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Spatiotemporal variation analysis of driving forces of urban land spatial expansion using logistic regression: A case study of port towns in Taicang City, China
Habitat International 43 (2014) 181e190
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Spatiotemporal variation analysis of driving forces of urban land spatial expansion using logistic regression: A case study of port towns in Taicang City, China Bangrong Shu a, *, Honghui Zhang b, Yongle Li c, Yi Qu d, Lihong Chen a a
School of Geodesy and Geomatics, Jiangsu Normal University, Xuzhou 221116, China School of Geographic and Oceanographic Sciences, Nanjing University, Nanjing 210093, China School of Public Administration, Nanjing University of Finance & Economics, Nanjing 210023, China d College of Public Management, Nanjing Agricultural University, Nanjing 210095, China b c
a r t i c l e i n f o
a b s t r a c t
Article history: Available online
Understanding the driving mechanisms of urban land spatial expansion (ULSE) is crucial for the guidance of rational urban land expansion. Previous studies have primarily focused on single large cities, with few explorations of the spatiotemporal differences in driving forces of ULSE of different towns in the same administrative region. This study aims to fill this gap. Three port towns of Taicang, located in China’s Yangtze River Delta region, were taken as examples to analyze the expansion process of urban land during 1989e2008. Eight factors, including ecological suitability, prime croplands, etc., were selected from four aspects of natural eco-environment, land control policies, accessibility and neighborhood. Binary logistic regression was employed to investigate the effects of various factors on ULSE during various periods in different regions. Results reveal that over the past two decades, urban land expanded rapidly in the three towns, but with different expansion speeds and growth rates. Diversified ULSE factor combinations exist during different periods in different regions, and the factors’ relative importance also varies with time and space. The four types of driving factors simultaneously affect ULSE, among which the accessibility is dominant. Based on the findings, we suggest that differentiated policies should be formulated to guide reasonable expansion of urban land. This study can help us better understand the driving mechanism of urban land expansion in small cities and towns, thus has important implications for urban planning and management in China and similar countries. Ó 2014 Elsevier Ltd. All rights reserved.
Keywords: Driving forces Spatiotemporal variation Urban land spatial expansion Logistic regression China
Introduction Land use/cover change (LUCC) has become one of the key issues in global change research (Pielke, 2005). As one of the cores of LUCC research, urban land expansion is related to sustainable regional socio-economic development. In particular, the loss of forest (Miller, 2012), cropland (Tan, Li, Xie, & Lu, 2005), as well as regional climate change (Pielke, 2005) and other problems caused by urban land expansion have become the focus of attention. In order to reduce the negative effects of urban land expansion, it is necessary to develop effective planning and management strategies, and its foundation is the clarification of the driving mechanisms of urban land expansion (Braimoh & Onishi, 2007; Li, Zhou, & Ouyang, 2013).
* Corresponding author. Tel.: þ86 13852434645. E-mail address: [email protected] (B. Shu). http://dx.doi.org/10.1016/j.habitatint.2014.02.004 0197-3975/Ó 2014 Elsevier Ltd. All rights reserved.
Many studies have been conducted to understand the driving forces of urban land expansion around the world (Deng, Huang, Rozelle, & Uchida, 2008; Li et al., 2013; Seto, Fragkias, Guneralp, & Reilly, 2011). However, urban land expansion involves two aspects, i.e. area and space. Previous studies have mainly focused on the drivers of urban land area expansion (Jiang, Deng, & Seto, 2012; Wu & Zhang, 2012) and the descriptive analysis on the driving forces of urban land spatial expansion (ULSE) (Xiao et al., 2006), or explorations of the temporal variation in the drivers of ULSE in single major cities (Li et al., 2013). Fewer studies have synthetically focused on the spatiotemporal differences in driving forces of ULSE of small cities and towns, especially those of different towns in the same administrative region. Such research is important because it could help us better understand the different effects of driving factors of ULSE in different areas, thus indicate policy regions where intervention is most urgently needed (Braimoh & Onishi, 2007), and design an effective urban planning and differentiated
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management strategies, so as to guide a rational spatial expansion of urban land, mitigate the adverse impacts such as the loss of quality farmland and ecological land. Meanwhile, the mechanisms of urban land expansion have been widely studied in developed countries, but less studied in developing countries (Zanganeh Shahraki et al., 2011), including China. China has entered a critical period of urbanization, in which urban land expands very fast, and a large part of expansion occurs in small cities and towns (Tan et al., 2005). This progression is very typical in the Yangtze River Delta region (Long, Tang, Li, & Heilig, 2007), the world’s sixth largest urban agglomeration (Liu, 2012), where the rapid expansion of urban land occurs not only in large cities, but also in small cities and towns, especially in some developed towns with special traffic conditions. For example, the port towns of Taicang City (one of the top ten most economically developed countylevel cities in China) have shown rapid expansion of urban land, accompanied by soaring economic development with the representativeness of China’s urban development, and most of other developing towns will experience similar urbanization process. However, no systematic study is available on the driving forces of ULSE from the view of town-scale in this area, though some other researches on land use change have been conducted (Long, Liu, Wu, & Dong, 2009). The research methods of urban land expansion driving forces mainly include multiple linear regression (MLR) (Seto et al., 2011), structural equation modeling (SEM) (Eboli, Forciniti, & Mazzulla, 2012), analytic hierarchy process (AHP) (Thapa & Murayama, 2010), system dynamics (SD) (He, Okada, Zhang, Shi, & Zhang, 2006), logistic regression (Li et al., 2013), etc. Among these, MLR and SEM are mainly applied to the area expansion of urban land, but are not applicable when dependent variables are categorical variables, or variables with spatial attributes (Menard, 2002). AHP is a subjective method, and it is also difficult to reflect the spatial information of urban land expansion. SD model can be coupled to cellular automata to include spatial dynamics (He et al., 2006), but still has difficulty in identifying factors and their effects (Liu, Li, & Yeh, 2006). Logistic regression may handle the regression problems of dependent variables as non-continuous variables, which require no linear relationship between dependent and independent variables. When combined with GIS, it could effectively reflect the variables’ spatial characteristics, and can be used to driving forces analysis and prediction of land use change (Hu & Lo, 2007; Li et al., 2013). In this study, the dependent variable (land urbanization) is a non-continuous binary variable, and each variable has a spatial attribute, therefore the logistic regression method is used. This study aims to explore the spatiotemporal dynamics of the driving factors’ effects on ULSE in different towns during different periods. Specifically, from the view of town-scale, which is the smallest administrative unit of land management in China, we attempt to address two questions: (1) Do ULSE factor combinations vary with periods and regions, and what’s the relative important driving factor? (2) What’s the spatiotemporal variation of various driving factors’ effects on ULSE? Taking the port towns of Taicang as examples, remote sensing (RS) images were used to analyze the urban land expansion process during three time periods (1989e 1995e2001e2008). Eight potential factors were selected from the aspects of natural eco-environment, land control policies, accessibility and neighborhood, and logistic regression models were applied to analyze the driving factors’ effects and their spatiotemporal variation. This will lead to a better understanding of driving mechanism of urban land expansion in China, and will be meaningful to differentiated control policy-making and urban land expansion simulation. Moreover, the results from this study will be a reference for other developing countries.
Methodology Study area The study area includes Huangjing Town, Fuqiao Town and Liuhe Town, with a total area of 31,506 ha, located in China’s most developed Yangtze River Delta, which is the sixth largest urban agglomeration in the world. The three towns, which are under the jurisdiction of Taicang City, are located on the south bank of the Yangtze River (Fig. 1), and are all port towns with convenient transportation conditions. As of 2008, the study area had a total population of 273,400, and a gross domestic product (GDP) of 22.13 billion Yuan RMB. Being port towns of Taicang, of which the socioeconomic development is subject to the impact of the ports, there are still some differences in industrial development and specific traffic conditions. Along with the rapid socio-economic development, the urban land area expanded to 5241.87 ha in 2008 from 676.43 ha in 1989, with an annual average growth rate of 11.38%. Potential driving forces By reviewing related literature, it may be summarized that the driving forces of ULSE mainly include natural eco-environment, accessibility, socio-economic development, neighborhood factors, relevant planning and policies (Deng, Huang, Rozelle, & Uchida, 2009; Dubovyk, Sliuzas, & Flacke, 2011; Hu & Lo, 2007; Li et al., 2013; Thapa & Murayama, 2010; Xiao et al., 2006). Among these factors, socio-economic development factors, such as increases in population and GDP, may promote ULSE (Wu & Zhang, 2012). However, those data for earlier years were denied, and their spatial resolution couldn’t achieve the requirements of logistic regression. The missing of those factors wouldn’t greatly affect the model’s performance (Li et al., 2013), therefore such factors were not selected in this study. According to the actual situation of the study area and data accessibility, eight factors from natural ecoenvironment, land control policies, accessibility and neighborhood were selected for analysis. Natural eco-environment factors Natural eco-environment is the fundamental determinant of ULSE. It includes topography, wetlands and other ecological sensitive areas (Batisani & Yarnal, 2009; Li et al., 2013). There are a dense river network, wetlands, ecological reserves and other ecological sensitive areas in the study area. Meanwhile, agricultural land, grassland, water and other types of land serve important ecological service functions (Costanza et al., 1997), and there is a fault zone located in Taicang City. Therefore, in order to comprehensively consider the impacts of natural eco-environment, the ecological suitability assessment method of urban land expansion based on variable weight (Shu, Huang, Liu, & Li, 2012) was used to evaluate the ecological sensitivity (ES). ES was then taken as a natural eco-environment factor. Due to the diversified ecoenvironment conditions during different periods, ESs of 1989, 1995 and 2001 were evaluated respectively. Six indicators, i.e. the slope, distance to riverbank, distance to the Yangtze River and reservoir shoreline, distance to the fault zone, land use status (according to the data in 1989, 1995 and 2001), and distance to ecologically sensitive areas, were selected for use in the evaluation. The results of existing research (Shu et al., 2012) were used for weight and parameter settings. Land control policies Relevant planning and policies, such as regional economic planning, urban planning and land control policies, can greatly affect ULSE (Li et al., 2013; Liu, Zhan, & Deng, 2005). Because some
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Fig. 1. Location of the study area.
factors such as urban planning and regional economic planning were difficult to obtain and be quantified throughout the study period, prime cropland1 protection policies with a close relationship to ULSE were selected. The Prime Cropland Protection Regulation (PCPR) has been implemented in China since October 1994. Therefore, the prime cropland (i.e. Pcropland; the prime cropland value is set as 1, while the non-prime cropland value is set as 0) was selected as a potential factor and applied to the two periods after 1995. Accessibility factors Accessibility is one of the main forces of ULSE, including the distances to roads, railways, ports, and various economic activity centers (Batisani & Yarnal, 2009; Dubovyk et al., 2011; Hu & Lo, 2007). Due to the fact that in the study area and during 1989e 2008, only rivers, ports, highways and main roads exist, the distances to town centers2 (Dctown), to main roads (Dmroad, roads in addition to thruways and branch roads in the towns), to ports and docks (Dpdock), to main river ways (Driver) and to exits of thruways (Dethruway) are selected. Among these, the thruways include the Riverside Thruway and SuzhoueKunshan Thruway, which were respectively constructed in 2004 and 2005, therefore the distance to exits of thruways may be used only for the period of 2001e2008. The data for 1995, 2001 and 2008 were used for main roads and ports; other factors show no significant changes throughout the study period, thus the same data are adopted for the entire period.
the status value of the pixel’s neighborhood factor varies, so neighborhood factors of 1989, 1995 and 2001 were calculated respectively. Data sources and data processing The data of this study involve land use, transportation and natural eco-environment. Among these, the Landsat TM RS images with the spatial resolution of 30 m, for the years of 1989, 1995, 2001 and 2008, were used for interpreting the urban land use data. The term urban land refers to the developed land covered by an impervious surface (e.g., residential land, industrial land, commercial land, roads) (Li et al., 2013). In the interpretation process, artificial interactive interpretation was implemented based on the ERDAS Imagine software. Field investigation and random sample check (Long et al., 2007) verified that the average interpretation accuracies were above 97%. And then the interpreted maps were
Neighborhood factors Neighborhood factors (Neig) also have impact on ULSE, the land unit is more likely to be developed into urban land if it is surrounded by more urban land (Cheng & Masser, 2003). In this study, the neighborhood factors are defined as the proportion of urban land area within a 7 7 pixel window for each pixel (a pixel is a grid with a 30 m resolution in a grid layer in this study). With the development of the pixel’s surrounding land,
1 Prime cropland is the arable land that is confirmed by land use planning and shall not be occupied according to the demand of agricultural products for the population and social economy during a specific period. 2 Each town has two centers or even more because of the mergence of small towns in the past.
Fig. 2. Urban land expansion in the study area from 1989 to 2008.
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overlapped with the boundaries of various towns, thus producing the land use vector graphics in 1989, 1995, 2001 and 2008. Further, the vector graphics were superimposed, and the results were converted into 30 m 30 m grid layers, so as to obtain the urban land expansion layer of each period (Fig. 2). Other factors, such as eco-sensitivity evaluation indicators, prime croplands, and accessibility factors, were derived from the land use status map, land use planning drawing (1997e2010), the 30 m resolution DEM, the waterway and road network status map, and ecological urban planning map of Taicang. The proximity factors mentioned above were calculated using the distance function in ArcGIS, and resampled into 30 m 30 m grid layers. Then, ecological suitability of the study area was evaluated, and the grid layers of ULSE factors were obtained (Fig. 3). Finally, the grid value of each factor was extracted and imported into SPSS software for merging and sampling. In order to ensure the model’s accuracy, first the samples which had land types in the base year during various periods classified as urban land were deleted. Then, the samples with the equal quantity of land
urbanization and non-urbanization were randomly extracted. At last, respective numbers of 7206, 11,504 and 21,358 groups of effective samples were obtained for the periods of 1989e1995, 1995e2001 and 2001e2008. Binary logistic regression Binary logistic regression is a non-linear statistical method of regression analysis for binary dependent variables (Menard, 2002). In this study, land urbanization (Y) is a binary dependent variable. When land is converted from non-urban land into urban land, its value is set as Y ¼ 1; otherwise, Y ¼ 0. Given ULSE has m drivers, i.e. x1, x2,., xm; the overall probability of Y ¼ 1 is P. The logistic regression model is shown below:
PðY ¼ 1jx1 ; x2 ; .; xm Þ . X X b i xi b i xi 1 þ exp b0 þ ¼ exp b0 þ
Fig. 3. Potential factors of urban land spatial expansion.
(1)
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logitPðY ¼ 1jx1 ; x2 ; .; xm Þ ¼ ln
P 1P
185
Spatiotemporal variation of the driving forces
¼ b0 þ b1 x1 þ / þ bm xm
The results of aforementioned collinearity test show that there is no significant collinearity in each model. The regression results (Table 2) show that the PCP of each model is ranged between 75.5% and 91.5%, ROC value reaches 0.84e0.96 (Fig. 4), and pseudo R2 values are all greater than 0.27. This indicates that all the models perform well and the models’ variables could effectively interpret the process of ULSE. As shown in Table 2, various factors’ regression coefficient (b) in each model are negative except for neighborhood factors, thus indicating that the larger those factors are, the smaller the possibility of land urbanization will be. In general, the factors and their impacts vary with towns and periods. These factors could be divided into common factors and special factors, of which the term common factors refers to those with impacts on ULSE in all periods or regions, and special factors refers to those with impacts on individual periods or regions.
(2)
where b0 is a constant, and bi (i ¼ 1, 2,., m) is the partial regression coefficient. After variable standardization, the obtained bi (i.e. standardized coefficients) may reflect the relative influence of each independent variable on the dependent variables (Menard, 2004). The larger the absolute value of bi is, the greater the impact on logit(P) will be. The interpretability of logistic regression results may be tested by means of Relative Operating Characteristics (ROC). The case of ROC 0.7 indicates that independent variables have strong interpretability on the dependent variable (Pontius & Schneider, 2001). Furthermore, the percent correct predictions (PCP) and pseudo R2 are used to interpret the predictive ability and fitting degree of the model respectively (Li et al., 2013; Menard, 2002). In the case where the pseudo R2 > 0.2, it shows a relatively good fit (Clark & Hosking, 1986). In order to eliminate the impact of collinearity among the variables on the logistic regression, and to compare the impacts of various factors, the Z-Score standardization method was used to process the variables, and multicollinearity test was carried out on independent variables. TOL (tolerance) >0.1 and VIF (variance inflation factor) <10 were taken as the standard (Ozdemir, 2011), to eliminate the samples with obvious collinearity. Finally, the binary logistic regression was applied for stepwise regression analysis on the sample data in various towns during each period. The significance of the variable sig.<0.1 was taken as the standard to eliminate variables with poor regression results, so as to obtain the relative impacts of various factors on ULSE.
Temporal differences The combination differences of ULSE factors, as well as the common factors and special factors during different periods in various towns are shown in Tables 2 and 3. For example, in Fuqiao the common factors during different periods include the neighborhood factors, the distances to ports and docks, to main river ways, while other factors are considered special factors. In Huangjing, the prime cropland and distances to exits of thruways are the special factors, and other factors are common ones. In Liuhe, the ecological sensitivity, distances to town centers and neighborhood factors are common factors, while other factors except distances to exits of thruways are special factors. The combination differences of those factors are mainly due to the variation of external environment of ULSE (e.g., government policies and development strategies). For example, due to the establishment of TPDZ in 1991, new urban land development occurred mainly near the Taicang Port rather than the main roads, thus the main roads had no significant effect in Fuqiao during 1989e1995. During the period of 1995e2001, the implementation of PCPR in October 1994 had significant restriction on ULSE. While in 2001e2008, the overall urban planning of Taicang (2001e2020) confirmed that Liuhe will focus on the development of tourism, which made new urban land expand along the main roads, and the influence of main river ways disappeared. And the thruways were constructed in 2005, thus their exits affected the adjacent Fuqiao and Huangjing during 2001e2008. The results in Table 3 also show that in Fuqiao, the relative importance of common factors during 1989e1995 is different from that in other periods because the distance to main river ways is more important than neighborhood factors during 1989e1995. In Huangjing, the relative importance of common factors is inconsistent during various periods, e.g., the distance to town centers is more important than the distance to main roads, which is opposite to that since 1995. However, in Liuhe the relative importance of common factors during 2001e2008 differs from that in the first two periods, and ecological sensitivity is more important than
Results Features of urban land expansion Over the past two decades, urban land of various towns expanded at different expansion speeds (i.e. annual expansion area) and growth rates (Table 1). Fuqiao has the largest expansion speed in each period, with an annual growth rate of 15.4% throughout the entire period. The expansion speed and growth rate increased from 40.8 ha/a and 13.2% in 1989e1995 to 328.9 ha/a and 18.3% in 2001e2008 respectively, this is mainly due to the development of Taicang Port development zone (TPDZ), and a large part of new urban land is located on the riverbank and involves port and industrial installations. Liuhe has the secondary expansion speed (38.4 ha/a), increasing from 25.1 ha/a in 1989e1995 to 61.2 ha/a in 2001e2008, especially in 2001e2008, its urban land expanded with an annual growth rate of 8.6%, and the expansion area accounted for 58.7% of that in the entire period. While Huangjing has the smallest expansion speed (38.3 ha/a), especially since 2001, the expansion speed and growth rate were the lowest among all three towns, even though they were greater than those of Liuhe before 2001.
Table 1 Urban land expansion in the study area from 1989 to 2008. Year
1989 1995 2001 2008
Urban land area (ha)
Time period
Huangjing
Fuqiao
Liuhe
206.36 348.93 619.38 933.21
220.86 465.48 1028.70 3330.72
249.21 399.69 549.90 977.94
1989e1995 1995e2001 2001e2008 1989e2008
Annual growth rate (%)
Annual expansion (ha/a)
Huangjing
Fuqiao
Liuhe
Huangjing
Fuqiao
Liuhe
9.2 10.0 6.0 8.3
13.2 14.1 18.3 15.4
8.2 5.5 8.6 7.5
23.8 45.1 44.8 38.3
40.8 93.9 328.9 163.7
25.1 25.0 61.2 38.4
0.244 2.310 1.412 0.837 86.8% 0.93 0.506 0.254 7.274 1.369 1.984 91.5% 0.96 0.658 0.615 2.986 0.486 1.094 84.6% 0.94 0.538
0.127 0.205 0.616 2.063 1.586 0.484
0.725 3.776 0.400 2.031 0.915 0.708 86.1% 0.93 0.524
0.321 1.329 79.9% 0.89 0.412
0.070 e 0.064 2.653 e 2.745
The degree of freedom of various variables are all 1; N indicates that the variable does not pass the test of significance; “e” indicates that the regression coefficient of the variable has no practical significance; and a ¼ significant at 10% level, and significant at 1% level for the rest; b indicates the standardized regression coefficient; OR indicates the odds ratio.
0.108 N 0.485 2.223 N 0.723
0.637 1.608 90.0% 0.96 0.608
0.529 4.994
0.310 0.369 0.906 0.494 1.250 0.258 e e 0.244 79.9% 0.86 0.328
b OR
0.743 0.298 0.755 0.113 0.398 N 0.296 1.212 0.281 2.183 0.921 N
b OR
0.763 0.860 0.270 0.151a
b OR
0.415 0.663 0.633 0.293 0.264 0.377 0.610 0.413 1.666 0.880 0.582 0.577 1.452 1.585 1.344 0.809 1.100 0.354 85.0% 0.91 0.482
b OR
0.251 0.162 0.511 0.207 0.059 0.281 1.383 1.821 0.670 1.577 2.826 1.271
b OR
0.525 0.809 0.644 0.212a
b OR
0.861 0.834 0.407 e 0.524 0.386 0.762 0.937 1.521 0.150 0.181 0.899 e 0.647 0.953 0.272 0.065 0.419 75.5% 0.84 0.279
b OR
1.014 0.611 0.435 0.505 0.840 0.218 0.014 0.492 0.832 0.682 0.174 1.523 0.480 N
b OR
0.619 N
b
1989e1995 Period
Constant ES Pcropland Dctown Dmroad Dpdock Dethruway Driver Neig PCP ROC Pseudo R2
2001e2008 1995e2001 1989e1995
Liuhe
2001e2008 1995e2001 1989e1995
Huangjing
2001e2008 1995e2001 Fuqiao Town
Table 2 Summary of the logistic regression models.
0.734 0.692 0.404 0.610 0.286 0.773 e e 1.277
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OR
186
neighborhood factors. In addition, special factors’ influences also vary with time. For example, in Fuqiao during 1995e2001, ecological sensitivity is more important than the distance to main roads, which is opposite to that during 2001e2008. Similar results exist in Huangjing and Liuhe. The above variation of factors’ relative influences in different time periods is also related to the phase characteristics of socioeconomic development. According to the change characteristics of various factors’ influences, those driving factors can be divided into three categories. The first category is of increasing importance, e.g., the prime cropland and distance to main roads. Since the implementation of PCPR in 1994, the prime cropland has had constraint effect on ULSE. And with the further decrease of cropland, the implementation of other related policies (e.g., the Pre-trial Management Approach for Construction Projects in July 2001) enhanced the influence of prime cropland, especially in the fastdeveloping regions, i.e. Fuqiao and Liuhe. The second one is of decreasing importance, e.g., the distances to main river ways and to town centers. Due to the development of highway traffic facilities, the traffic function of inland waterways became relatively weaker, thus the main river ways had decreasing effect on ULSE, especially in Liuhe during 2001e2008. The last one is of fluctuant influences, e.g., neighborhood factors, ecological sensitivity and the distance to ports and docks. The influence of neighborhood factors increased in 1995e2001 and then decreased in 2001e2008. This is due to the urban land expanded in the alternate modes of leapfrogging and infilling in those towns. Spatial differences As viewed from the perspective of spatial differences, the factors and their effects in different towns differ as well (Table 2, Fig. 5). This can be attributed to the differences of development environment in different regions. During 1989e1995, the common factors impacting various towns include neighborhood factors, the distances to town centers, to ports and docks and to main river ways. Those factors’ relative importance varies with towns, e.g., the distance to ports and docks is the most important factor in Fuqiao, while its influence is weaker than the distances to town centers. It is because the Taicang Port, which is located in Fuqiao, was more important than other factors for the development of TPDZ and urban land. While other towns owned less ports and docks, which were not the primary transportation mode, thus new urban land was mainly developed around the town centers. However, ecological sensitivity and the distances to main roads are considered special factors, of which ecological sensitivity only impacts Huangjing and Liuhe with the weakest influence, and its regression coefficients are significant merely at the 10% level; the latter one only impacts Huangjing, and its importance is secondary to the distance to town centers. This is because the importance of ecoenvironment was generally ignored at the early stage of economic development; this was obviously shown in Fuqiao during its development of TPDZ. Meanwhile, the main roads system was imperfect in Fuqiao and Liuhe, thus it was of little influence on urban land development in those towns. During 1995e2001, the factors in addition to the distances to exits of thruways and to ports and docks are common factors for every town. Their relative importance also varies with regions. For example, the distance to main roads is the most important factor in Huangjing, while it’s the weakest factor in Fuqiao and medium one in Liuhe. This is due to the improvement of main roads system in this period, but with the development of TPDZ, most of the new urban land in Fuqiao was still developed adjacent to the ports, while in Liuhe, urban land development mainly occurred around town centers. The distance to ports and docks is a special factor. It only impacts the ULSE in Fuqiao and Huangjing, and it’s the most
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Fig. 4. ROC curves of the logistic regression model in various towns during different periods.
thruways. Main river ways only impact Fuqiao and Huangjing, and it is more important than ecological sensitivity and prime croplands in Huangjing, which is opposite to that in Fuqiao.
important factor in Fuqiao. This is due to Taicang Port and some other docks were very important for the development of TPDZ in Fuqiao, and Huangjing also had several docks, while there was no dock in Liuhe. During 2001e2008, the distances to town centers, to exits of thruways and to main river ways are considered special factors, while the others are common factors. The relative importance of common factors is different in three towns. For example, the distance to ports and docks is more important than distance to main roads in Fuqiao, which is opposite to that in Liuhe and Huangjing. This is because the Taicang Port in Fuqiao was deemed as the No. 1 foreign trade port and a key project by Jiangsu Province according to Development Strategy Along the Yangtze River (DSAYR) in 2003, most of the new urban land development occurred near Taicang Port to develop TPDZ, while other towns, which owned fewer docks, had greater urban land expansion near main roads, though they were affected by the development of Taicang Port. Among the special factors, the distance to town centers only impacts Huangjing and Liuhe, and it is more important than prime croplands in Huangjing, which is opposite to that in Liuhe. The exits of thruways constructed in 2005 only impact adjacent Fuqiao and Huangjing slightly, which is due to the fact that development of TPDZ and Taicang Port brought a great demand for high speed transportation, and the development of Huangjing’s docks also needed transportation. However, Liuhe is located far away from the exits of
Discussion Theoretical implications It was found that all the potential factors have impacts on ULSE in the towns, but the combination of driving factors as well as the relative importance of common and special factors varied with towns and periods. Those differences exist because of diversified external environments of ULSE in different regions during various periods. Among the four types of variables, accessibility is the most important, which is consistent with the views of Li et al. (Dubovyk et al., 2011; Li et al., 2013). However, most of the following detail findings are seldom reported or different from that in previous studies focusing on large cities. Accessibility factors: We found that all the accessibility factors had significant negative effects on ULSE in various areas except for that in some periods, their relative influences not only varied with time, which is consistent with previous studies (Li et al., 2013), but also varied with regions. It was shown that all the towns were affected by the ports and docks, however, it was only in Fuqiao, in which the Taicang Port is located, the distance to ports and docks
Table 3 The rank order of the factors’ relative influences in different logistic models. Town
Fuqiao
Period
1989e1995
1995e2001
2001e2008
1989e1995
1995e2001
2001e2008
1989e1995
1995e2001
2001e2008
6 2
6
4 2 3 1 5
3 4
6 2
4 3
5 2
6 7 2 1 3 5 4 8
3 6 1 4
1
1 2 5
3 7 4 1 6
5
2
5 3 4 7 1
ES Pcropland Dctown Dmroad Dpdock Dethruway Driver Neig
Huangjing
3 1 5 7 4
Liuhe
The bold and underlined numbers indicate that the factors are the common factors in the same town.
1 4 2 3
5 2
6
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Fig. 5. Contrast among the absolute value of b coefficients in various towns during different periods.
was the most important factor during the entire study period, and the relative influences of distances to town centers and to main roads were weaker. While in other towns where roads were the major transportation routes, the relative influences of town centers and main roads were more important. And with the expansion of urban land, the influence of town centers decreased and main roads became increasingly important. This proves that the guidance of major traffic elements on ULSE is a basic pattern, which is similar to the conclusions of relevant studies (Poelmans & Van Rompaey, 2010). Results also showed that the influence of river ways on ULSE was becoming much weaker. This may be due to the fact that other traffic means such as road transport have advantages over river ways in inland transportation, especially under the requirement of speed transportation for economic development, increasing new urban land will be located adjacent to major traffic elements other than river ways. Besides, thruways also had impacts on ULSE, but they were much more important to the adjacent fast-developing regions, where the development of economy brought a great demand for transportation. Thus, the exits of thruways affected ULSE in those areas. Other factors: With the process of urbanization, the influence of ecological sensitivity first enhanced and then weakened, thus indicates that the natural eco-environment has not been given adequate attentions under the temptation of economic benefits of urbanization in China. Prime croplands had impacts on various towns since the implementation of PCPR in October 1994, and it has increasingly effects on the fast-growing towns, thus indicates that the faster the urban land expand, the stronger the land control policies’ effects will be, and those policies have effective effects on urban sprawl controlling (Tan et al., 2005). The importance of neighborhood factors also first increased and then decreased in various towns, it is different from that found in Beijing (Li et al., 2013). This may be attributed to that the study areas are not in the same size, and some factors such as prime croplands are not considered into the models in previous studies; besides, it is not proper to explain the relative influence of the independent variables on the dependent variable according to the unstandardized coefficients or statistical significance (Menard, 2002, 2004). It also reveals that the patterns of ULSE in Chinese small cities and towns alternate between leapfrogging growth and infilling growth, which is a form of low-density urban sprawl. Methodological implications In this study, comparing the existing research, the spatiotemporal differences in the driving forces of ULSE in various mediumsized regions during different periods were discussed based on standardized logistic regression coefficients. This enriches the research perspective of the driving mechanism of urban land expansion. It may provide decision support to develop differentiated urban land expansion control policies in the study area. This is due to the fact that many studies focus on the land expansion drive of a single large city, while the study area may be subdivided into
several sub-administrative areas, and the driving mechanisms in small areas may differ slightly or even greatly. Therefore, such generalized researches may mask the differences of smaller regions, thus leading to policies according to the principle of “one size fits all”. In addition, this study is beneficial to research of dynamic simulation of urban land expansion, which is a hot research topic based on thorough understanding of urban land expansion mechanisms. This study shows that spatiotemporal variation exists in the driving mechanisms of ULSE, so the simulation results could be much more rational if the simulation model is established and conducted with some spatiotemporal different simulation rules rather than constant rules in a large area. Furthermore, unlike previous studies, natural eco-environment factors and land control policies were considered in this study, thus the regression models are more close to the actual urban land expansion in China. The values of PCP and ROC in various models are high and very close to the results of relevant studies (Dubovyk et al., 2011; Li et al., 2013), and the pseudo R2 values are also greater than 0.27, thus indicating that the main factors were included in the models. Management and policies implications In this study, we found that accessibility factors especially the distances to town centers, to ports and docks, to main roads, have important effects on ULSE, but the relative magnitude of the unique effects varied with periods and areas. Those factors could be affected by the economic development policies (e.g., DSAYR) and related planning (e.g., traffic planning), thus government’s behavior can be seemed as the original impetus for ULSE. Therefore, in order to make urban land expand rationally, those related policies and planning should be made scientifically. For example, in urban planning, it is necessary to strengthen the scientific basis of traffic planning and town centers planning, which is a long-term strategy for the suppression of disordered urban sprawl. Of course, the dominant factors of ULSE should be grasped, and the common and special factors should be considered comprehensively, so as to develop differentiated land use policies. For example, the planning and development of ports and docks should be given much more attention to guide the rational ULSE in Fuqiao, while in Huangjing and Liuhe, scientific road traffic planning should be emphasized, so as to strengthen the development coordination between the port towns. Meanwhile, policies such as PCPR and ecological conservation are also another aspect of government’s behavior, which plays a restraining role on urban sprawl. For example, after the implementation of PCPR, China has gradually established the world’s most stringent farmland protection system. Prime cropland protection has a positive role in guiding expansion of urban land, but its effect depends largely on the implementation (Long et al., 2007). Therefore, it is necessary to further strengthen the enforcement of those policies, establish a mechanism of efficient urban land use through strict constraint of ULSE, and thus reduce the loss of
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cropland. It also shows that eco-environment hasn’t attracted sufficient attention yet, thus results in some serious ecoenvironmental problems. Therefore, eco-environment conservation should be strengthened in urban planning, especially in the fast-developing areas. In addition, the results also indicate that the ULSE pattern of low-density sprawl exists in Chinese small cities and towns, it is adverse to the intensive utilization of land, thus measures such as urban growth boundary (UGB) (Long et al., 2007) should be introduced to control urban sprawl in those areas. In general, ULSE is affected by many factors, but most of them can be affected by government’s behavior, i.e. government can not only stimulate urban land development but also constrain urban land expansion in spatial. Therefore, while formulating related policies and planning, government should pre-assess their effects on ULSE, and seek a winewin in the dilemma of socio-economic development and farmland protection. Meanwhile, differentiated policies should be formulated according to differences in various regions during different phases. Conclusions By using binary logistic regression, we analyzed the spatiotemporal differences among the driving forces of ULSE in the three port towns of Taicang City, and some distinctive features behind ULSE at the town-scale were revealed. This study extends our understanding of urban land expansion especially in towns in China, thus is meaningful to policy-making and other related researches. The results show that combinations of driving factors of ULSE, as well as various factors’ relative influences vary with periods or regions. The accessibility is always the dominant driver, in which the distances to town centers, to main roads are the dominant factors in Huangjing and Liuhe, while the distance to ports and docks is always the most important factor in Fuqiao. Meanwhile, the influences of town centers and river ways decrease and main roads become increasingly important. However, the exits of thruways only affect the adjacent fast-developing regions. In addition, China’s specific land control policy (prime cropland protection) has an important influence on ULSE especially in fast-developing areas since its implementation. What’s more, the variation of natural ecoenvironment factor (ecological sensitivity) and neighborhood factors indicates that urban land expands in the form of low-density sprawl in Chinese small cities and towns, without giving adequate attentions to the eco-environment. China has entered a period of dramatic urbanization. The spatial expansion of urban land in towns is an important aspect of urban land expansion, irrational ULSE in those areas will threaten the protection of eco-environment and farmland in China. In order to ensure the sustainable urbanization, spatiotemporal variation of the driving forces should be understood, so as to formulate differentiated ULSE management policies. Besides, we suggest that the government should strengthen its guidance, and some countermeasures from aspects of scientific planning of regional traffic networks and town centers, UGB, effective protection of prime croplands and natural eco-environment, etc. should be made, so as to guide reasonable urban land expansion. In this study, only eight potential factors were selected. In the case of available data, it is also required to further add relevant factors. In addition, 30 m 30 m grids were used for sample processing on the data. Although the results of the regression models are desirable, relevant studies have shown that the fitting results of the logistic regression model will vary in different grid scales (Olaniyi, Abdullah, Ramli, & Alias, 2012). Therefore, sensitivity impacts of grid scales on ULSE driving forces in future research would be worthwhile.
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Acknowledgments This study is supported by the National Natural Science Foundation of China (_501100001809) (No. 41101546 and No. 41201386) and the Natural Science Foundation of Jiangsu Province (No. BK20131008). We sincerely thank two anonymous reviewers for their constructive comments and suggestions. References Batisani, N., & Yarnal, B. (2009). Urban expansion in Centre County, Pennsylvania: spatial dynamics and landscape transformations. Applied Geography, 29, 235e 249. Braimoh, A. K., & Onishi, T. (2007). Spatial determinants of urban land use change in Lagos, Nigeria. Land Use Policy, 24, 502e515. Cheng, J., & Masser, I. (2003). Urban growth pattern modeling: a case study of Wuhan city, PR China. Landscape and Urban Planning, 62, 199e217. Clark, W. A., & Hosking, P. L. (1986). Statistical methods for geographers. New York: Wiley. Costanza, R., d’Arge, R., De Groot, R., Farber, S., Grasso, M., Hannon, B., et al. (1997). The value of the world’s ecosystem services and natural capital. Nature, 387, 253e260. Deng, X., Huang, J., Rozelle, S., & Uchida, E. (2008). Growth, population and industrialization, and urban land expansion of China. Journal of Urban Economics, 63, 96e115. Deng, X., Huang, J., Rozelle, S., & Uchida, E. (2009). Economic growth and the expansion of urban land in China. Urban Studies, 47, 813e843. Dubovyk, O., Sliuzas, R., & Flacke, J. (2011). Spatio-temporal modelling of informal settlement development in Sancaktepe district, Istanbul, Turkey. ISPRS Journal of Photogrammetry and Remote Sensing, 66, 235e246. Eboli, L., Forciniti, C., & Mazzulla, G. (2012). Exploring land use and transport interaction through structural equation modelling. Procedia e Social and Behavioral Sciences, 54, 107e116. He, C., 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. Applied Geography, 26, 323e345. Hu, Z., & Lo, C. P. (2007). Modeling urban growth in Atlanta using logistic regression. Computers, Environment and Urban Systems, 31, 667e688. Jiang, L., Deng, X., & Seto, K. C. (2012). Multi-level modeling of urban expansion and cultivated land conversion for urban hotspot counties in China. Landscape and Urban Planning, 108, 131e139. Li, X., Zhou, W., & Ouyang, Z. (2013). Forty years of urban expansion in Beijing: what is the relative importance of physical, socioeconomic, and neighborhood factors? Applied Geography, 38, 1e10. Liu, H. (2012). Comprehensive carrying capacity of the urban agglomeration in the Yangtze River Delta, China. Habitat International, 36, 462e470. Liu, X., Li, X., & Yeh, A. G.-O. (2006). Multi-agent systems for simulating spatial decision behaviors and land-use dynamics. Science in China (Series D: Earth Sciences), 49, 1184e1194. Liu, J., Zhan, J., & Deng, X. (2005). Spatio-temporal patterns and driving forces of urban land expansion in China during the economic reform era. AMBIO: A Journal of the Human Environment, 34, 450e455. Long, H., Liu, Y., Wu, X., & Dong, G. (2009). Spatio-temporal dynamic patterns of farmland and rural settlements in SueXieChang region: implications for building a new countryside in coastal China. Land Use Policy, 26, 322e 333. Long, H., Tang, G., Li, X., & Heilig, G. K. (2007). Socio-economic driving forces of land-use change in Kunshan, the Yangtze River Delta economic area of China. Journal of Environmental Management, 83, 351e364. Menard, S. (2002). Applied logistic regression analysis. Thousand Oaks, CA: Sage Publications. Menard, S. (2004). Six approaches to calculating standardized logistic regression coefficients. The American Statistician, 58, 218e223. Miller, M. D. (2012). The impacts of Atlanta’s urban sprawl on forest cover and fragmentation. Applied Geography, 34, 171e179. Olaniyi, A. O., Abdullah, A. M., Ramli, M. F., & Alias, M. S. (2012). Assessment of drivers of coastal land use change in Malaysia. Ocean & Coastal Management, 67, 113e123. Ozdemir, A. (2011). Using a binary logistic regression method and GIS for evaluating and mapping the groundwater spring potential in the Sultan Mountains (Aksehir, Turkey). Journal of Hydrology, 405, 123e136. Pielke, R. A., Sr. (2005). Land use and climate change. Science, 310, 1625e1626. Poelmans, L., & Van Rompaey, A. (2010). Complexity and performance of urban expansion models. Computers, Environment and Urban Systems, 34, 17e27. Pontius, R. G., Jr., & Schneider, L. C. (2001). Land-cover change model validation by an ROC method for the Ipswich watershed, Massachusetts, USA. Agriculture, Ecosystems & Environment, 85, 239e248. Seto, K. C., Fragkias, M., Guneralp, B., & Reilly, M. K. (2011). A meta-analysis of global urban land expansion. PLoS ONE, 6, e23777. Shu, B., Huang, Q., Liu, Y., & Li, Y. (2012). Spatial fuzzy assessment of ecological suitability for urban land expansion based on variable weights: a case study of taicang. Journal of Natural Resources, 27, 402e412 (in Chinese).
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Spatiotemporal variation analysis of driving forces of urban land spatial expansion using logistic regression: A case study of port towns in Taicang City, China Bangrong Shu a, *, Honghui Zhang b, Yongle Li c, Yi Qu d, Lihong Chen a a
School of Geodesy and Geomatics, Jiangsu Normal University, Xuzhou 221116, China School of Geographic and Oceanographic Sciences, Nanjing University, Nanjing 210093, China School of Public Administration, Nanjing University of Finance & Economics, Nanjing 210023, China d College of Public Management, Nanjing Agricultural University, Nanjing 210095, China b c
a r t i c l e i n f o
a b s t r a c t
Article history: Available online
Understanding the driving mechanisms of urban land spatial expansion (ULSE) is crucial for the guidance of rational urban land expansion. Previous studies have primarily focused on single large cities, with few explorations of the spatiotemporal differences in driving forces of ULSE of different towns in the same administrative region. This study aims to fill this gap. Three port towns of Taicang, located in China’s Yangtze River Delta region, were taken as examples to analyze the expansion process of urban land during 1989e2008. Eight factors, including ecological suitability, prime croplands, etc., were selected from four aspects of natural eco-environment, land control policies, accessibility and neighborhood. Binary logistic regression was employed to investigate the effects of various factors on ULSE during various periods in different regions. Results reveal that over the past two decades, urban land expanded rapidly in the three towns, but with different expansion speeds and growth rates. Diversified ULSE factor combinations exist during different periods in different regions, and the factors’ relative importance also varies with time and space. The four types of driving factors simultaneously affect ULSE, among which the accessibility is dominant. Based on the findings, we suggest that differentiated policies should be formulated to guide reasonable expansion of urban land. This study can help us better understand the driving mechanism of urban land expansion in small cities and towns, thus has important implications for urban planning and management in China and similar countries. Ó 2014 Elsevier Ltd. All rights reserved.
Keywords: Driving forces Spatiotemporal variation Urban land spatial expansion Logistic regression China
Introduction Land use/cover change (LUCC) has become one of the key issues in global change research (Pielke, 2005). As one of the cores of LUCC research, urban land expansion is related to sustainable regional socio-economic development. In particular, the loss of forest (Miller, 2012), cropland (Tan, Li, Xie, & Lu, 2005), as well as regional climate change (Pielke, 2005) and other problems caused by urban land expansion have become the focus of attention. In order to reduce the negative effects of urban land expansion, it is necessary to develop effective planning and management strategies, and its foundation is the clarification of the driving mechanisms of urban land expansion (Braimoh & Onishi, 2007; Li, Zhou, & Ouyang, 2013).
* Corresponding author. Tel.: þ86 13852434645. E-mail address: [email protected] (B. Shu). http://dx.doi.org/10.1016/j.habitatint.2014.02.004 0197-3975/Ó 2014 Elsevier Ltd. All rights reserved.
Many studies have been conducted to understand the driving forces of urban land expansion around the world (Deng, Huang, Rozelle, & Uchida, 2008; Li et al., 2013; Seto, Fragkias, Guneralp, & Reilly, 2011). However, urban land expansion involves two aspects, i.e. area and space. Previous studies have mainly focused on the drivers of urban land area expansion (Jiang, Deng, & Seto, 2012; Wu & Zhang, 2012) and the descriptive analysis on the driving forces of urban land spatial expansion (ULSE) (Xiao et al., 2006), or explorations of the temporal variation in the drivers of ULSE in single major cities (Li et al., 2013). Fewer studies have synthetically focused on the spatiotemporal differences in driving forces of ULSE of small cities and towns, especially those of different towns in the same administrative region. Such research is important because it could help us better understand the different effects of driving factors of ULSE in different areas, thus indicate policy regions where intervention is most urgently needed (Braimoh & Onishi, 2007), and design an effective urban planning and differentiated
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management strategies, so as to guide a rational spatial expansion of urban land, mitigate the adverse impacts such as the loss of quality farmland and ecological land. Meanwhile, the mechanisms of urban land expansion have been widely studied in developed countries, but less studied in developing countries (Zanganeh Shahraki et al., 2011), including China. China has entered a critical period of urbanization, in which urban land expands very fast, and a large part of expansion occurs in small cities and towns (Tan et al., 2005). This progression is very typical in the Yangtze River Delta region (Long, Tang, Li, & Heilig, 2007), the world’s sixth largest urban agglomeration (Liu, 2012), where the rapid expansion of urban land occurs not only in large cities, but also in small cities and towns, especially in some developed towns with special traffic conditions. For example, the port towns of Taicang City (one of the top ten most economically developed countylevel cities in China) have shown rapid expansion of urban land, accompanied by soaring economic development with the representativeness of China’s urban development, and most of other developing towns will experience similar urbanization process. However, no systematic study is available on the driving forces of ULSE from the view of town-scale in this area, though some other researches on land use change have been conducted (Long, Liu, Wu, & Dong, 2009). The research methods of urban land expansion driving forces mainly include multiple linear regression (MLR) (Seto et al., 2011), structural equation modeling (SEM) (Eboli, Forciniti, & Mazzulla, 2012), analytic hierarchy process (AHP) (Thapa & Murayama, 2010), system dynamics (SD) (He, Okada, Zhang, Shi, & Zhang, 2006), logistic regression (Li et al., 2013), etc. Among these, MLR and SEM are mainly applied to the area expansion of urban land, but are not applicable when dependent variables are categorical variables, or variables with spatial attributes (Menard, 2002). AHP is a subjective method, and it is also difficult to reflect the spatial information of urban land expansion. SD model can be coupled to cellular automata to include spatial dynamics (He et al., 2006), but still has difficulty in identifying factors and their effects (Liu, Li, & Yeh, 2006). Logistic regression may handle the regression problems of dependent variables as non-continuous variables, which require no linear relationship between dependent and independent variables. When combined with GIS, it could effectively reflect the variables’ spatial characteristics, and can be used to driving forces analysis and prediction of land use change (Hu & Lo, 2007; Li et al., 2013). In this study, the dependent variable (land urbanization) is a non-continuous binary variable, and each variable has a spatial attribute, therefore the logistic regression method is used. This study aims to explore the spatiotemporal dynamics of the driving factors’ effects on ULSE in different towns during different periods. Specifically, from the view of town-scale, which is the smallest administrative unit of land management in China, we attempt to address two questions: (1) Do ULSE factor combinations vary with periods and regions, and what’s the relative important driving factor? (2) What’s the spatiotemporal variation of various driving factors’ effects on ULSE? Taking the port towns of Taicang as examples, remote sensing (RS) images were used to analyze the urban land expansion process during three time periods (1989e 1995e2001e2008). Eight potential factors were selected from the aspects of natural eco-environment, land control policies, accessibility and neighborhood, and logistic regression models were applied to analyze the driving factors’ effects and their spatiotemporal variation. This will lead to a better understanding of driving mechanism of urban land expansion in China, and will be meaningful to differentiated control policy-making and urban land expansion simulation. Moreover, the results from this study will be a reference for other developing countries.
Methodology Study area The study area includes Huangjing Town, Fuqiao Town and Liuhe Town, with a total area of 31,506 ha, located in China’s most developed Yangtze River Delta, which is the sixth largest urban agglomeration in the world. The three towns, which are under the jurisdiction of Taicang City, are located on the south bank of the Yangtze River (Fig. 1), and are all port towns with convenient transportation conditions. As of 2008, the study area had a total population of 273,400, and a gross domestic product (GDP) of 22.13 billion Yuan RMB. Being port towns of Taicang, of which the socioeconomic development is subject to the impact of the ports, there are still some differences in industrial development and specific traffic conditions. Along with the rapid socio-economic development, the urban land area expanded to 5241.87 ha in 2008 from 676.43 ha in 1989, with an annual average growth rate of 11.38%. Potential driving forces By reviewing related literature, it may be summarized that the driving forces of ULSE mainly include natural eco-environment, accessibility, socio-economic development, neighborhood factors, relevant planning and policies (Deng, Huang, Rozelle, & Uchida, 2009; Dubovyk, Sliuzas, & Flacke, 2011; Hu & Lo, 2007; Li et al., 2013; Thapa & Murayama, 2010; Xiao et al., 2006). Among these factors, socio-economic development factors, such as increases in population and GDP, may promote ULSE (Wu & Zhang, 2012). However, those data for earlier years were denied, and their spatial resolution couldn’t achieve the requirements of logistic regression. The missing of those factors wouldn’t greatly affect the model’s performance (Li et al., 2013), therefore such factors were not selected in this study. According to the actual situation of the study area and data accessibility, eight factors from natural ecoenvironment, land control policies, accessibility and neighborhood were selected for analysis. Natural eco-environment factors Natural eco-environment is the fundamental determinant of ULSE. It includes topography, wetlands and other ecological sensitive areas (Batisani & Yarnal, 2009; Li et al., 2013). There are a dense river network, wetlands, ecological reserves and other ecological sensitive areas in the study area. Meanwhile, agricultural land, grassland, water and other types of land serve important ecological service functions (Costanza et al., 1997), and there is a fault zone located in Taicang City. Therefore, in order to comprehensively consider the impacts of natural eco-environment, the ecological suitability assessment method of urban land expansion based on variable weight (Shu, Huang, Liu, & Li, 2012) was used to evaluate the ecological sensitivity (ES). ES was then taken as a natural eco-environment factor. Due to the diversified ecoenvironment conditions during different periods, ESs of 1989, 1995 and 2001 were evaluated respectively. Six indicators, i.e. the slope, distance to riverbank, distance to the Yangtze River and reservoir shoreline, distance to the fault zone, land use status (according to the data in 1989, 1995 and 2001), and distance to ecologically sensitive areas, were selected for use in the evaluation. The results of existing research (Shu et al., 2012) were used for weight and parameter settings. Land control policies Relevant planning and policies, such as regional economic planning, urban planning and land control policies, can greatly affect ULSE (Li et al., 2013; Liu, Zhan, & Deng, 2005). Because some
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Fig. 1. Location of the study area.
factors such as urban planning and regional economic planning were difficult to obtain and be quantified throughout the study period, prime cropland1 protection policies with a close relationship to ULSE were selected. The Prime Cropland Protection Regulation (PCPR) has been implemented in China since October 1994. Therefore, the prime cropland (i.e. Pcropland; the prime cropland value is set as 1, while the non-prime cropland value is set as 0) was selected as a potential factor and applied to the two periods after 1995. Accessibility factors Accessibility is one of the main forces of ULSE, including the distances to roads, railways, ports, and various economic activity centers (Batisani & Yarnal, 2009; Dubovyk et al., 2011; Hu & Lo, 2007). Due to the fact that in the study area and during 1989e 2008, only rivers, ports, highways and main roads exist, the distances to town centers2 (Dctown), to main roads (Dmroad, roads in addition to thruways and branch roads in the towns), to ports and docks (Dpdock), to main river ways (Driver) and to exits of thruways (Dethruway) are selected. Among these, the thruways include the Riverside Thruway and SuzhoueKunshan Thruway, which were respectively constructed in 2004 and 2005, therefore the distance to exits of thruways may be used only for the period of 2001e2008. The data for 1995, 2001 and 2008 were used for main roads and ports; other factors show no significant changes throughout the study period, thus the same data are adopted for the entire period.
the status value of the pixel’s neighborhood factor varies, so neighborhood factors of 1989, 1995 and 2001 were calculated respectively. Data sources and data processing The data of this study involve land use, transportation and natural eco-environment. Among these, the Landsat TM RS images with the spatial resolution of 30 m, for the years of 1989, 1995, 2001 and 2008, were used for interpreting the urban land use data. The term urban land refers to the developed land covered by an impervious surface (e.g., residential land, industrial land, commercial land, roads) (Li et al., 2013). In the interpretation process, artificial interactive interpretation was implemented based on the ERDAS Imagine software. Field investigation and random sample check (Long et al., 2007) verified that the average interpretation accuracies were above 97%. And then the interpreted maps were
Neighborhood factors Neighborhood factors (Neig) also have impact on ULSE, the land unit is more likely to be developed into urban land if it is surrounded by more urban land (Cheng & Masser, 2003). In this study, the neighborhood factors are defined as the proportion of urban land area within a 7 7 pixel window for each pixel (a pixel is a grid with a 30 m resolution in a grid layer in this study). With the development of the pixel’s surrounding land,
1 Prime cropland is the arable land that is confirmed by land use planning and shall not be occupied according to the demand of agricultural products for the population and social economy during a specific period. 2 Each town has two centers or even more because of the mergence of small towns in the past.
Fig. 2. Urban land expansion in the study area from 1989 to 2008.
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overlapped with the boundaries of various towns, thus producing the land use vector graphics in 1989, 1995, 2001 and 2008. Further, the vector graphics were superimposed, and the results were converted into 30 m 30 m grid layers, so as to obtain the urban land expansion layer of each period (Fig. 2). Other factors, such as eco-sensitivity evaluation indicators, prime croplands, and accessibility factors, were derived from the land use status map, land use planning drawing (1997e2010), the 30 m resolution DEM, the waterway and road network status map, and ecological urban planning map of Taicang. The proximity factors mentioned above were calculated using the distance function in ArcGIS, and resampled into 30 m 30 m grid layers. Then, ecological suitability of the study area was evaluated, and the grid layers of ULSE factors were obtained (Fig. 3). Finally, the grid value of each factor was extracted and imported into SPSS software for merging and sampling. In order to ensure the model’s accuracy, first the samples which had land types in the base year during various periods classified as urban land were deleted. Then, the samples with the equal quantity of land
urbanization and non-urbanization were randomly extracted. At last, respective numbers of 7206, 11,504 and 21,358 groups of effective samples were obtained for the periods of 1989e1995, 1995e2001 and 2001e2008. Binary logistic regression Binary logistic regression is a non-linear statistical method of regression analysis for binary dependent variables (Menard, 2002). In this study, land urbanization (Y) is a binary dependent variable. When land is converted from non-urban land into urban land, its value is set as Y ¼ 1; otherwise, Y ¼ 0. Given ULSE has m drivers, i.e. x1, x2,., xm; the overall probability of Y ¼ 1 is P. The logistic regression model is shown below:
PðY ¼ 1jx1 ; x2 ; .; xm Þ . X X b i xi b i xi 1 þ exp b0 þ ¼ exp b0 þ
Fig. 3. Potential factors of urban land spatial expansion.
(1)
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logitPðY ¼ 1jx1 ; x2 ; .; xm Þ ¼ ln
P 1P
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Spatiotemporal variation of the driving forces
¼ b0 þ b1 x1 þ / þ bm xm
The results of aforementioned collinearity test show that there is no significant collinearity in each model. The regression results (Table 2) show that the PCP of each model is ranged between 75.5% and 91.5%, ROC value reaches 0.84e0.96 (Fig. 4), and pseudo R2 values are all greater than 0.27. This indicates that all the models perform well and the models’ variables could effectively interpret the process of ULSE. As shown in Table 2, various factors’ regression coefficient (b) in each model are negative except for neighborhood factors, thus indicating that the larger those factors are, the smaller the possibility of land urbanization will be. In general, the factors and their impacts vary with towns and periods. These factors could be divided into common factors and special factors, of which the term common factors refers to those with impacts on ULSE in all periods or regions, and special factors refers to those with impacts on individual periods or regions.
(2)
where b0 is a constant, and bi (i ¼ 1, 2,., m) is the partial regression coefficient. After variable standardization, the obtained bi (i.e. standardized coefficients) may reflect the relative influence of each independent variable on the dependent variables (Menard, 2004). The larger the absolute value of bi is, the greater the impact on logit(P) will be. The interpretability of logistic regression results may be tested by means of Relative Operating Characteristics (ROC). The case of ROC 0.7 indicates that independent variables have strong interpretability on the dependent variable (Pontius & Schneider, 2001). Furthermore, the percent correct predictions (PCP) and pseudo R2 are used to interpret the predictive ability and fitting degree of the model respectively (Li et al., 2013; Menard, 2002). In the case where the pseudo R2 > 0.2, it shows a relatively good fit (Clark & Hosking, 1986). In order to eliminate the impact of collinearity among the variables on the logistic regression, and to compare the impacts of various factors, the Z-Score standardization method was used to process the variables, and multicollinearity test was carried out on independent variables. TOL (tolerance) >0.1 and VIF (variance inflation factor) <10 were taken as the standard (Ozdemir, 2011), to eliminate the samples with obvious collinearity. Finally, the binary logistic regression was applied for stepwise regression analysis on the sample data in various towns during each period. The significance of the variable sig.<0.1 was taken as the standard to eliminate variables with poor regression results, so as to obtain the relative impacts of various factors on ULSE.
Temporal differences The combination differences of ULSE factors, as well as the common factors and special factors during different periods in various towns are shown in Tables 2 and 3. For example, in Fuqiao the common factors during different periods include the neighborhood factors, the distances to ports and docks, to main river ways, while other factors are considered special factors. In Huangjing, the prime cropland and distances to exits of thruways are the special factors, and other factors are common ones. In Liuhe, the ecological sensitivity, distances to town centers and neighborhood factors are common factors, while other factors except distances to exits of thruways are special factors. The combination differences of those factors are mainly due to the variation of external environment of ULSE (e.g., government policies and development strategies). For example, due to the establishment of TPDZ in 1991, new urban land development occurred mainly near the Taicang Port rather than the main roads, thus the main roads had no significant effect in Fuqiao during 1989e1995. During the period of 1995e2001, the implementation of PCPR in October 1994 had significant restriction on ULSE. While in 2001e2008, the overall urban planning of Taicang (2001e2020) confirmed that Liuhe will focus on the development of tourism, which made new urban land expand along the main roads, and the influence of main river ways disappeared. And the thruways were constructed in 2005, thus their exits affected the adjacent Fuqiao and Huangjing during 2001e2008. The results in Table 3 also show that in Fuqiao, the relative importance of common factors during 1989e1995 is different from that in other periods because the distance to main river ways is more important than neighborhood factors during 1989e1995. In Huangjing, the relative importance of common factors is inconsistent during various periods, e.g., the distance to town centers is more important than the distance to main roads, which is opposite to that since 1995. However, in Liuhe the relative importance of common factors during 2001e2008 differs from that in the first two periods, and ecological sensitivity is more important than
Results Features of urban land expansion Over the past two decades, urban land of various towns expanded at different expansion speeds (i.e. annual expansion area) and growth rates (Table 1). Fuqiao has the largest expansion speed in each period, with an annual growth rate of 15.4% throughout the entire period. The expansion speed and growth rate increased from 40.8 ha/a and 13.2% in 1989e1995 to 328.9 ha/a and 18.3% in 2001e2008 respectively, this is mainly due to the development of Taicang Port development zone (TPDZ), and a large part of new urban land is located on the riverbank and involves port and industrial installations. Liuhe has the secondary expansion speed (38.4 ha/a), increasing from 25.1 ha/a in 1989e1995 to 61.2 ha/a in 2001e2008, especially in 2001e2008, its urban land expanded with an annual growth rate of 8.6%, and the expansion area accounted for 58.7% of that in the entire period. While Huangjing has the smallest expansion speed (38.3 ha/a), especially since 2001, the expansion speed and growth rate were the lowest among all three towns, even though they were greater than those of Liuhe before 2001.
Table 1 Urban land expansion in the study area from 1989 to 2008. Year
1989 1995 2001 2008
Urban land area (ha)
Time period
Huangjing
Fuqiao
Liuhe
206.36 348.93 619.38 933.21
220.86 465.48 1028.70 3330.72
249.21 399.69 549.90 977.94
1989e1995 1995e2001 2001e2008 1989e2008
Annual growth rate (%)
Annual expansion (ha/a)
Huangjing
Fuqiao
Liuhe
Huangjing
Fuqiao
Liuhe
9.2 10.0 6.0 8.3
13.2 14.1 18.3 15.4
8.2 5.5 8.6 7.5
23.8 45.1 44.8 38.3
40.8 93.9 328.9 163.7
25.1 25.0 61.2 38.4
0.244 2.310 1.412 0.837 86.8% 0.93 0.506 0.254 7.274 1.369 1.984 91.5% 0.96 0.658 0.615 2.986 0.486 1.094 84.6% 0.94 0.538
0.127 0.205 0.616 2.063 1.586 0.484
0.725 3.776 0.400 2.031 0.915 0.708 86.1% 0.93 0.524
0.321 1.329 79.9% 0.89 0.412
0.070 e 0.064 2.653 e 2.745
The degree of freedom of various variables are all 1; N indicates that the variable does not pass the test of significance; “e” indicates that the regression coefficient of the variable has no practical significance; and a ¼ significant at 10% level, and significant at 1% level for the rest; b indicates the standardized regression coefficient; OR indicates the odds ratio.
0.108 N 0.485 2.223 N 0.723
0.637 1.608 90.0% 0.96 0.608
0.529 4.994
0.310 0.369 0.906 0.494 1.250 0.258 e e 0.244 79.9% 0.86 0.328
b OR
0.743 0.298 0.755 0.113 0.398 N 0.296 1.212 0.281 2.183 0.921 N
b OR
0.763 0.860 0.270 0.151a
b OR
0.415 0.663 0.633 0.293 0.264 0.377 0.610 0.413 1.666 0.880 0.582 0.577 1.452 1.585 1.344 0.809 1.100 0.354 85.0% 0.91 0.482
b OR
0.251 0.162 0.511 0.207 0.059 0.281 1.383 1.821 0.670 1.577 2.826 1.271
b OR
0.525 0.809 0.644 0.212a
b OR
0.861 0.834 0.407 e 0.524 0.386 0.762 0.937 1.521 0.150 0.181 0.899 e 0.647 0.953 0.272 0.065 0.419 75.5% 0.84 0.279
b OR
1.014 0.611 0.435 0.505 0.840 0.218 0.014 0.492 0.832 0.682 0.174 1.523 0.480 N
b OR
0.619 N
b
1989e1995 Period
Constant ES Pcropland Dctown Dmroad Dpdock Dethruway Driver Neig PCP ROC Pseudo R2
2001e2008 1995e2001 1989e1995
Liuhe
2001e2008 1995e2001 1989e1995
Huangjing
2001e2008 1995e2001 Fuqiao Town
Table 2 Summary of the logistic regression models.
0.734 0.692 0.404 0.610 0.286 0.773 e e 1.277
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OR
186
neighborhood factors. In addition, special factors’ influences also vary with time. For example, in Fuqiao during 1995e2001, ecological sensitivity is more important than the distance to main roads, which is opposite to that during 2001e2008. Similar results exist in Huangjing and Liuhe. The above variation of factors’ relative influences in different time periods is also related to the phase characteristics of socioeconomic development. According to the change characteristics of various factors’ influences, those driving factors can be divided into three categories. The first category is of increasing importance, e.g., the prime cropland and distance to main roads. Since the implementation of PCPR in 1994, the prime cropland has had constraint effect on ULSE. And with the further decrease of cropland, the implementation of other related policies (e.g., the Pre-trial Management Approach for Construction Projects in July 2001) enhanced the influence of prime cropland, especially in the fastdeveloping regions, i.e. Fuqiao and Liuhe. The second one is of decreasing importance, e.g., the distances to main river ways and to town centers. Due to the development of highway traffic facilities, the traffic function of inland waterways became relatively weaker, thus the main river ways had decreasing effect on ULSE, especially in Liuhe during 2001e2008. The last one is of fluctuant influences, e.g., neighborhood factors, ecological sensitivity and the distance to ports and docks. The influence of neighborhood factors increased in 1995e2001 and then decreased in 2001e2008. This is due to the urban land expanded in the alternate modes of leapfrogging and infilling in those towns. Spatial differences As viewed from the perspective of spatial differences, the factors and their effects in different towns differ as well (Table 2, Fig. 5). This can be attributed to the differences of development environment in different regions. During 1989e1995, the common factors impacting various towns include neighborhood factors, the distances to town centers, to ports and docks and to main river ways. Those factors’ relative importance varies with towns, e.g., the distance to ports and docks is the most important factor in Fuqiao, while its influence is weaker than the distances to town centers. It is because the Taicang Port, which is located in Fuqiao, was more important than other factors for the development of TPDZ and urban land. While other towns owned less ports and docks, which were not the primary transportation mode, thus new urban land was mainly developed around the town centers. However, ecological sensitivity and the distances to main roads are considered special factors, of which ecological sensitivity only impacts Huangjing and Liuhe with the weakest influence, and its regression coefficients are significant merely at the 10% level; the latter one only impacts Huangjing, and its importance is secondary to the distance to town centers. This is because the importance of ecoenvironment was generally ignored at the early stage of economic development; this was obviously shown in Fuqiao during its development of TPDZ. Meanwhile, the main roads system was imperfect in Fuqiao and Liuhe, thus it was of little influence on urban land development in those towns. During 1995e2001, the factors in addition to the distances to exits of thruways and to ports and docks are common factors for every town. Their relative importance also varies with regions. For example, the distance to main roads is the most important factor in Huangjing, while it’s the weakest factor in Fuqiao and medium one in Liuhe. This is due to the improvement of main roads system in this period, but with the development of TPDZ, most of the new urban land in Fuqiao was still developed adjacent to the ports, while in Liuhe, urban land development mainly occurred around town centers. The distance to ports and docks is a special factor. It only impacts the ULSE in Fuqiao and Huangjing, and it’s the most
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187
Fig. 4. ROC curves of the logistic regression model in various towns during different periods.
thruways. Main river ways only impact Fuqiao and Huangjing, and it is more important than ecological sensitivity and prime croplands in Huangjing, which is opposite to that in Fuqiao.
important factor in Fuqiao. This is due to Taicang Port and some other docks were very important for the development of TPDZ in Fuqiao, and Huangjing also had several docks, while there was no dock in Liuhe. During 2001e2008, the distances to town centers, to exits of thruways and to main river ways are considered special factors, while the others are common factors. The relative importance of common factors is different in three towns. For example, the distance to ports and docks is more important than distance to main roads in Fuqiao, which is opposite to that in Liuhe and Huangjing. This is because the Taicang Port in Fuqiao was deemed as the No. 1 foreign trade port and a key project by Jiangsu Province according to Development Strategy Along the Yangtze River (DSAYR) in 2003, most of the new urban land development occurred near Taicang Port to develop TPDZ, while other towns, which owned fewer docks, had greater urban land expansion near main roads, though they were affected by the development of Taicang Port. Among the special factors, the distance to town centers only impacts Huangjing and Liuhe, and it is more important than prime croplands in Huangjing, which is opposite to that in Liuhe. The exits of thruways constructed in 2005 only impact adjacent Fuqiao and Huangjing slightly, which is due to the fact that development of TPDZ and Taicang Port brought a great demand for high speed transportation, and the development of Huangjing’s docks also needed transportation. However, Liuhe is located far away from the exits of
Discussion Theoretical implications It was found that all the potential factors have impacts on ULSE in the towns, but the combination of driving factors as well as the relative importance of common and special factors varied with towns and periods. Those differences exist because of diversified external environments of ULSE in different regions during various periods. Among the four types of variables, accessibility is the most important, which is consistent with the views of Li et al. (Dubovyk et al., 2011; Li et al., 2013). However, most of the following detail findings are seldom reported or different from that in previous studies focusing on large cities. Accessibility factors: We found that all the accessibility factors had significant negative effects on ULSE in various areas except for that in some periods, their relative influences not only varied with time, which is consistent with previous studies (Li et al., 2013), but also varied with regions. It was shown that all the towns were affected by the ports and docks, however, it was only in Fuqiao, in which the Taicang Port is located, the distance to ports and docks
Table 3 The rank order of the factors’ relative influences in different logistic models. Town
Fuqiao
Period
1989e1995
1995e2001
2001e2008
1989e1995
1995e2001
2001e2008
1989e1995
1995e2001
2001e2008
6 2
6
4 2 3 1 5
3 4
6 2
4 3
5 2
6 7 2 1 3 5 4 8
3 6 1 4
1
1 2 5
3 7 4 1 6
5
2
5 3 4 7 1
ES Pcropland Dctown Dmroad Dpdock Dethruway Driver Neig
Huangjing
3 1 5 7 4
Liuhe
The bold and underlined numbers indicate that the factors are the common factors in the same town.
1 4 2 3
5 2
6
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Fig. 5. Contrast among the absolute value of b coefficients in various towns during different periods.
was the most important factor during the entire study period, and the relative influences of distances to town centers and to main roads were weaker. While in other towns where roads were the major transportation routes, the relative influences of town centers and main roads were more important. And with the expansion of urban land, the influence of town centers decreased and main roads became increasingly important. This proves that the guidance of major traffic elements on ULSE is a basic pattern, which is similar to the conclusions of relevant studies (Poelmans & Van Rompaey, 2010). Results also showed that the influence of river ways on ULSE was becoming much weaker. This may be due to the fact that other traffic means such as road transport have advantages over river ways in inland transportation, especially under the requirement of speed transportation for economic development, increasing new urban land will be located adjacent to major traffic elements other than river ways. Besides, thruways also had impacts on ULSE, but they were much more important to the adjacent fast-developing regions, where the development of economy brought a great demand for transportation. Thus, the exits of thruways affected ULSE in those areas. Other factors: With the process of urbanization, the influence of ecological sensitivity first enhanced and then weakened, thus indicates that the natural eco-environment has not been given adequate attentions under the temptation of economic benefits of urbanization in China. Prime croplands had impacts on various towns since the implementation of PCPR in October 1994, and it has increasingly effects on the fast-growing towns, thus indicates that the faster the urban land expand, the stronger the land control policies’ effects will be, and those policies have effective effects on urban sprawl controlling (Tan et al., 2005). The importance of neighborhood factors also first increased and then decreased in various towns, it is different from that found in Beijing (Li et al., 2013). This may be attributed to that the study areas are not in the same size, and some factors such as prime croplands are not considered into the models in previous studies; besides, it is not proper to explain the relative influence of the independent variables on the dependent variable according to the unstandardized coefficients or statistical significance (Menard, 2002, 2004). It also reveals that the patterns of ULSE in Chinese small cities and towns alternate between leapfrogging growth and infilling growth, which is a form of low-density urban sprawl. Methodological implications In this study, comparing the existing research, the spatiotemporal differences in the driving forces of ULSE in various mediumsized regions during different periods were discussed based on standardized logistic regression coefficients. This enriches the research perspective of the driving mechanism of urban land expansion. It may provide decision support to develop differentiated urban land expansion control policies in the study area. This is due to the fact that many studies focus on the land expansion drive of a single large city, while the study area may be subdivided into
several sub-administrative areas, and the driving mechanisms in small areas may differ slightly or even greatly. Therefore, such generalized researches may mask the differences of smaller regions, thus leading to policies according to the principle of “one size fits all”. In addition, this study is beneficial to research of dynamic simulation of urban land expansion, which is a hot research topic based on thorough understanding of urban land expansion mechanisms. This study shows that spatiotemporal variation exists in the driving mechanisms of ULSE, so the simulation results could be much more rational if the simulation model is established and conducted with some spatiotemporal different simulation rules rather than constant rules in a large area. Furthermore, unlike previous studies, natural eco-environment factors and land control policies were considered in this study, thus the regression models are more close to the actual urban land expansion in China. The values of PCP and ROC in various models are high and very close to the results of relevant studies (Dubovyk et al., 2011; Li et al., 2013), and the pseudo R2 values are also greater than 0.27, thus indicating that the main factors were included in the models. Management and policies implications In this study, we found that accessibility factors especially the distances to town centers, to ports and docks, to main roads, have important effects on ULSE, but the relative magnitude of the unique effects varied with periods and areas. Those factors could be affected by the economic development policies (e.g., DSAYR) and related planning (e.g., traffic planning), thus government’s behavior can be seemed as the original impetus for ULSE. Therefore, in order to make urban land expand rationally, those related policies and planning should be made scientifically. For example, in urban planning, it is necessary to strengthen the scientific basis of traffic planning and town centers planning, which is a long-term strategy for the suppression of disordered urban sprawl. Of course, the dominant factors of ULSE should be grasped, and the common and special factors should be considered comprehensively, so as to develop differentiated land use policies. For example, the planning and development of ports and docks should be given much more attention to guide the rational ULSE in Fuqiao, while in Huangjing and Liuhe, scientific road traffic planning should be emphasized, so as to strengthen the development coordination between the port towns. Meanwhile, policies such as PCPR and ecological conservation are also another aspect of government’s behavior, which plays a restraining role on urban sprawl. For example, after the implementation of PCPR, China has gradually established the world’s most stringent farmland protection system. Prime cropland protection has a positive role in guiding expansion of urban land, but its effect depends largely on the implementation (Long et al., 2007). Therefore, it is necessary to further strengthen the enforcement of those policies, establish a mechanism of efficient urban land use through strict constraint of ULSE, and thus reduce the loss of
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cropland. It also shows that eco-environment hasn’t attracted sufficient attention yet, thus results in some serious ecoenvironmental problems. Therefore, eco-environment conservation should be strengthened in urban planning, especially in the fast-developing areas. In addition, the results also indicate that the ULSE pattern of low-density sprawl exists in Chinese small cities and towns, it is adverse to the intensive utilization of land, thus measures such as urban growth boundary (UGB) (Long et al., 2007) should be introduced to control urban sprawl in those areas. In general, ULSE is affected by many factors, but most of them can be affected by government’s behavior, i.e. government can not only stimulate urban land development but also constrain urban land expansion in spatial. Therefore, while formulating related policies and planning, government should pre-assess their effects on ULSE, and seek a winewin in the dilemma of socio-economic development and farmland protection. Meanwhile, differentiated policies should be formulated according to differences in various regions during different phases. Conclusions By using binary logistic regression, we analyzed the spatiotemporal differences among the driving forces of ULSE in the three port towns of Taicang City, and some distinctive features behind ULSE at the town-scale were revealed. This study extends our understanding of urban land expansion especially in towns in China, thus is meaningful to policy-making and other related researches. The results show that combinations of driving factors of ULSE, as well as various factors’ relative influences vary with periods or regions. The accessibility is always the dominant driver, in which the distances to town centers, to main roads are the dominant factors in Huangjing and Liuhe, while the distance to ports and docks is always the most important factor in Fuqiao. Meanwhile, the influences of town centers and river ways decrease and main roads become increasingly important. However, the exits of thruways only affect the adjacent fast-developing regions. In addition, China’s specific land control policy (prime cropland protection) has an important influence on ULSE especially in fast-developing areas since its implementation. What’s more, the variation of natural ecoenvironment factor (ecological sensitivity) and neighborhood factors indicates that urban land expands in the form of low-density sprawl in Chinese small cities and towns, without giving adequate attentions to the eco-environment. China has entered a period of dramatic urbanization. The spatial expansion of urban land in towns is an important aspect of urban land expansion, irrational ULSE in those areas will threaten the protection of eco-environment and farmland in China. In order to ensure the sustainable urbanization, spatiotemporal variation of the driving forces should be understood, so as to formulate differentiated ULSE management policies. Besides, we suggest that the government should strengthen its guidance, and some countermeasures from aspects of scientific planning of regional traffic networks and town centers, UGB, effective protection of prime croplands and natural eco-environment, etc. should be made, so as to guide reasonable urban land expansion. In this study, only eight potential factors were selected. In the case of available data, it is also required to further add relevant factors. In addition, 30 m 30 m grids were used for sample processing on the data. Although the results of the regression models are desirable, relevant studies have shown that the fitting results of the logistic regression model will vary in different grid scales (Olaniyi, Abdullah, Ramli, & Alias, 2012). Therefore, sensitivity impacts of grid scales on ULSE driving forces in future research would be worthwhile.
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