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Modeling Urban Land Use Conversion of Daqing City, China: a Comparative Analysis of ''Top-Down'' and ''Bottom-Up'

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Modeling Urban Land Use Conversion of Daqing City, China: a Comparative Analysis of ''Top-Down'' and ''Bottom-Up' 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 Keywords Urbanization Á Stochastic cellular automata Á has experienced unprecedented urban expansion due to the CLUE-S model Á Daqing rapid development of petroleum industry. With rapid urbanization and lack of strategic planning, Daqing is facing many socio-economic and environmental problems, 1 Introduction and it is essential to examine the process of urbanization, and to develop policy recommendations for sustainable During the past 20 years, many metropolitan areas in China development. To address this problem, this paper examined have experienced unprecedented expansion due to popu- the urbanization process of Daqing City through develop- lation growth and migration. Urban built-up areas in China ing two multi-level models: an integrated system dynamic have increased from 10,161 km2 in 1986 to 32,600 km2 in (SD) and CLUE-S model (SD-CLUES), and an integrated 2006, with an increment of 220.83 % (China Statistic SD and stochastic cellular automata model (SD-CA). Yearbook 2003; China Association of Mayors 2007). This Analysis of results suggests that these two models generate high-speed urbanization is associated with the rapid growth significantly different results. With the SD-CLUES model, of urban population. In particular, urban population has new urban developments are clustered in the downtown increased from 302 million in 1990 to 456 million in 2000, area or along major transportation networks, indicating and it is projected that in 2020, *900 million Chinese exogenous driving forces playing an important role in people will reside in urban areas (Song and Ding 2009). shaping urban spatial dynamics. With the SD-CA model, Simultaneously, the percent of urban population to the total on the contrary, the resultant new urban cells are spread population has increased from 26 % in 1990 to 36 % in over the entire study area, and associated with existing 2000, and to 50 % in 2010, and it is projected that urban urban areas. Further, visual comparisons and validations population percentage will reach 65 % in 2050 (Chen et al. indicate that the SD-CA model is a better alternative in 2009; Song and Ding 2009). Rapid urbanization in China is explaining the urbanization mechanism of Daqing City. In associated with high speed industrialization and a phe- addition, analysis of results suggests that the stochastic nomenal economic growth. China has maintained the factor in the CA model has significant impact on the fastest gross domestic production (GDP) growth rate (e.g. modeling accuracy. 9.6 % annually since 1987) in the world since the imple- mentation of the economic reform and open-door policies. As a result of rapid economic development during these & W. Li Á C. Wu ( ) Á S. Zang years, China was ranked as one of the four world’s largest Key Laboratory for Remote Sensing Monitoring of Geographic Environment, College of Heilongjiang Province, Harbin Normal economies with a GDP of $5.88 trillion US dollars in 2010. University, Harbin, Heilongjiang 150025, China While rapid urbanization brought economic benefits and e-mail: [email protected] improved the quality of life, ill-planned urban growth also generated numerous challenging socio-economic issues W. Li Á C. Wu Department of Geography, University of Wisconsin-Milwaukee, (e.g. social and economic inequity, excess commuting, PO Box 413, Milwaukee, WI 53201, USA congestion) and environmental problems, such as air and 123 Stoch Environ Res Risk Assess water pollution, urban micro-climate alteration, excess of urbanization (Chomitz and Gray 1996; Huang et al. carbon emission, reduction of biodiversity, degradation of 2009; Irwin and Geoghegan 2001; Landis and Zhang 1998; surrounding ecological systems, and resource depletion Luo and Wei 2009; Mohapatra and Wu In Press; Nugroho (Barasa et al. 2011; Cao and Ye 2012; Guttikunda et al. et al. 2011; Wu et al. 2012). In addition to these regression- 2003; Lin and Ho 2003; Newman and Kenworthy 1999; based techniques, systems dynamic (SD) models proposed Pauleit and Duhme 2000; Pielke 2005; Stevens et al. 2007; by Forrester (1961, 1969) have also been applied to Song and Xu 2011, Tu et al. 2012; Van Metre and Mahler examine the driving forces of urban spatial dynamics 2005; Wang et al. 2012; Zang et al. 2011; Zhou et al. 2004). (Verburg et al. 2002; He et al. 2006; Neto de et al. 2006; In particular, due to urban expansion, excess commuting Han et al. 2009; Yu et al. 2011). The SD model has the and congestion have become major issues for many cities in ability to uncover complicated relationships among dif- China. Taking Beijing, China as an example, the average ferent driving forces within a system, and it can be commuting time is *60 min during rush hours, and it is employed to simulate a number of urban development estimated that the congestion cost per person is about $53 scenarios under different policy recommendations. Besides per month (Xinlang Auto 2009). Moreover, the conversion the macro-level models, micro-level models have been from rural to urban land uses has modified physical developed to simulate land use changes (e.g. conversion parameters of the earth surface, resulting in reduced bio- from rural to urban land uses) at individual locations. In diversity and degraded natural ecosystem functions (Wang particular, two types of micro-level models, top-down and et al. 2009; Yue et al. 2012; Zang et al. 2011). The process bottom-up techniques, have been successfully applied in of urbanization in a resource-based city is particular inter- modeling urban spatial dynamics. Specially, top-down esting to scholars and urban planners. Unlike other cities, models consider the land use conversion at an individual the development of a resource-based city is mainly driven location is mainly due to exogenous forces, instead of local by the exploration of its resources, as well as the develop- interactions (Verburg and Overmars 2009). Therefore, a ment of resource related industries. For the exploration of top-down model allocates the demands of a particular land high intensity resources (e.g. oil, coal, timer, etc.), there is use category to individual cells according to their relations always a tradeoff between economic benefit and environ- with exogenous forces. A widely used top-down urban land mental cost. In addition, the city always has to face the use change model is the conversion of land use and its challenge of economic transformation when the resources effects (CLUE) model developed by Verburg and his col- are depleted. As a typical resource-based city, Daqing in leagues (Verburg et al. 2002, 2006). Further, Verburg and Heilongjiang Province, China was established in 1959 fol- Overmars (2009) developed a revised model, the Dyna- lowing the discovery of oil wells. Since then, urban infra- CLUE model, and argued that a top-down modeling structures have been constructed around the explored oil approach is more appropriate for examining urban land use wells for continuous oil exploration, and the economy of conversion. Comparatively, bottom-up approaches assume Daqing is highly dependent on petroleum production. that complicated urban spatial dynamics are the results of Daqing has the largest oil field of China, which is also one local interactions, instead of exogenous forces. While some of the largest ones in the world. During the past decades, bottom-up approaches have incorporated regional envi- Daqing has experienced unprecedented urban expansion ronmental constraints and land use zoning policies, due to the rapid development of petroleum industry, and neighborhood effects have played a much important role in new urban development mainly located around the explored shaping urban spatial dynamics. Typical bottom-up urban oil wells. Because of rapid urbanization and generally lack dynamic simulation models include the cellular automata of strategic planning, Daqing is facing many socio-eco- (CA) (Clarke et al. 1997; Kamusoko et al. 2009; Li and nomic and environmental problems, and it is essential to Yeh 2002; Wu et al. 2010; Wu and Chan 2011; Zhang et al. examine the process of urbanization, and to develop policy 2011) and agent-based models (Evans and Kelley 2004; recommendations for sustainable development. Mena et al. 2011; Parker et al. 2003). For examining the process of urbanization, a number of Although both ‘‘top-down’’ and ‘‘bottom-up’’ approa- models have been developed in the literature, and they can ches have been widely applied in analyzing urban spatial be divided into two broad categories: macro-scale and dynamics at the micro level, so far no research has been micro-scale models (Irwin et al. 2009). The major objec- conducted for an empirical comparative analysis of these tives of macro-scale models are to examine exogenous two types of models for a specific urban development drivers (e.g. socio-economic, political, or biophysical fac- process, especially for a resource-based city like Daqing. tors) of urbanization, and to predict the amount of land use These models, moreover, are significantly different in changes in aggregated geographic regions. Regression terms of the forces of urban land use conversion at the techniques, such as econometric models and panel data micro level for different urban development process, and analysis, have been applied to examine the driving forces consequently the resultant urban spatial dynamics should 123 Stoch Environ Res Risk Assess be highly different. Therefore, this study attempted to take maps as references. The overall Kappa values for all the Daqing, China, as an example to explore which model, the classification results are over 0.85, indicating that these ‘‘top-down’’ or ‘‘bottom-up’’ approach, is suitable to land use maps have satisfactory accuracies for further explain the urbanization mechanism of an oil resource- analysis.
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