420 41500 41620

41700

41700

41740

41740

41940

41940

42020

42020

42060

42060

42100 42220 42340

42100 42220 42340

42540

42540

43340 43620

43340 43620

43780 43900 44060 44100 44180 44700 45060 45220

43780 43900 44060 44100 44180 44700 45060 45220

45300 45780 45820 45940 46060

45300 45780 45820 45940 46060

Sacramento--Arden-Arcade-Roseville, CA MSA Saginaw-Saginaw Township North, MI MSA St. Louis, MO-IL MSA Salem, OR MSA Salinas, CA MSA Salt Lake City, UT MSA San Antonio-New Braunfels, TX MSA San Diego-Carlsbad-San Marcos, CA MSA San Jose-Sunnyvale-Santa Clara, CA MSA San Luis Obispo-Paso Robles, CA MSA Santa Barbara-Santa MariaGoleta, CA MSA Santa Cruz-Watsonville, CA MSA Santa Rosa-Petaluma, CA MSA Savannah, GA MSA Scranton--Wilkes-Barre, PA MSA Shreveport-Bossier City, LA MSA Sioux Falls, SD MSA South Bend-Mishawaka, IN-MI MSA Spartanburg, SC MSA Spokane, WA MSA Springfield, IL MSA Springfield, MO MSA Stockton, CA MSA Syracuse, NY MSA Tallahassee, FL MSA Tampa-St. PetersburgClearwater, FL MSA Toledo, OH MSA Topeka, KS MSA Trenton-Ewing, NJ MSA Tucson, AZ MSA

M1

111.65

119.11

104.19

108.92

99.27

M1 M1 M1 M1 M1

86.77 97.68 93.11 101.65 117.77

93.77 108.29 123.48 116.00 125.49

110.97 93.86 113.50 102.94 93.32

93.62 113.80 98.10 90.70 97.63

116.62 82.06 123.35 115.19 106.96

M1

100.67

93.56

95.15

102.43

77.37

M1

125.08

130.37

100.90

119.95

105.18

M1

149.50

148.76

86.80

131.45

128.76

M1

89.90

119.80

103.87

88.53

118.90

M1

112.28

148.85

109.48

122.05

146.59

M1 M1 M1

98.88 93.70 90.08

146.15 132.31 84.94

107.90 91.91 115.36

112.18 96.82 115.03

145.02 113.92 115.81

M1

91.28

116.46

95.07

123.01

115.84

M1 M1

87.79 97.68

76.94 104.85

72.39 95.96

84.53 60.16

72.63 101.75

M1 M1 M1 M1 M1 M1 M1 M1

90.94 81.26 98.98 90.39 89.10 106.54 94.75 91.64

94.08 68.26 115.82 100.51 89.25 135.75 100.93 68.25

111.91 91.26 108.57 160.03 75.99 82.11 122.57 130.77

118.68 72.48 128.26 96.74 91.87 121.04 69.91 79.80

121.71 74.00 129.40 142.24 83.96 120.28 96.65 98.95

M1 M1 M1 M1 M1

105.18 95.30 88.98 115.88 100.79

105.35 120.34 83.12 128.00 90.96

93.00 85.46 102.18 97.36 78.71

150.09 95.85 71.38 139.06 94.72

98.49 100.90 94.82 144.71 78.92

146

MEASURING URBAN SPRAWL AND VALIDATING SPRAWL MEASURES

46140 46220 46340 46540 46700

46140 46220 46340 46540 46700

47260 47300 47380 48620 48900 49180 49420 49620

47260 47300 47380 48620 48900 49180 49420 49620

49660 1698016974 1698023844

49660 16980 16980

1698029404 1910019124 1910023104

16980 19100 19100

1982019804

19820

1982047644

19820

3110031084

31100

3110042044

31100

3310022744

33100

3310033124

33100

3310048424 3562020764 3562035004 3562035084

33100 35620 35620 35620

3562035644

35620

Tulsa, OK MSA Tuscaloosa, AL MSA Tyler, TX MSA Utica-Rome, NY MSA Vallejo-Fairfield, CA MSA Virginia Beach-NorfolkNewport News, VA-NC MSA Visalia-Porterville, CA MSA Waco, TX MSA Wichita, KS MSA Wilmington, NC MSA Winston-Salem, NC MSA Yakima, WA MSA York-Hanover, PA MSA Youngstown-WarrenBoardman, OH-PA MSA Chicago-Joliet-Naperville, IL MD Gary, IN MD Lake County-Kenosha County, IL-WI MD Dallas-Plano-Irving, TX MD Fort Worth-Arlington, TX MD Detroit-Livonia-Dearborn, MI MD Warren-Troy-Farmington Hills, MI MD Los Angeles-Long BeachGlendale, CA MD Santa Ana-Anaheim-Irvine, CA MD Fort Lauderdale-Pompano Beach-Deerfield Beach, FL MD Miami-Miami Beach-Kendall, FL MD West Palm Beach-Boca RatonBoynton Beach, FL MD Edison-New Brunswick, NJ MD Nassau-Suffolk, NY MD Newark-Union, NJ-PA MD New York-White Plains-Wayne, NY-NJ MD

M1 M1 M1 M1 M1

90.54 85.85 85.76 90.87 105.38

92.40 68.60 72.48 83.53 132.03

93.54 154.72 122.62 98.35 79.32

103.35 92.03 93.19 61.91 115.90

86.65 122.18 110.66 84.71 124.16

M1 M1 M1 M1 M1 M1 M1 M1

106.41 91.94 87.96 95.63 85.89 86.43 90.95 90.92

105.24 106.37 96.10 107.27 73.12 68.62 117.91 95.83

102.38 79.64 100.62 88.57 83.92 87.42 133.08 113.20

131.60 83.98 107.83 83.65 84.13 68.47 65.81 90.32

104.45 91.55 117.11 91.74 77.27 63.44 123.19 105.12

M1 M3 M3

87.36 145.50 94.53

100.76 140.09 107.73

74.10 143.24 82.31

81.52 160.21 106.33

78.08 125.90 96.70

M3 M3 M3

101.65 111.46 103.71

112.39 105.90 100.89

67.78 94.21 72.55

132.08 129.74 117.21

103.10 86.15 78.56

M3

125.20

124.65

107.48

183.98

137.17

M3

97.88

110.33

70.54

96.17

67.03

M3

187.39

160.18

115.66

154.40

130.33

M3

161.91

155.02

79.64

181.81

139.86

M3

140.93

136.53

61.79

153.66

121.41

M3

160.18

136.41

117.91

166.90

144.12

M3 M3 M3 M3

110.73 109.41 123.33 126.86

121.02 125.05 144.75 139.67

69.66 69.02 81.01 90.43

118.46 137.91 155.85 113.76

98.18 96.77 117.04 109.62

M3

384.29

159.34

213.49

193.80

203.36

147

MEASURING URBAN SPRAWL AND VALIDATING SPRAWL MEASURES

3798015804 3798037964 3798048864 4186036084 4186041884 4266042644 4266045104 4790013644 4790047894

37980 Camden, NJ MD 37980 Philadelphia, PA MD 37980 Wilmington, DE-MD-NJ MD Oakland-Fremont-Hayward, CA 41860 MD San Francisco-San Mateo41860 Redwood City, CA MD Seattle-Bellevue-Everett, WA 42660 MD 42660 Tacoma, WA MD Bethesda-Rockville-Frederick, 47900 MD MD Washington-Arlington47900 Alexandria, DC-VA-MD-WV MD

M3 M3 M3

105.39 141.01 102.42

125.72 142.25 109.29

78.53 115.95 96.53

120.07 140.06 120.29

103.22 122.42 112.94

M3

136.28

145.75

88.11

159.44

127.24

M3

185.97

167.17

230.92

162.83

194.28

M3 M3

121.27 103.62

123.99 105.56

121.68 92.25

131.86 119.05

116.11 107.48

M3

115.08

123.84

98.97

118.94

114.66

M3

122.35

117.61

133.16

125.91

107.21

148

MEASURING URBAN SPRAWL AND VALIDATING SPRAWL MEASURES

Appendix D. Urbanized Areas Compactness Indices 2010 UA10 UA00 code code 199 766 970 1171 1495 2602 2683 2764 3358 3817 3898 4222 4384 4681 4843 5680 7786 8785 8974 10972 11350 13375 13510 15508 15670 15832 16264 16885 17668 18964 19099 19234 19504 19558 19755

199 766 928 1171 1495 2602 2683 2764 3358 3817 3898 4222 4384 4681 4843 5680 7786 8785 8974 10972 11350 13375 13510 15508 15670 15832 16264 16885 17668 18964 19099 19234 19504 19558 87328

UZA name Aberdeen--Bel Air South--Bel Air North, MD Akron, OH Albany--Schenectady, NY Albuquerque, NM Allentown, PA--NJ Ann Arbor, MI Antioch, CA Appleton, WI Asheville, NC Atlanta, GA Atlantic City, NJ Augusta-Richmond County, GA--SC Austin, TX Bakersfield, CA Baltimore, MD Baton Rouge, LA Birmingham, AL Boise City, ID Bonita Springs, FL Brownsville, TX Buffalo, NY Canton, OH Cape Coral, FL Charleston--North Charleston, SC Charlotte, NC--SC Chattanooga, TN--GA Chicago, IL--IN Cincinnati, OH--KY--IN Cleveland, OH Columbia, SC Columbus, GA--AL Columbus, OH Concord, CA Concord, NC Conroe--The Woodlands, TX

density mix centering street composite factor10 factor10 factor10 factor10 index10 85.49 81.39 97.77 116.44 97.96 97.99 114.41 95.15 60.41 84.64 93.87 72.48 113.28 125.2 129.32 81.92 73.46 108.78 77.33 104.71 108.69 78.14 71.37 89.82 82.95 68.92 138.66 96.17 98.46 77.26 83.9 109.73 117.87 61.76 84.06

120.77 116.43 118.87 78.03 143.39 79.81 159.21 115.28 95.23 75.63 91.07 77.69 81.33 121.55 121.02 75.3 86.42 117.41 82.83 69.61 129.82 120.31 48.77 87.42 64.56 54.18 115.95 108.85 119.6 72.43 81.28 111.69 127.75 92.03 74.6

76.74 93.24 112.62 93.5 104.15 147.32 55.47 129.16 103.73 107.29 157.06 94.35 134.13 76.44 123.1 77.21 105.98 75.99 62 60.4 93.58 79.59 102.22 117.43 115.94 97.03 146.41 108.51 95.01 117.99 109.53 106.51 88.56 63.73 90.9

77.96 89.2 89.2 122.76 137.07 63.7 116.28 109.29 77.43 36.84 143.86 84.62 86.92 116.2 122.24 77.61 112.13 117.27 76.13 113.57 79.29 119.98 108.16 97.96 53.01 70.33 132.57 70.43 56.7 80.39 85.81 101.67 108.39 68.79 55.55

96 92.2 106.98 101.29 131.35 102.94 126.73 131.07 83.12 37.45 144.25 76.28 96.11 116.85 122.49 64.38 88.06 113.63 66.52 90.72 98.81 107.69 73.12 97.6 57.41 60.96 121.64 81.34 74.58 79.72 93.81 101.64 116.23 66.05 72.27

149

MEASURING URBAN SPRAWL AND VALIDATING SPRAWL MEASURES

20287 22042 22366 22528 23500 23527 23743 23824 25228 27253 28117 28333 29440

20287 22042 22366 22528 23500 23527 23743 23824 25228 27253 28117 28333 29440

29494 30628 31087 31843 34300 34813 35164 35461 35920 37081 37243 38647 40429 40753 40780 41212 41347 42211 42346 43723 43912 44479 44992 45451 45640 46045

29494 30628 31087 31843 34300 34813 35164 35461 35920 37081 37243 38647 40429 40753 40780 41212 41347 42211 42346 43723 43912 44479 44992 45451 45640 46045

Corpus Christi, TX Dallas--Fort Worth--Arlington, TX Davenport, IA--IL Dayton, OH Denton--Lewisville, TX Denver--Aurora, CO Des Moines, IA Detroit, MI Durham, NC El Paso, TX--NM Eugene, OR Evansville, IN--KY Fayetteville, NC Fayetteville--Springdale--Rogers, AR--MO Fort Collins, CO Fort Wayne, IN Fresno, CA Grand Rapids, MI Green Bay, WI Greensboro, NC Greenville, SC Gulfport, MS Harrisburg, PA Hartford, CT Hickory, NC Houston, TX Huntington, WV--KY--OH Huntsville, AL Indianapolis, IN Indio--Cathedral City, CA Jackson, MS Jacksonville, FL Kalamazoo, MI Kansas City, MO--KS Kennewick--Pasco, WA Killeen, TX Kissimmee, FL Knoxville, TN Lafayette, LA

106.9 115.92 90.86 87.21 104.86 128.15 99.26 106.01 94.32 118.51 114.84 94.15 79.4

117.65 90.22 140.14 126.45 111.25 94.52 110 112.41 67.57 78.44 134.37 101.62 73.65

86.49 101.95 73.06 89.21 66.35 118.79 92.79 91.65 96.34 78.15 134.15 94.07 67.16

119 117.33 128.02 95.11 100.65 127.61 100.45 109.31 68.93 123.97 123.07 105.58 64.43

118.91 84.43 121.31 96.47 98.54 110.96 103.87 85.73 76.75 95.69 152.54 108.97 61.05

81.99 101.36 85.44 128.7 91.17 93.27 87.97 67.92 68.81 90.63 93.32 46.92 114.84 78.77 73.08 94.06 96.72 75.8 96.67 76.6 98.85 89.99 95.9 87.75 71.09 81.01

95.85 112.13 100.41 131.47 108.19 91.28 98.75 75.26 73.89 110.89 106.67 78.41 88.59 114.67 63.42 90.56 112.31 70.34 84.48 86.21 105.06 107.69 100.91 49.1 53.58 94.51

95.95 97.17 93.76 85.2 107.46 74.78 92.59 89.88 85.65 104.73 129.53 72.2 100.16 141.6 81.92 95.71 71.8 112 99.64 104.69 92.38 89.25 69.3 60.42 155.82 92.99

69.57 104.12 86.58 114.97 74.4 92.29 69.88 57.88 104.8 119.9 45.2 44.94 121.05 119.14 96.52 88.12 107.44 70.53 97.33 69.49 103.91 89.51 101.84 104.82 67.02 88.43

85.16 115.05 93.59 122.62 92.57 92.67 86.85 60.57 85.14 113.49 84.27 48.64 84.54 133.96 74.11 76.17 101.29 77.22 83.97 86.63 88.64 102.36 98.59 67.9 79.3 92.42

150

MEASURING URBAN SPRAWL AND VALIDATING SPRAWL MEASURES

46828 47530 47611 47719 47854 47995 49582 49933 50392

46828 47530 47611 47719 47854 47962 49582 49933 50392

51445 51755 51877 52390 53200 56116 56602 57466 57628

51445 51715 51877 52390 53200 56116 56602 57466 57628

57709 57925 58006 58600 60799 60895 61273 62407 62677 63217 64135 64945 65080 65269 65863 66673 67105

57709 57925 58006 58600 87004 60895 61273 62407 62677 63217 64135 64945 65080 65269 65863 66673 67105

67134 22636 68482 68482

Lakeland, FL Lancaster, PA Lancaster--Palmdale, CA Lansing, MI Laredo, TX Las Vegas--Henderson, NV Lexington-Fayette, KY Lincoln, NE Little Rock, AR Los Angeles--Long Beach-Anaheim, CA Louisville/Jefferson County, KY--IN Lubbock, TX McAllen, TX Madison, WI Memphis, TN--MS--AR Miami, FL Milwaukee, WI Minneapolis--St. Paul, MN--WI Mission Viejo--Lake Forest--San Clemente, CA Mobile, AL Modesto, CA Montgomery, AL Murrieta--Temecula--Menifee, CA Myrtle Beach--Socastee, SC--NC Nashville-Davidson, TN New Haven, CT New Orleans, LA New York--Newark, NY--NJ--CT Norwich--New London, CT--RI Ogden--Layton, UT Oklahoma City, OK Omaha, NE--IA Orlando, FL Oxnard, CA Palm Bay--Melbourne, FL Palm Coast--Daytona Beach--Port Orange, FL Pensacola, FL--AL

86.1 90.53 111.72 98.23 123.87 147.64 126.87 118.63 86.38

46.46 127.52 111.36 68.2 131.21 63.47 122.82 127.46 82.4

106.74 132.24 54.81 134.04 81.56 121.83 121.63 97.02 97.12

106.84 79.34 82.34 86.9 166.54 107.58 98.98 141.77 115.29

87.65 116.07 90.2 102.07 151.8 102.24 136.19 143.38 95.84

212.21 97.86 107.82 88.19 118.16 93.13 143.68 112.66 112.17

144.75 82.91 127.9 63.8 121.82 63.89 108.89 116.03 98.47

102.23 92.73 75.58 85.12 182.19 101.9 109.46 164.62 119.91

138.92 90.53 130.09 99.04 99.33 86.31 134.49 112.47 108.34

143.42 79.4 126.98 71.63 152.87 70.86 112.06 132.07 97.57

127.87 77.59 127.77 85.32 103.58 57 87.51 90.04 125.35 197.5 72.73 98.34 95.88 110.48 109.38 147.55 88.7

147.54 103.81 145.02 118.52 98.68 48.17 47.43 111.59 102.93 106.8 88.4 117.46 87.23 110.3 78.11 137.14 78.17

62.55 71.28 79.06 97.11 60.2 100.23 111.18 139.46 93.92 179.1 132.22 63.46 96.6 98.07 92.26 82.42 60.31

118.63 106.28 109.17 76.74 73.39 94.2 70.03 58.5 187.3 125.06 60.7 87.66 101.44 128.31 109.72 135.08 88.15

122.47 90.23 130 100.22 77.41 71.35 60.27 100.08 138.57 142.71 93.49 87.35 87.68 116.15 84.41 146.19 68.9

84.24 73.9

82.84 71.76

66.42 74.11

108.04 111.04

82.45 78.47

151

MEASURING URBAN SPRAWL AND VALIDATING SPRAWL MEASURES

68509 69076 69184 69697 71263 71317 71479 71803 72559 73261 73693 74179 74746 75340 75421 75664 75718

68509 69076 69184 69697 71263 71317 71479 71803 72559 73261 73693 74179 74746 75340 75421 75664 75718

76474 77068 77770 78229

76474 77068 77770 78229

78499 78580 78904 79039 79309 79606 79768 80227 80389 81739 83116 83764 83953 85087 86302 86464 86599

78499 78580 78904 79039 79309 79606 79768 80227 80389 81739 83116 83764 83953 85087 86302 86464 86599

Peoria, IL Philadelphia, PA--NJ--DE--MD Phoenix--Mesa, AZ Pittsburgh, PA Portland, ME Portland, OR--WA Port St. Lucie, FL Poughkeepsie--Newburgh, NY--NJ Provo--Orem, UT Raleigh, NC Reading, PA Reno, NV--CA Richmond, VA Riverside--San Bernardino, CA Roanoke, VA Rochester, NY Rockford, IL Round Lake Beach--McHenry-Grayslake, IL--WI Sacramento, CA St. Louis, MO--IL Salem, OR Salt Lake City--West Valley City, UT San Antonio, TX San Francisco--Oakland, CA San Jose, CA Santa Clarita, CA Sarasota--Bradenton, FL Savannah, GA Scranton, PA Seattle, WA Shreveport, LA South Bend, IN--MI Spokane, WA Springfield, MO Stockton, CA Syracuse, NY Tallahassee, FL Tampa--St. Petersburg, FL

85.82 127.16 119.2 93.7 88.48 127.64 78.65 75.26 113.81 90.27 127.71 101.13 94.09 119.16 83.87 103 86.89

104.21 124.32 79.12 119.21 123.43 129.26 57.1 112.65 130.08 77.3 150.87 59.47 83.03 112.94 108.45 101.93 103.05

125.17 131.46 99.99 125.55 148.13 107.58 76.99 121.96 77.33 112.47 124.45 123.12 111.23 81.81 81.82 103.14 96.03

113.45 105.73 106.59 117.1 85.12 135.17 103.04 43.25 100.13 54.9 147.46 94.24 109.31 82.29 110.57 61.44 119.57

120.49 109.05 80.27 109.25 130.27 126.14 71.26 84.82 110.6 68.86 169.32 95.67 93.1 84.2 105.72 85.12 109.98

80.35 125.63 100.04 115.59

83.85 104.85 114.26 125.71

79.57 109.31 104.27 112.5

90.59 107.52 110.7 103.81

81.75 106.02 96.18 133.51

130.73 113.62 205.69 181.13 120.29 84.4 82.3 95.52 118.83 80.89 79.42 97.43 86.57 126.41 100.81 97.65 103.05

116.64 85.4 129.92 136.26 129.69 94.94 96.52 145.27 89.41 74.13 91.31 109.36 110.76 131.34 110.01 71.33 92.69

84.13 92.02 164.34 86.67 81.81 93.16 110.11 102.54 142.43 70.96 105.6 103.98 68.49 98.73 133.54 144.71 93.15

99.9 104.86 153.38 127.03 93.51 115.72 111.85 133.3 110.09 102.49 129.25 141.33 115.03 117.22 85.98 84.14 122.73

105.81 85.2 180.94 139.98 119.53 93.95 109.61 135.5 104.65 79.07 110.77 125.49 101.06 134.67 116.05 109.39 87.63

152

MEASURING URBAN SPRAWL AND VALIDATING SPRAWL MEASURES

87868 88462 88732 88948 90541 90946 92242 95077 95833 96670 96697 97750 97831

87868 88462 88732 88948 90541 90946 92242 95077 95833 96670 96697 97750 97831

Toledo, OH--MI Trenton, NJ Tucson, AZ Tulsa, OK Victorville--Hesperia, CA Visalia, CA Washington, DC--VA--MD Wichita, KS Wilmington, NC Winston-Salem, NC Winter Haven, FL York, PA Youngstown, OH--PA

94.96 123.71 100 90.85 82.38 118.08 142.28 96.94 81.25 66.31 67.51 91.8 76.37

127.77 121.54 70.98 97.81 67.79 126.58 96.36 92.64 102.01 68.97 52.97 129.86 134.31

90.58 106.59 90.13 96.64 57.01 92.94 136.7 94.44 89.16 88.15 77.78 121.89 77.47

100.46 108.84 94.08 99.33 61.88 127.07 104.92 110.31 96.91 54.29 110.62 103.78 90.73

106.97 132.08 77.54 92.29 54.15 137.22 107.69 100.02 99.02 55.56 75.86 129.62 96.18

153

MEASURING URBAN SPRAWL AND VALIDATING SPRAWL MEASURES

Appendix E. Urbanized Areas Compactness Indices 2000 UA10 code

UA00 code

199 766 970 1171 1495 2602 2683 2764 3358 3817 3898 4222 4384 4681 4843 5680 7786 8785 8974 10972 11350 13375 13510 15508 15670 15832 16264 16885 17668 18964 19099 19234 19504 19558 19755

199 766 928 1171 1495 2602 2683 2764 3358 3817 3898 4222 4384 4681 4843 5680 7786 8785 8974 10972 11350 13375 13510 15508 15670 15832 16264 16885 17668 18964 19099 19234 19504 19558 87328

UZA name Aberdeen--Bel Air South--Bel Air North, MD Akron, OH Albany--Schenectady, NY Albuquerque, NM Allentown, PA--NJ Ann Arbor, MI Antioch, CA Appleton, WI Asheville, NC Atlanta, GA Atlantic City, NJ Augusta-Richmond County, GA--SC Austin, TX Bakersfield, CA Baltimore, MD Baton Rouge, LA Birmingham, AL Boise City, ID Bonita Springs, FL Brownsville, TX Buffalo, NY Canton, OH Cape Coral, FL Charleston--North Charleston, SC Charlotte, NC--SC Chattanooga, TN--GA Chicago, IL--IN Cincinnati, OH--KY--IN Cleveland, OH Columbia, SC Columbus, GA--AL Columbus, OH Concord, CA Concord, NC Conroe--The Woodlands, TX

density factor00

mix factor00

centering factor00

street factor00

composite index00

85.05 83.56 97.62 115.4 98.6 109.06 112.73 109.95 56.51 88.54 93.52 74.42 121.77 121.27 129.5 83.46 81.96 104.83 76.78 108.24 114.62 83.07 81.59 87.45 85.5 65.83 148.83 100.48 111.72 82.21 85.1 114.53 123.25 55.14 94.53

130.29 124.2 126.41 83.47 154.6 89.82 162.91 136.48 112.01 90.28 86.06 87.91 113.65 134.54 120.15 72.66 94.9 131.24 77.85 107.19 131.15 135.07 79.16 95.25 99.95 55.21 120.65 116.34 122.33 84.52 69 124.36 126.99 99.79 92.36

74.74 91.55 111.23 88.92 98.74 125.09 50.78 103.75 102.96 106.29 158.52 89.02 133.13 78.88 128.93 85.07 100.51 71.93 61.38 65.79 103.32 79.23 95.48 127.26 108.27 92.3 131.04 116.18 112.13 125.63 117.06 102.14 82.93 79.86 86.68

47.27 81.39 76.74 111.01 137.75 58.53 108.2 115.15 63.14 19.9 140.57 70.65 82.98 118.03 96.29 64.16 99.74 104.89 46.22 106.8 75.28 123 90.72 96.79 35.77 53.9 122.64 61.77 56.21 77.91 77.88 96.89 82.12 68.46 54.45

85.29 89.78 102.59 96.53 133.48 98.12 122 135.96 81.59 39.5 139.23 71.97 113.25 122.72 113.51 61.39 86.68 110.79 52.49 106.23 101.45 114.04 82.2 103.51 66.06 49.7 117.76 84.83 86.01 88.92 86.41 105.27 104.03 76.14 88.85

154

MEASURING URBAN SPRAWL AND VALIDATING SPRAWL MEASURES

20287 22042 22366 22528 23500 23527 23743 23824 25228 27253 28117 28333 29440

20287 22042 22366 22528 23500 23527 23743 23824 25228 27253 28117 28333 29440

29494 30628 31087 31843 34300 34813 35164 35461 35920 37081 37243 38647 40429 40753 40780 41212 41347 42211 42346 43723 43912 44479 44992 45451 45640 46045

29494 30628 31087 31843 34300 34813 35164 35461 35920 37081 37243 38647 40429 40753 40780 41212 41347 42211 42346 43723 43912 44479 44992 45451 45640 46045

Corpus Christi, TX Dallas--Fort Worth--Arlington, TX Davenport, IA--IL Dayton, OH Denton--Lewisville, TX Denver--Aurora, CO Des Moines, IA Detroit, MI Durham, NC El Paso, TX--NM Eugene, OR Evansville, IN--KY Fayetteville, NC Fayetteville--Springdale--Rogers, AR--MO Fort Collins, CO Fort Wayne, IN Fresno, CA Grand Rapids, MI Green Bay, WI Greensboro, NC Greenville, SC Gulfport, MS Harrisburg, PA Hartford, CT Hickory, NC Houston, TX Huntington, WV--KY--OH Huntsville, AL Indianapolis, IN Indio--Cathedral City, CA Jackson, MS Jacksonville, FL Kalamazoo, MI Kansas City, MO--KS Kennewick--Pasco, WA Killeen, TX Kissimmee, FL Knoxville, TN Lafayette, LA

105.97 117.14 96.81 89.73 97.48 135.76 111.38 113.29 91.24 118.39 121.5 96.61 78.97

125.48 102.37 152.84 123.77 116.34 108.29 121.97 112.29 77.48 84.76 141.47 124.11 98.97

84.91 95.57 73.58 101.57 67.11 116.57 97.25 106.16 106.3 79.51 130.73 99.57 62.63

104.4 98.9 114.3 88.21 75.13 129.44 101.92 100.28 58.06 111.2 114.89 100.57 56.65

113.13 81.46 120.78 96.56 86.9 120.11 115.33 89.38 78.11 93.22 151.42 116.75 64.13

86.95 105.79 91.02 131.07 94.4 99.89 92.19 75.59 77.88 90.93 93.14 49.14 115.73 82.45 73.26 97.94 99.18 86.8 100.97 82.13 101.66 84.2 99.15 85.68 68.61 78.58

121.68 111.9 114.76 145.64 120 115.98 125.19 101.55 94.44 118.89 114.07 81.34 98.06 124.09 78.59 101.45 127.03 87.99 92.98 111.47 115.2 126.72 110.93 66.13 70.74 104.29

115.49 89.91 89.81 93.2 102.39 71.74 96.01 89.54 94.46 103.03 126.66 75.33 94.99 152.46 90.56 112.38 71.49 112.18 92.56 99.11 90.55 99.02 77.07 58.9 130.5 100.28

60.4 95.01 86.8 120.39 61.32 91.68 67.38 59.56 91.98 108.09 32.36 42.67 97.43 109.9 55.71 86.6 107.22 63.13 96.26 64.66 99.55 89.04 98.45 106.74 57.65 74.01

104.38 109.69 97.94 134.15 89.35 101.39 98.07 74.82 92.1 110.9 79.58 48.76 79.22 138 67.33 88.58 108.33 84.77 85.83 92.53 90.81 111.45 105.12 76.56 71.74 93.04

155

MEASURING URBAN SPRAWL AND VALIDATING SPRAWL MEASURES

46828 47530 47611 47719 47854 47995 49582 49933 50392

46828 47530 47611 47719 47854 47962 49582 49933 50392

51445 51755 51877 52390 53200 56116 56602 57466 57628

51445 51715 51877 52390 53200 56116 56602 57466 57628

57709 57925 58006 58600 60799 60895 61273 62407 62677 63217 64135 64945 65080 65269 65863 66673 67105

57709 57925 58006 58600 87004 60895 61273 62407 62677 63217 64135 64945 65080 65269 65863 66673 67105

67134 22636 68482 68482

Lakeland, FL Lancaster, PA Lancaster--Palmdale, CA Lansing, MI Laredo, TX Las Vegas--Henderson, NV Lexington-Fayette, KY Lincoln, NE Little Rock, AR Los Angeles--Long Beach-Anaheim, CA Louisville/Jefferson County, KY--IN Lubbock, TX McAllen, TX Madison, WI Memphis, TN--MS--AR Miami, FL Milwaukee, WI Minneapolis--St. Paul, MN--WI Mission Viejo--Lake Forest--San Clemente, CA Mobile, AL Modesto, CA Montgomery, AL Murrieta--Temecula--Menifee, CA Myrtle Beach--Socastee, SC--NC Nashville-Davidson, TN New Haven, CT New Orleans, LA New York--Newark, NY--NJ--CT Norwich--New London, CT--RI Ogden--Layton, UT Oklahoma City, OK Omaha, NE--IA Orlando, FL Oxnard, CA Palm Bay--Melbourne, FL Palm Coast--Daytona Beach--Port Orange, FL Pensacola, FL--AL

76.03 96.79 109.53 101.03 134.65 155.61 132.62 118.03 93

86.84 132.28 123.6 96.55 148.02 69.07 121.28 133.12 95.64

114.04 126.42 56.21 105.33 86.2 127.05 125.89 97.15 93.19

85.81 65.11 73.56 80.06 189.55 105.4 78.65 135.15 103.44

94.03 112.02 91.06 97.71 174.12 111.38 130.01 141.19 96.64

212.14 101.73 112.3 76.58 122.06 101.44 142.94 118.7 113.41

131.99 93.98 132.03 79.91 126.86 72.29 107.92 128.61 90.31

105.37 90.76 74.66 84.37 158.37 100.26 93.37 125.45 118.69

127.52 81.59 124.18 90.18 101.3 71.3 131.01 106.94 95.92

135.59 80.16 126.23 70.76 147.2 69.71 104.22 120.5 89.25

129.04 81.05 127.86 95.52 95.58 66.75 89.26 88.44 161.24 197.18 70.09 98.24 100.17 113.56 106.07 151.09 76.29

140.04 108.53 147.59 130.65 105.7 80.03 67.83 119.87 106.84 115.6 97.16 124.92 107.78 122.98 87.13 138.91 75.93

64.39 73.69 97.76 103.01 108.31 108.57 106.22 132.78 95.97 170.57 133.84 64.85 93.5 98.8 94.04 76.66 62.16

92.13 70.94 105.06 80.25 72.89 105.93 46.1 47.06 181.06 120.19 48.33 76.93 94.11 124.72 96.83 115.75 77.64

108.57 77.49 135.64 112.96 100.43 98.74 58.11 93.54 149.64 141.75 90.23 86.84 92.8 120.65 83.39 136.4 58.18

95.16 73.65

98.84 74.45

68.39 72.66

109.08 94.85

94.85 69.79

156

MEASURING URBAN SPRAWL AND VALIDATING SPRAWL MEASURES

68509 69076 69184 69697 71263 71317 71479 71803 72559 73261 73693 74179 74746 75340 75421 75664 75718

68509 69076 69184 69697 71263 71317 71479 71803 72559 73261 73693 74179 74746 75340 75421 75664 75718

76474 77068 77770 78229

76474 77068 77770 78229

78499 78580 78904 79039 79309 79606 79768 80227 80389 81739 83116 83764 83953 85087 86302 86464 86599

78499 78580 78904 79039 79309 79606 79768 80227 80389 81739 83116 83764 83953 85087 86302 86464 86599

Peoria, IL Philadelphia, PA--NJ--DE--MD Phoenix--Mesa, AZ Pittsburgh, PA Portland, ME Portland, OR--WA Port St. Lucie, FL Poughkeepsie--Newburgh, NY--NJ Provo--Orem, UT Raleigh, NC Reading, PA Reno, NV--CA Richmond, VA Riverside--San Bernardino, CA Roanoke, VA Rochester, NY Rockford, IL Round Lake Beach--McHenry-Grayslake, IL--WI Sacramento, CA St. Louis, MO--IL Salem, OR Salt Lake City--West Valley City, UT San Antonio, TX San Francisco--Oakland, CA San Jose, CA Santa Clarita, CA Sarasota--Bradenton, FL Savannah, GA Scranton, PA Seattle, WA Shreveport, LA South Bend, IN--MI Spokane, WA Springfield, MO Stockton, CA Syracuse, NY Tallahassee, FL Tampa--St. Petersburg, FL

91.09 131.05 130.95 96.98 89.38 124.3 71.15 78.28 127.45 85.53 119.44 113.14 92.38 116.92 86.36 108.58 89.96

126.26 121.96 100.13 127.23 134.9 134.07 72.97 112.78 156.85 110.35 157.15 72 90.82 113.99 111.67 97.82 114.18

99.4 126.98 97.92 118.72 155.27 102.05 75.25 115.34 75.63 75.3 126.12 126.28 104.41 91.49 76.59 103.61 95.46

105.88 101.25 103.57 106.44 66.41 128.07 94.03 22.28 94.04 52.22 118.53 92.04 88.19 83.23 86.48 50.71 103.81

115.78 106.14 92.82 105.11 128.14 121.95 70.87 74.14 126.13 67.3 155.74 105.82 83.85 89.17 93.5 79.59 107.07

76.99 124.59 103.99 115.57

115.86 120.43 123.85 137.9

79.25 124.87 101.53 112.49

75.95 98.6 96.97 99.24

86.73 115.3 93.99 134.82

133.2 117.87 219.66 178.91 118.24 90.69 99.99 101.5 113.58 86.6 83 99.69 89.76 134.42 104.93 93.87 106.59

130.41 96.14 128.39 134.54 137.68 100.26 89.55 155.32 93.37 82.72 111.98 110.75 138.09 145.18 115.92 68.08 94.04

88.53 99.08 162.41 82.37 79.83 114.14 108.58 100.64 135.64 74.56 104.07 102.36 66.86 104.41 130.6 112.12 89.98

99.2 106.77 149.84 116.63 67.1 110.88 123.77 129.53 97.4 93.39 99.66 140.36 87.37 124.09 76.08 61.01 122.04

113.34 96.28 184.06 131.9 111.89 104.18 117.33 136.8 96.57 80.28 104.93 124.73 101 147.55 112.42 82.66 88.57

157

MEASURING URBAN SPRAWL AND VALIDATING SPRAWL MEASURES

87868 88462 88732 88948 90541 90946 92242 95077 95833 96670 96697 97750

87868 88462 88732 88948 90541 90946 92242 95077 95833 96670 96697 97750

Toledo, OH--MI Trenton, NJ Tucson, AZ Tulsa, OK Victorville--Hesperia, CA Visalia, CA Washington, DC--VA--MD Wichita, KS Wilmington, NC Winston-Salem, NC Winter Haven, FL York, PA

102.27 130.68 103.97 96.26 74.79 116.84 133.79 101.36 74.31 66.67 72.57 84.59

129.37 138.84 93.06 101.83 84.24 142.48 104.48 107.4 109.17 68.56 72.7 139.34

93.5 103.24 82.2 93.07 56.75 107.53 112.04 97.06 91.78 93.67 75.82 129.88

92.92 106.32 91.29 96.58 51.04 108.93 85.55 112.11 77.06 44.02 100.19 93.59

106.17 137.57 83.13 92.84 55.43 145.05 90.84 108.03 92.18 53.49 80.21 128.45

158