Stoch Environ Res Risk Assess DOI 10.1007/s00477-012-0671-0
ORIGINAL PAPER
Modeling urban land use conversion of Daqing City, China: a comparative analysis of ‘‘top-down’’ and ‘‘bottom-up’’ approaches Wenliang Li • Changshan Wu • Shuying Zang
Ó Springer-Verlag Berlin Heidelberg 2012
Abstract During the past decades, Daqing City, China has experienced unprecedented urban expansion due to the rapid development of petroleum industry. With rapid urbanization and lack of strategic planning, Daqing is facing many socio-economic and environmental problems, and it is essential to examine the process of urbanization, and to develop policy recommendations for sustainable development. To address this problem, this paper examined the urbanization process of Daqing City through developing two multi-level models: an integrated system dynamic (SD) and CLUE-S model (SD-CLUES), and an integrated SD and stochastic cellular automata model (SD-CA). Analysis of results suggests that these two models generate significantly different results. With the SD-CLUES model, new urban developments are clustered in the downtown area or along major transportation networks, indicating exogenous driving forces playing an important role in shaping urban spatial dynamics. With the SD-CA model, on the contrary, the resultant new urban cells are spread over the entire study area, and associated with existing urban areas. Further, visual comparisons and validations indicate that the SD-CA model is a better alternative in explaining the urbanization mechanism of Daqing City. In addition, analysis of results suggests that the stochastic factor in the CA model has significant impact on the modeling accuracy. W. Li C. Wu (&) S. Zang Key Laboratory for Remote Sensing Monitoring of Geographic Environment, College of Heilongjiang Province, Harbin Normal University, Harbin, Heilongjiang 150025, China e-mail:
[email protected] W. Li C. Wu Department of Geography, University of Wisconsin-Milwaukee, PO Box 413, Milwaukee, WI 53201, USA
Keywords Urbanization Stochastic cellular automata CLUE-S model Daqing
1 Introduction During the past 20 years, many metropolitan areas in China have experienced unprecedented expansion due to population growth and migration. Urban built-up areas in China have increased from 10,161 km2 in 1986 to 32,600 km2 in 2006, with an increment of 220.83 % (China Statistic Yearbook 2003; China Association of Mayors 2007). This high-speed urbanization is associated with the rapid growth of urban population. In particular, urban population has increased from 302 million in 1990 to 456 million in 2000, and it is projected that in 2020, *900 million Chinese people will reside in urban areas (Song and Ding 2009). Simultaneously, the percent of urban population to the total population has increased from 26 % in 1990 to 36 % in 2000, and to 50 % in 2010, and it is projected that urban population percentage will reach 65 % in 2050 (Chen et al. 2009; Song and Ding 2009). Rapid urbanization in China is associated with high speed industrialization and a phenomenal economic growth. China has maintained the fastest gross domestic production (GDP) growth rate (e.g. 9.6 % annually since 1987) in the world since the implementation of the economic reform and open-door policies. As a result of rapid economic development during these years, China was ranked as one of the four world’s largest economies with a GDP of $5.88 trillion US dollars in 2010. While rapid urbanization brought economic benefits and improved the quality of life, ill-planned urban growth also generated numerous challenging socio-economic issues (e.g. social and economic inequity, excess commuting, congestion) and environmental problems, such as air and
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water pollution, urban micro-climate alteration, excess carbon emission, reduction of biodiversity, degradation of surrounding ecological systems, and resource depletion (Barasa et al. 2011; Cao and Ye 2012; Guttikunda et al. 2003; Lin and Ho 2003; Newman and Kenworthy 1999; Pauleit and Duhme 2000; Pielke 2005; Stevens et al. 2007; Song and Xu 2011, Tu et al. 2012; Van Metre and Mahler 2005; Wang et al. 2012; Zang et al. 2011; Zhou et al. 2004). In particular, due to urban expansion, excess commuting and congestion have become major issues for many cities in China. Taking Beijing, China as an example, the average commuting time is *60 min during rush hours, and it is estimated that the congestion cost per person is about $53 per month (Xinlang Auto 2009). Moreover, the conversion from rural to urban land uses has modified physical parameters of the earth surface, resulting in reduced biodiversity and degraded natural ecosystem functions (Wang et al. 2009; Yue et al. 2012; Zang et al. 2011). The process of urbanization in a resource-based city is particular interesting to scholars and urban planners. Unlike other cities, the development of a resource-based city is mainly driven by the exploration of its resources, as well as the development of resource related industries. For the exploration of high intensity resources (e.g. oil, coal, timer, etc.), there is always a tradeoff between economic benefit and environmental cost. In addition, the city always has to face the challenge of economic transformation when the resources are depleted. As a typical resource-based city, Daqing in Heilongjiang Province, China was established in 1959 following the discovery of oil wells. Since then, urban infrastructures have been constructed around the explored oil wells for continuous oil exploration, and the economy of Daqing is highly dependent on petroleum production. Daqing has the largest oil field of China, which is also one of the largest ones in the world. During the past decades, Daqing has experienced unprecedented urban expansion due to the rapid development of petroleum industry, and new urban development mainly located around the explored oil wells. Because of rapid urbanization and generally lack of strategic planning, Daqing is facing many socio-economic and environmental problems, and it is essential to examine the process of urbanization, and to develop policy recommendations for sustainable development. For examining the process of urbanization, a number of models have been developed in the literature, and they can be divided into two broad categories: macro-scale and micro-scale models (Irwin et al. 2009). The major objectives of macro-scale models are to examine exogenous drivers (e.g. socio-economic, political, or biophysical factors) of urbanization, and to predict the amount of land use changes in aggregated geographic regions. Regression techniques, such as econometric models and panel data analysis, have been applied to examine the driving forces
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of urbanization (Chomitz and Gray 1996; Huang et al. 2009; Irwin and Geoghegan 2001; Landis and Zhang 1998; Luo and Wei 2009; Mohapatra and Wu In Press; Nugroho et al. 2011; Wu et al. 2012). In addition to these regressionbased techniques, systems dynamic (SD) models proposed by Forrester (1961, 1969) have also been applied to examine the driving forces of urban spatial dynamics (Verburg et al. 2002; He et al. 2006; Neto de et al. 2006; Han et al. 2009; Yu et al. 2011). The SD model has the ability to uncover complicated relationships among different driving forces within a system, and it can be employed to simulate a number of urban development scenarios under different policy recommendations. Besides the macro-level models, micro-level models have been developed to simulate land use changes (e.g. conversion from rural to urban land uses) at individual locations. In particular, two types of micro-level models, top-down and bottom-up techniques, have been successfully applied in modeling urban spatial dynamics. Specially, top-down models consider the land use conversion at an individual location is mainly due to exogenous forces, instead of local interactions (Verburg and Overmars 2009). Therefore, a top-down model allocates the demands of a particular land use category to individual cells according to their relations with exogenous forces. A widely used top-down urban land use change model is the conversion of land use and its effects (CLUE) model developed by Verburg and his colleagues (Verburg et al. 2002, 2006). Further, Verburg and Overmars (2009) developed a revised model, the DynaCLUE model, and argued that a top-down modeling approach is more appropriate for examining urban land use conversion. Comparatively, bottom-up approaches assume that complicated urban spatial dynamics are the results of local interactions, instead of exogenous forces. While some bottom-up approaches have incorporated regional environmental constraints and land use zoning policies, neighborhood effects have played a much important role in shaping urban spatial dynamics. Typical bottom-up urban dynamic simulation models include the cellular automata (CA) (Clarke et al. 1997; Kamusoko et al. 2009; Li and Yeh 2002; Wu et al. 2010; Wu and Chan 2011; Zhang et al. 2011) and agent-based models (Evans and Kelley 2004; Mena et al. 2011; Parker et al. 2003). Although both ‘‘top-down’’ and ‘‘bottom-up’’ approaches have been widely applied in analyzing urban spatial dynamics at the micro level, so far no research has been conducted for an empirical comparative analysis of these two types of models for a specific urban development process, especially for a resource-based city like Daqing. These models, moreover, are significantly different in terms of the forces of urban land use conversion at the micro level for different urban development process, and consequently the resultant urban spatial dynamics should
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be highly different. Therefore, this study attempted to take Daqing, China, as an example to explore which model, the ‘‘top-down’’ or ‘‘bottom-up’’ approach, is suitable to explain the urbanization mechanism of an oil resourcebased city. This paper is aimed to conduct an empirical comparison of top-down and bottom-up models in Daqing, China. Then these models were validated using visual comparisons and computer-based validation techniques. Further, with the CA model, the impact of stochasticity on model performance was examined, and a suitable random effect was recommended.
2 Study area and data Daqing City, located in Heilongjiang Province, China, was chosen as the study area. Daqing is situated between the latitudes of 45°460 –46°550 N and longitudes of 124°190 – 125°120 E (Fig. 1). It has a geographic area of 5,144 square kilometers, and is comprised of five sub-districts including Ranghulu, Saertu, Longfeng, Honggang, and Datong. The population of Daqing is *1.31 million and the average GDP was about $15,934 per person in 2010, ranked the first in the Northeast China and the eighth in the mainland China (Ministry of Housing and Urban-Rural Development of People’s Republic of China, and Gohighfund 2011). For analyzing the process of urban dynamics in Daqing, Landsat Thematic Mapper (TM) images acquired in 2000 and 2005, and SPOT images taken in 2010 were obtained from the China Remote Sensing Satellite Ground Station and the Second National Land Use Survey Office of China. These images were geo-rectified and mosaicked with highresolution (with a scale of 1:5) topographic maps as references. Six land use classes, agricultural land, forest, grassland, built-up, water, and barren land, were derived using the unsupervised classification function provided by the ERDAS Imagine 9.3 software. In order to further improve the classification accuracy, manual interpretation and digitization were conducted with historical land use
maps as references. The overall Kappa values for all the classification results are over 0.85, indicating that these land use maps have satisfactory accuracies for further analysis. Besides the remote sensing imagery, digital elevation data with a 90 m resolution was obtained from the Global Topography Database of the Consultative Group on International Agricultural Research (CGIAR) Consortium for Spatial Information (http://srtm.csi.cgiar.org). Soil maps were acquired from the Institute of Soil Science, Chinese Academy of Sciences (ISSCAS). Moreover, transportation network data (e.g. railway, provincial and rural roads) were obtained from the Surveying and Mapping Department of Heilongjiang Province, China. Further, related socio-economic data such as population estimates and GDP values for the study area were obtained from the Daqing Statistics Yearbooks of 2001, 2006, and 2011.
3 Methods To examine the urban spatial dynamics of the Daqing City, an integrated macro-scale and micro-scale approach was employed. At the macro scale, we developed a system dynamic model to explore the role of social, economic, political, and biophysical factors on urban dynamics. Further, two micro-scale models, a CLUE-S model and a CA model, were developed as the top-down and bottom-up approaches respectively. 3.1 Macro-scale analysis: SD model In order to examine exogenous drivers of urbanization and predict the demand of urban lands at aggregated geographic scales, we developed an SD model using the Vensim PLE 5.10 program, a commercial software package developed by the Ventana Systems, Inc. To implement this model, we identified two groups of driving forces, land use management policies and socio-economic factors, as
Fig. 1 Daqing City, Heilongjiang Province, China
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major exogenous variables affecting urban dynamics. These driving forces were identified following the recommendations from the International Geosphere and Biosphere Program (IGBP) and the International Human Dimensions Program (IHDP) (Turner et al. 1995; Nunes and Auge 1996; Vellinge 1998). With all the identified socio-economic and land use related driving forces, we divided the SD model into two subsystems, land use subsystem and socio-economic subsystem. The internal structures of these two sub-systems are shown in Fig. 2. Within the land use subsystem, the major components are the six land use types (e.g. agricultural land, forest, grassland, water, built-up, and barren land), and the changes of each land use demand are dependent on other land use types and are also affected by the driving forces in the socio-economic subsystem. Further, the socio-economic subsystem models the impact of socio-economic factors, such as GDP growth, population growth, and technology progress, etc., on land use changes. In addition, a feedback loop structure was constructed to examine the relationship among each component in the system (see Fig. 2). For example, the quantity of built-up lands is highly influenced by population and economy related variables. With high speed population and economy growth, much more residential and commercial land uses are needed to accommodate the growing population and the developments of secondary and tertiary industries. As a result, the quantity of built-up lands will increase to meet the demands. In the model, therefore, their relationships were set as: Population growth rate? ? Population? ? Built-up demand? ? Built-up? and GDP growth rate? ? GDP? ? Secondary and tertiary industries??Built-up?. With the socio-economic and land use information as inputs, we constructed and calibrated the SD model. As a result of the SD model, aggregated land use demand for each land use category was estimated.
Fig. 2 Framework of the SD model
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3.2 Micro-scale analysis For the micro-scale urban dynamic simulation, we developed a top-down (CLUE-S) and a bottom-up (CA) model. These two models were coupled with the macro-scale SD method to form two multi-level models: SD-CLUES and SD-CA. 3.2.1 CLUE-S model At the micro scale, the CLUE-S model was first established to simulate urban spatial dynamics. As a top-down model, CLUE-S allocates the land use demands estimated from the SD approach into specific locations based on conversion rules. In particular, the CLUE-S model decides whether to assign a land use type to a specific location based on three criteria, including (1) global land use probability, (2) land use change elasticity (ELAS) value, and (3) neighborhood effects. For calculating the global land use probability, fourteen variables, including elevation, slope, soil types (e.g. black soil, black and calcium soil, sandy soil, meadow soil, swamp soil, and saline-alkali soil), distance to rivers and ponds, distance to railway, distance to provincial roads, distance to rural roads, distance to the nearest town, and distance to county boundary were chosen as the driving factors to examine the probability that a cell belonging to a specific land use type. A logistic regression analysis was conducted to examine the relation between land use probability and a number of driving forces (see Eq. 1). Pk Log ¼ a0 þ a1 X1;k þ a2 X2;k þ þ an Xn;k ð1Þ 1 Pk where Pk indicates the probability of a cell being assigned to a land use category k (k = 1..6), Xj,k indicates the jth driving factors of land use type k, and n represents the total number of factors. In addition to land use probability information, land
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use change ELAS for a particular land use type was also calculated from the historical land use conversion rate. The resultant ELAS value has a range from 0 to 1, indicating the degree of difficulty of converting from a land use type to other types. That is, an ELAS of 1 indicates that it is almost impossible to convert this type of land uses to others. Further, the neighborhood effects were considered and incorporated in the CLUE-S model. To examine the impact of neighborhood effects on land use conversion, the enrichment factor for each land use type was calculated as follows (Verburg et al. 2004). Fk ¼
nk =n Nk =N
ð2Þ
where Fk is the enrichment factor of land use type k in a neighborhood (e.g. 3 by 3 cells), nk represents the number of cells with land use type k within the neighborhood, n is the total number of cells within the neighborhood, Nk is the total number of cells with land use type k in the study area, and N is the total number of cells in the study area. With the enrichment factor for each land use type and each cell, we applied a logistic regression analysis method to examine the effect of neighborhood on the probability of land use conversion (see Eq. 3). Qk Log ð3Þ ¼ b0 þ b1 F1 þ b2 F2 þ þ bn Fn 1 Qk where Qk is the neighborhood-based probability that a cell being devoted to land use type k, Fi is the enrichment factor for land use type i(i = 1..6), and bi is the regression coefficient. With the global land use probability, ELAS values, and neighborhood effects, we applied the CLUE-S model to allocate land use demands to each cell. Then the allocated areas of each land use type were compared with the demands estimated using the SD model, and adjusted iteratively until the allocated geographic areas for each land use type equals to the demand estimated by the SD model. 3.2.2 Cellular automata (CA) model The other micro-scale urban spatial dynamics simulation model is the CA model, a widely accepted ‘‘bottom-up’’ approach. The general structure of a CA model can be expressed as follows. Ui ¼ f ðPi ; Ni ; Ci ; Ri Þ
ð4Þ
where Ui is the probability of cell i being converted to urban land uses, Pi is the global urban expansion probability, Ni is the neighborhood effect, Ci is the constraint factor, and Ri is the random factor. To determine the global urban expansion probability Pi, we employed the logistic regression analysis method (see Eq. 1) to be consistent with the CLUE-S model.
The resultant global urban expansion probability is as same as the global land use probability for the urban land use type employed in the CLUE-S model. In addition to the global probability, the neighborhood effect Ni, the most important factor in the CA model, was calculated through dividing the number of urban cells within the neighborhood by the total number of cells in a neighborhood (e.g. 3 by 3 cells). The resultant Ni, therefore, represents the impact of neighboring land uses on the land conversion probability of a particular cell. Furthermore, two constraint factors (Ci), slope and water body, were incorporated in the CA model. In particular, a cell with a slope of 22.5° and higher, or identified as a water body was excluded to be developed into urban lands. Finally, a random factor (Ri) was incorporated into the CA model due to the stochastic characteristics of the urban spatial dynamics. The random factor was calculated as follows (see Eq. 5). Ri ¼ 1 þ ð ln cÞa
ð5Þ
where c is a random number between 0 and 1, and a is the control parameter ranging from 1 to 10. With all the calculated factors, including the global probability Pi, Neighborhood effect Ni, constraint factors Ci, and random factor Ri, the probability of a cell being converted to urban lands was derived as the product of these factors. 3.3 Modeling result validation With the simulated results from the coupled SD-CLUES and SD-CA models and the 2005 and 2010 land use maps derived from classifying the Landsat TM and SPOT images as ground truth data, we applied multiple validation approaches to assess the modeling accuracy, including (1) pixel matching, (2) spatial and feature pattern recognition (Torrens 2011). In particular, pixel matching evaluates the proportion of agreement between the simulated and observed results through a pixel by pixel comparison. Although it is considered the simplest and most straightforward approach, the pixel matching method ignores the overall spatial pattern, and the results can be affected significantly by locational errors. To address this problem, a multi-resolution procedure proposed by Costanza (1989) was utilized in this study through comparing the observed and simulated maps over different resolutions. The goodness of fit at different sampling window sizes can be calculated using the following formula: Pp Ptw jai;j bi;j j i¼1 j¼1 1 2w2 Fw ¼ ð6Þ tw where Fw is the goodness of fit at the sampling window size w (w is the linear dimension of a sampling window), ai,j is the total number of cells with land use type i in the
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sampling window j (with a size of w) of the simulated map, bi,j is the total number of cells with land use type i in the sampling window j of the reference map, p is the total number of different land use types, and tw is the total number of sampling windows with a size of w in the maps. With the goodness of fit Fw at each window size, the overall fit between the simulated map and reference map can be calculated with a weighted average of the fit over all window sizes (see Eq. 7). P cðw1Þ w Fw e Ft ¼ P ð7Þ cðw1Þ we where Ft is the overall fit and c is a constant that determines the weight given to sampling window size w. If c equals to 0, all sampling window sizes are given the same weight; whereas if c equals to 1, only the goodness of fits with small window sizes are considered important. In this study, c is set to be 0.1 following the guidance of Costanza (1989). Further, in order to evaluate the compactness and complexity of urban land uses, landscape metrics measurements were employed to examine the spatial pattern. In particular, five indices, number of patches (NP), aggregation index (AI), area-weighted mean fractal dimension index (AWMPFD), landscape shape index (LSI), and patch edge density (ED), were calculated using the Fragstat 3.3, an open source software package (see Table 1).
4 Results 4.1 Macro-scale model With the land use information and socio-economic variables acquired in 2000 and 2005, an SD model was constructed and calibrated. Then this model was applied to predict the demand of each land use type in 2010. Results (see Table 2) indicate that the SD model performs reasonably well, with the relative errors for all land use types \8 %. In particular, for built-up lands, the estimation error is relatively low (-4.08 %), indicating that this model has a satisfactory accuracy, and can serve as the macro-scale model for estimating the aggregated urban land use demand. Table 2 also indicates that from 2005 to 2010, Daqing has experienced a rapid urban expansion, with a significant increase of built-up lands at the cost of agricultural land, grassland, and water body. Specially, the geographic area of the built-up land has increased from 32,380 ha in 2005 to 50,224 ha in 2010, with an increment of 55.11 %. Simultaneously, rural lands including agriculture land, grassland, and water body have decreased about 2.16, 14.07, and 27.84 % respectively. As the objective of this study is to model urban dynamics, rural land use types were merged into a single land use type, and only two land use classes, urban and rural lands, were employed for further analyses.
Table 1 Landscape metrics Name
Description
Number of patches (NP)
Number of patches of a particular land use type, indicating the level of fragmentation of a study area
Aggregation index (AI)
Percentage of like adjacencies (joins with the same land use type) for a pixel, indicating the degree of contagion
Area-weighted mean fractal dimension index (AWMPFD)
Calculated as the fractal dimension weighted by the patch area, indicating the complexity of shapes
Landscape shape index(LSI)
A measure of patch disaggregation, calculated as the total length of edges divided by the minimum total length possible
Patch edge density (ED)
Calculated by dividing the total lengths of all edge segments by the total landscape area, indicating the patch complexity
Table 2 Prediction accuracy of the SD model (assessed using 2010 reference data) Years
Agriculture land
Forest
Grass land
Water body
Built-up
Barren land
Start
2005
326,948
3,616
19,892
55,628
32,380
75,988
Reference data
2010
319,896
4,026
17,094
40,141
50,224
83,071
Simulation results Relative error (%)
2010
322,322 0.76
4,288 6.51
18,399 7.60
43,267 7.79
48,175 -4.08
78,001 -6.10
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4.2 Micro-level model
Table 4 Enrichment factors (F) and beta-coefficients derived from the logistic regression
4.2.1 Top-down model: CLUE-S
Enrichment factors
To implement the CLUE-S model, a logistic regression model was constructed to derive the land use probability map. Results (see Table 3) indicate that the probability of a cell belonging to urban land is associated with elevation, slope, soil types (e.g. black and calcium soil, sandy soil, and meadow soil), distance to railway, distance to provincial roads, distance to rural roads, distance to the nearest town, and distance to county boundary. The resultant ROC value for the model is 0.862, indicating the selected driving forces can successfully explain the distribution of urban lands in the study area. After generating the land use probability map, we also calculated the land use change ELAS based on the land use conversion rate from 2000 to 2005. The resultant ELAS value equals to 1 for urban lands, and 0.55 for rural lands. This indicates that rural lands can be converted to urban lands, while urban lands cannot be transformed to rural lands. Based on the ELAS values, we constructed the conversion matrix, and assigned a value of zero to urban cells, and one to rural cells, indicating that only rural lands can be converted to urban lands. Further, the neighborhood effects were estimated through the logistic regression analysis with enrichment factors for each land use type as inputs. Results (see Table 4) indicate that a significantly positive association exists among urban cells (ROC equals to 0.719), and the
Non-urban factor
Table 3 Driving factors and beta-coefficients derived from the logistic regression Driving factors
Beta-coefficients
Elevation (m)
0.10736
Slope (degree) Soil Black soil
0.23355 –
Black and calcium soil
1.36258
Sandy soil
1.52491
Meadow soil
1.19111
Swamp soil
–
Saline-alkali soil
–
Distance to rivers and ponds (m) Distance to railway (m) Distance to provincial roads (m)
– -0.00004 -0.00057
Distance to the nearest town (m)
-0.00005
Constant ROC
1.61035
Urban factor Constant
0.96849 -4.88124
ROC
0.719
neighborhood effects should be considered in modeling urban spatial dynamics. With the land use demands estimated by the SD model, the land use probability map, ELAS value, and the neighborhood effects, a CLUE-S model was implemented. Results (see Fig. 3b) indicate that, with the CLUE-S model, urban expansion mainly happens along transportation networks, such as railway, roads, etc. Moreover, significant urban expansion also takes place in or around the downtown of Daqing City located in the northeastern part. These results clearly indicate that, with the CLUE-S model, many exogenous driving forces have played important roles in shaping future urban dynamics. 4.2.2 Bottom-up model (CA) With the land use information and driving factors for the years of 2000–2005, we constructed and calibrated the CA model. Further, this model was applied to simulate the urban growth dynamics in 2010. To construct the CA model, land use demands estimated by the SD model were employed to control the total areas of urban and rural lands, and the rural-to-urban conversion probability was generated through integrating the global urban expansion probability (see Table 3), the neighborhood effect, the constraint factors, and the random factor. For this research, we set the control parameter a as 1 following the results of other studies. Results of the CA model (see Fig. 3c) show that the simulated urban expansion majorly happens around the existing urban centers, indicating that the neighborhood effects have played an important role in driving urban growth. 4.3 Comparison and validation of the SD-CLUES and SD-CA models
0.00004
Distance to rural roads (m) Distance to county boundary (m)
Urban
-0.00019 -17.86102 0.861
Not significant at the 95 % significant level and values are not included
With the simulated urban spatial dynamics from the SDCLUES and SD-CA models, visual comparison and model validation have also been performed to examine the trade-offs between these two models. With the SD-CLUES model (see Fig. 3b), it appears that the majorities (over 70 %) of simulated urban expansions are clustered in or around the downtown of Daqing City (the northeastern part). Moreover, most
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Stoch Environ Res Risk Assess Fig. 3 Comparison of simulated and reference urban land uses a reference map in 2005, b simulated map with the SD-CLUES model in 2010, c simulated map with the SDCA model in 2010, d reference map in 2010
of the simulated urban cells follow the major transportation networks (e.g. provincial roads, railways, etc.), indicating that the exogenous driving forces have significant impact on urban growth patterns. On the contrary, quite different spatial patterns have been found in the simulated map derived from the SD-CA model (see Fig. 3c). In particular, it can be observed that the simulated urban growth is spread over the study area, and most of the growths are around existing urban infrastructures in 2005. This result indicates that, with the SD-CA model, the neighborhood effects dominate the simulation results. This is likely to be consistent with the characteristics of urbanization process of Daqing City. As a resource-based
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city, Daqing was initially established around oil wells for convenient oil exploration and production, and therefore generating a unique multi-nuclei city pattern. With the high speed urban expansion, new urban constructions in Daqing are highly clustered around existing urban infrastructures, which were constructed around explored oil wells several decades ago. Like many other resource-based cities in China, Daqing is in the process of transforming from a resourcedependent economy to a diversified economy because of the incoming issues of oil resource depletion. Associated with the high demands of economic growth and diversified industry development, rapid urban expansion occurs in the fringe of
Stoch Environ Res Risk Assess Table 5 Validation results of SD-CLUES model and SD-CA model
Validation approaches
2010 reference data
Pixel matching
Pixel by pixel comparison
Pattern recognition
Ft
SD-CLUES model 93.18 %
SD-CA model 93.89 %
0.9941
0.9981
NP
881
483
533
ED
5.0727
3.8013
4.1101
AWMPFD
1.2112
1.1951
1.2024
LSI
29.0756
21.7956
23.5156
AI
94.5577
95.8369
95.5525
Fig. 4 Simulated 2010 urban land maps using CA model with different random parameters (a)
the city. Therefore, unlike many other cities in which transportation networks drive urbanization, Daqing’s urban infrastructures are more associated with the locations of existing urban infrastructures, as well as oil wells, and the urban distribution pattern of Daqing has also been transformed from scattered spots to a zonal landscape through connecting these individual spots. Therefore, the map generated from SD-CA model is more close to the reality. Further, a visual comparison indicates that the results from the SD-CA model appear to be more similar to the observed map in 2010 (Fig. 3d), although more rigorous comparisons are necessary. In addition to visual comparisons, we also assessed the accuracy of the simulation results with the pixel-matching and spatial pattern recognition techniques. Results (see Table 5) indicate that, with the pixel matching measure, the accuracy of the SD-CLUES (93.18 %) is slightly lower than that of the SD-CA model (93.89 %). Moreover, the multiresolution goodness of fit (Fw) shows that the SD-CA model performs better than SD-CLUES model for all resolutions, and the accuracy increases as the resolution decreases. A comparison of the overall accuracy of the multi-resolution
goodness of fit (Ft) (see Table 5) also indicates that the SD-CA model has a slightly better fit. Further, it can be observed that the landscape metrics values, including number of patches (NP), area-weighted mean patch fractal dimension (AWMPFD), patch edge density (ED), and landscape shape index (LSI), derived from the SD-CA model are more similar to those calculated with the 2010 reference map. All of these results indicate that the SD-CA model has slightly outperformed the SD-CLUES model.
Fig. 5 Accuracy assessment (pixel-by-pixel matching) of the SD-CA model with different random factors (a changes from 0 to 10)
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Fig. 6 Overall accuracy of the model goodness of fit (Ft)
4.4 Effects of the random factor in the SD-CA model Due to the importance of the stochastic factors in the SD-CA model, we also performed a sensitivity analysis to examine the effects of the random factors on the modeling results. In detail, we let the control parameter a changes from 0 (without random effect) to 10 (with the highest random effect), and evaluate the resultant urban spatial dynamics (see Fig. 4). Validation results indicate that, with the pixel-by-pixel comparison measure, the SD-CA model without a random factor generated the best results with an accuracy of 94.02 %. Fig. 7 Landscape metric indices and overall accuracy of for the reference and simulated urban maps in 2010 (the first point shows the value of the reference map in 2010, and others show the values of the simulated maps with the value of a varying from 0 to 10)
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When a random factor was added, the overall accuracy continues to drop dependent on the degree of randomness (see Fig. 5). When the spatial pattern was measured, however, the introduction of random factors improves the modeling accuracy when the control factor has a value of 1 or 2 (see Figs. 6, 7). For example, when the control factor has a value of 1, the Ft value is the highest (0.9981). Moreover, the landscape metric measures are also similar to those in the reference map. As a summary, the sensitivity analysis showed that, with the pixel-by-pixel measure, the SD-CA model without a random factor performs the best. However, with spatial pattern recognition measures, the stochastic model with appropriate randomness assignment (e.g. control factor equal to one in this study) may generate better results.
5 Conclusions and discussion This paper developed two multi-scale modeling approaches, the SD-CLUES and SD-CA models, to examine the process of oil resources based urban spatial dynamics of Daqing City, China. In particular, the SD-CLUES connects the SD model (a macro-scale approach) with the CLUE-S model (a top-down micro-level method); and the SD-CA
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model integrates the results of the SD model with the bottom-up CA modeling approach. Further, the results of these two models were compared through visual examination and computer-based validation. Analysis of results suggests several major conclusions. First, significantly different results have been generated using the SD-CLUES and SD-CA models. As a top-down approach, the simulated urban growth from the SD-CLUES model clustered around the downtown of Daqing City and along the major transportation networks. It indicates a strong influence of exogenous driving forces on urban spatial dynamics. With the SD-CA model, on the contrary, simulated urban cells are spread over the entire study area, and closely associated with existing urban infrastructures. This indicates that the resultant urban dynamics derived from the SD-CA model are majorly due to the neighborhood effects, instead of the exogenous factors. Analysis of results indicates that the map generated by the SD-CA model is more close to the reality as it represents the characteristics of the urbanization process in Daqing City, in which urban expansion is clustered around existing urban infrastructures, as well as oil well locations. Secondly, through visual comparisons and computer-based validations, we found that the SD-CA model generated slightly better results with the pixel-by-pixel comparison and spatial pattern recognition approaches. Finally, it appears that the stochastic factor in the SD-CA model has significant impact on the modeling results, and the choice of such a factor should be carefully examined. Modeling the process of urbanization has been an important research topic in the geography and urban planning literature during the past decades. In particular, with the advances of computer-based modeling capabilities, bottom-up simulation approaches have emerged, and consequently debates on whether these approaches are appropriate for urban growth analysis have been carried out. Although this paper sheds some lights on the trade-offs between top-down and bottom-up models, systematic examinations of these two modeling approaches are necessary as future research. Further, more studies are necessary on evaluating the impacts of exogenous driving forces and local effects on urban spatial dynamics. Acknowledgments This research was supported by the National Natural Science Foundation of China (Nos. 41030743, 41171322). We would like to acknowledge the anonymous reviewers for their constructive and valuable suggestions on the earlier drafts of this manuscript.
References Barasa B, Majaliwa JGM, Lwasa S, Obando J, Bamutaze Y (2011) Magnitude and transition potential of land-use/cover changes in
the trans-boundary river Sio catchment using remote sensing and GIS. Ann Gis 17(1):73–80 Cao K, Ye X (2012) Coarse-grained parallel genetic algorithm applied to a vector based land use allocation optimization problem: the case study of Tongzhou Newtown, Beijing, China. Stoch Environ Res Risk Assess. doi:10.1007/s00477-012-0649-y Chen MX, Lu DD, Zhang H (2009) Comprehensive evaluation and the driving factors of China’s urbanization. Acta Geograpica Sinica 64:387–398 China Association of Mayors (CAM) (2007) Urban development report of China (2006). China City Press, Beijing China Statistic Yearbook (2003).China statistic press. Beijing Chomitz KM, Gray DA (1996) Roads, land use, and deforestation: a spatial model applied to Belize. World Bank Econ Rev 10(3): 487–512 Clarke KC, Hoppen S, Gaydos L (1997) A self-modifying cellular automation model of historical urbanization in the San Francisco Bay Area. Environ Planning B 24:247–261 Costanza R (1989) Model goodness of fit-a multiple resolution procedure. Ecol Model 47(3–4):199–215 de Neto ACL, Legey LFL, Gonza´lez-Araya MC, Jablonski S (2006) A system dynamic model for the environmental management of the Sepetiba Bay watershed, Brazil. Environ Manag 38:879–888 Evans TP, Kelley H (2004) Multi-scale analysis of a household level agent-based model of land cover change. J Environ Manag 72:57–72 Forrester JW (1961) Industrial dynamics. Pegasus Communications, Waltham Forrester JW (1969) Urban dynamics. The Massachusetts Institute of Technology Press, Cambridge Guttikunda SK, Carmichael GR, Calori G, Eck C, Woo JH (2003) The contribution of megacities to regional sulfur pollution in Asia. Atmos Environ 37:11–22 Han J, Hayashi Y, Cao X, Imura H (2009) Application of an integrated system dynamics and cellular automata model for urban growth assessment: a case study of Shanghai, China. Landsc Urban Planning 91:133–141 He CY, Okada N, Zhang Q, Shi P, Zhang J (2006) Modeling urban expansion scenarios by coupling cellular automata model and system dynamic model in Beijing, China. Appl Geogr 26: 323–345 Huang B, Xie C, Tay R, Wu B (2009) Land-use-change modeling using unbalanced support-vector machines. Environ Planning B 36(3):398–416 Irwin EG, Geoghegan J (2001) Theory, data, methods: developing spatially-explicit economic models of land use change. J Agric Ecosyst Environ 85(1–3):7–24 Irwin EG, Jayaprakash C, Munroe DK (2009) Toward a comprehensive framework for modeling urban spatial dynamics. Landsc Ecol 24:1223–1236 Kamusoko C, Aniya M, Adi B, Manjoro M (2009) Rural sustainability under threat in Zimbabwe-Simulation of future land use/ cover changes in the Bindura district based on the Markovcellular automata model. Appl Geogr 29:435–447 Landis JD, Zhang M (1998) The second generation of the California urban features model: part 2: specification and calibration results of the land-use change submodel. Environ Planning B 25(6):795–824 Li X, Yeh AG (2002) Neural-network-based cellular automata for simulating multiple land use changes using GIS. Int J Geogr Inf Sci 16(4):323–343 Lin GC, Ho SP (2003) China’s land resources and land-use change: insights from the 1996 land survey. Land Use Policy 20:87–107 Luo J, Wei DYH (2009) Modeling spatial variation of urban growth patterns in Chinese cities, the case of Nanjing. Landsc Urban Planning 91:51–64
123
Stoch Environ Res Risk Assess Mena CF, Walsh SJ, Frizzelle BG, Yao X, Malanson GP (2011) Land use change on household farms in the Ecuadorian Amazon: design and implementation of an agent-based model. Appl Geogr 31(1):210–222 Ministry of Housing and Urban-Rural Development of People’s Republic of China, and Gohighfund (2011) The Chinese private capital investment report, http://www.gohighfund.com/articles/ 21. Accessed Nov 2012 Mohapatra R and Wu C Modeling urban growth at a micro level: a panel data analysis. Int J Appl Geospatial Res (in press) Newman P, Kenworthy JR (1999) Sustainability and cities: overcoming automobile dependence. Island Press, Washington DC Nugroho SB, Fujiwara A, Zhang JY (2011) An empirical analysis of the impact of a bus rapid transit system on the secondary pollutants in the roadside areas of the Transjakart corridors—a structural equation model and artificial neural network approach. Stoch Environ Res Risk Assess 25(5):655–669 Nunes C, Auge JI (1996) Land use and land cover change (LUCC) implementation strategy. IGBP Report No. 48 and IHDP Report No. 10 Parker DC, Manson SM, Janssen MA, Hoffmann MJ, Deadman P (2003) Multi-agent systems for the simulation of land-use and land-cover change: a review. Ann Assoc Am Geogr 93(2):314–337 Pauleit S, Duhme F (2000) Assessing the environmental performance of land cover types for urban planning. Landsc Urban Planning 52:1–20 Pielke RA (2005) Land use and climate change. Science 310: 1625–1626 Song Y, Ding C (2009) Smart urban growth for China. Lincoln Institute of Land Policy, Cambridge Song HM, Xu LY (2011) A method of urban ecological risk assessment: combing the multimedia fugacity model and GIS. Stoch Environ Res Risk Assess 25(5):713–719 Stevens D, Dragicevic S, Rothley K (2007) iCity: GIS-CA modelling tool for urban planning and decision making. Environ Model Softw 22:761–773 Torrens PM (2011) Calibrating and validating cellular automata models of urbanization. In: Yang X (ed) Urban remote sensing: monitoring, synthesis and modeling in the urban environment. Wiley-Blackwell Press, Chichester, pp 335–345 Tu XJ, Zhang Q, Singh VP, Chen XH, Liu CL, Wang SB (2012) Space-time changes in hydrological processes in response to human activities and climatic change in the south China. Stoch Environ Res Risk Assess 26(6):823–834 Turner II BL, David S, and Liu Y (1995) Land use and land cover change science/research plan, IHDP Report No. 07 Van Metre PC, Mahler BJ (2005) Trends in hydrophobic organic contaminants in urban and Reference Lake sediments across the United States, 1970–2001. Environ Sci Technol 39:5567–5574 Vellinge P (1998) IHDP Industrial transformation. IHDP-IT Publication No. 12.5
123
Verburg PH, Overmars KR (2009) Combining top-down and bottomup dynamics in land use modeling: exploring the future of abandoned farmlands in Europe with the Dyna-CLUE model. Landsc Ecol 24:1167–1181 Verburg PH, Soepboer W, Limpiada R, Espaldon MVO, Sharifa M, Veldkamp A (2002) Land use change modeling at the regional scale: the CLUE-S Model. Environ Manag 30(3):391–405 Verburg PH, Eck JR, Nijs TC, Schot MP (2004) Determinants of land-use change patterns in the Netherlands. Environ Planning B 31:125–150 Verburg PH, Schulp CJE, Witte Veldkamp NA (2006) Downscaling of land use change scenarios to assess the dynamics of European landscapes. Agric Ecosyst Environ 114:39–56 Wang SJ, Li J, Wu DQ, Liu J, Zhang K, Wang RQ (2009) The strategic ecological impact assessment of urban development policies: a case study of Rizhao City, China. Stoch Environ Res Risk Assess 23(8):1169–1180 Wang QS, Yuan XL, Ma CY, Zhang Z, Zuo J (2012) Research on the impact assessment of urbanization on air environment with urban environmental entropy model: a case study. Stoch Environ Res Risk Assess 26(3):443–450 Wu YT, Chan KY (2011) Optimal design and impact analysis of urban traffic regulations under ambient uncertainty. Stoch Environ Res Risk Assess 25(2):271–286 Wu DQ, Liu J, Wang SJ, Wang RQ (2010) Simulating urban expansion by coupling a stochastic cellular automata model and socioeconomic indicators. Stoch Environ Res Risk Assess 24(2):235–245 Wu K, Ye X, Fang Z, Zhang H (2012) Impact of land use/ land cover change and socioeconomic development on regional ecosystem services: the case of the fast-growing Hangzhou metropolitan area, China. Cities. doi:10.1016/j.bbr.2011.03.031 Xinlang Auto (2009) Futian index: economic cost of Beijing’s traffic congestion is ranked the first, http://auto.sina.com.cn/news/200912-25/1039553292.shtml. Accessed Jan 2012 Yu W, Zang S, Wu C, Liu W, Na X (2011) Analyzing and modeling land use land cover change (LUCC) in the Daqing City China. Appl Geogr 31(2):600–608 Yue WZ, Liu Y, Fan P, Ye XY, Wu CF (2012) Assessing spatial pattern of urban thermal environment in Shanghai, China. Stoch Environ Res Risk Assess 26(7):899–911 Zang S, Wu C, Liu H, Na X (2011) Impact of urbanization on natural ecosystem service values: a comparative study. Environ Monit Assess 179:575–588 Zhang Q, Ban Y, Liu J, Hu Y (2011) Simulation and analysis of urban growth scenarios for the Greater Shanghai Area China. Comput Environ Urban Syst 35:126–139 Zhou L, Dickinson R, Tian Y, Fang J, Li Q, Kaufman RK, Tucker CJ, Myneni RB (2004) Evidence for a significant urbanization effect on climate in China. Proc Natl Acad Sci U S A 101:9540–9544