A comparative analysis of megacity expansions in China and the U.S.: Patterns, rates and driving forces

Landscape and Urban Planning 132 (2014) 121–135

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Research paper

A comparative analysis of megacity expansions in China and the U.S.: Patterns, rates and driving forces Wenhui Kuang a , Wenfeng Chi a,b , Dengsheng Lu c,d,∗ , Yinyin Dou e a

Institute of Geographic Sciences and Natural Resources Research, CAS, A11 Datun Road, Chaoyang District, Beijing 100101, PR China College of Resources and Environment, University of Chinese Academy of Sciences, 19A Yuquan Road, Shijinshan District, Beijing 100049, PR China Zhejiang Provincial Key Laboratory of Carbon Cycling in Forest Ecosystems and Carbon Sequestration, School of Environmental & Resource Sciences, Zhejiang A&F University, Hangzhou 311300, Zhejiang Province, PR China d Center for Global Change and Earth Observations, Michigan State University, 1405 S. Harrison Road, East Lansing, MI 48864, USA e Beijing Normal University, Beijing 100875, PR China b c

h i g h l i g h t s • • • • •

Chinese megacities have five times higher urban expansion in area than American megacities in past three decades. Chinese megacities have obvious urbanization patterns and rates at temporal scale but American megacities do not have. Chinese megacities expand from urban to rural with concentric rings but American megacities fill open spaces within inner cities. Chinese megacities are in developing stage that population, economic conditions and policies affect urbanization patterns and rates. American megacities are in developed stages without obvious impacts from population, economic conditions and policies.

a r t i c l e

i n f o

Article history: Received 8 December 2013 Received in revised form 30 August 2014 Accepted 30 August 2014 Available online 20 September 2014 Keywords: Impervious surface area Urban expansion Megacities USA China

a b s t r a c t Research on physical characteristics and land-cover dynamic changes of megacities over time provides valuable insights for effectively regulating urban planning and management. This study conducts a comparative analysis of 30-year urban expansion patterns and rates among three metropolises in China (Beijing, Shanghai, and Guangzhou) and another three in the USA (New York, Los Angeles, and Chicago) based on time-series impervious surface area (ISA) data extracted from multitemporal Landsat images using the linear spectral mixture analysis approach. This research indicates significantly different urbanization patterns and rates between the Chinese and American megacities. The ISA expansion area in Chinese megacities was five times higher than that in American megacities during the past three decades. The Chinese megacities expand outward from the urban core to the periphery in a concentric ring structure, whereas the American megacities increase ISA mainly within the inner cities with patch-filling patterns. The Chinese megacities are in the development stage where population and economic conditions significantly influence urban expansion patterns and rates, but the American megacities are in the developed stage where population and economic conditions are not important forces driving the ISA expansion. The ISA intensity in the American megacities decreases constantly and smoothly, but ISA intensity in Chinese megacities decays abruptly within certain distances, depending on different cities and years. The most obvious urban expansions were between 8 and 20 km in Beijing in the 1980s, between 14 and 50 km in Shanghai in the 2000s, and between 8 and 18 km in Guangzhou in the 1990s. © 2014 Elsevier B.V. All rights reserved.

1. Introduction

∗ Corresponding author at: Center for Global Change and Earth Observations, Michigan State University, 1405 S. Harrison Road, East Lansing, MI 48823, USA. Tel.: +1 517 432 4765; fax: +1 517 353 2932. E-mail address: [email protected] (D. Lu). http://dx.doi.org/10.1016/j.landurbplan.2014.08.015 0169-2046/© 2014 Elsevier B.V. All rights reserved.

A megacity is usually referred to as a metropolitan area with a total population of more than 10 million. Different megacities between developed and developing countries have tremendous divergences in urban expansion magnitudes and driving forces. The population living in cities is expected to rise to 67% in developing countries and to 86% in developed regions by 2050 (United

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Nations, 2012). In particular, a large demographic migration from rural to urban has occurred in developing countries due to rapid urbanization (Montgomery, 2008). China, as a developing country, underwent an accelerated urban expansion by 76% from the 1980s to 2010 (Liu, Zhan, & Deng, 2005; Wang et al., 2012). The United States of America (USA), as a developed country, is experiencing urban sprawl with fragmentation and a leapfrog expansion model (Kaza, 2013). Continuous population migration from rural areas to cities produces new challenges in urban management, public safety, biodiversity, carbon pools, and sustainability (Seto, Guneralp, & Hutyra, 2012). Natural disasters such as heat waves, floods, and hurricanes have produced unprecedented challenges in urban ecosystem services (Alberti, 2010; Grimm et al., 2008; McGranahan, Balk, & Anderson, 2007). As an essential component of urban landscapes, the growth of impervious surface area (ISA) is closely related to solar radiation and surface heat flux distribution, affecting urban heat islands and local climates (Bierwagen et al., 2010; Cui, Liu, Hu, Wang, & Kuang, 2012; Grimmond, King, Cropley, Nowak, & Souch, 2002; Imhoff, Zhang, Wolfe, & Bounoua, 2010; Jones, Lister, & Li, 2008; Oke, 2006; Xiao et al., 2007), and further affecting the residents’ comfort and health. As an important indicator in reflecting human activities and evaluating growth of urban construction land (Arnold & Gibbons, 1996), ISA has been used for health assessment and environmental quality evaluation in urban ecosystems. ISA is generally defined as any artificial surface resulting from urban development and construction that water cannot penetrate. It includes roads, parking lots, building roofs, and others (Lu & Weng, 2004). ISA can be extracted from individual sensor data such as IKONOS (Lu & Weng, 2009), QuickBird (Lu, Hetrick, & Moran, 2011a), Terra ASTER (Advanced Space-borne Thermal Emission and Reflection Radiometer) (Lu & Weng, 2006a), Landsat TM/ETM+ (Thematic Mapper/Enhanced Thematic Mapper Plus) (Lu, Moran, & Hetrick, 2011c; Yuan & Bauer, 2007), and DMSP-OLS (Defense Meteorological Satellite Program’s Operational Linescan System) (Elvidge et al., 2007; Zhang & Seto, 2011). In order to improve ISA mapping performance, combinations of different sensor data, such as TM and Radar (Lu, Li, Moran, Batistella, & Freitas, 2011; Zhang, Zhang, & Lin 2014), TM and QuickBird (Lu, Moran, Hetrick, & Li, 2012), and Terra MODIS (Moderate-resolution Imaging Spectroradiometer) and DMSP-OLS (Lu, Tian, Zhou, & Ge, 2008; Zhang, Schaaf, & Seto, 2013) have been proven valuable. The major techniques for ISA mapping have been summarized in review papers by Brabec (2002), Lu, Li, Kuang, and Moran (2014), Wang, Lu, Wu, and Li (2013), and Weng (2012). Landsat TM/ETM+ images are common data sources for ISA mapping at regional and even national scales (Lu & Weng, 2006b; Wickham et al., 2013; Xian & Homer, 2010). For example, they have been used for developing national ISA data in the USA (Xian & Homer, 2010). The first global ISA spatial distribution at 1-km spatial resolution was developed using the DMSP-OLS data (Elvidge et al., 2007), indicating that China has the largest ISA data in the world. Compared to the USA, China has had significantly different ISA growth rate, intensity, and pattern over the past 30 years. ISA in U.S. inner cities accounts for about 40–50% of the area (ratio of forest to ISA is 1.4:1), and ISA in Chinese cities accounts for about 66% (Kuang, Liu, Zhang, Lu, & Xiang, 2013; Nowak & Greenfield, 2012). The analysis of aerial photographs on Google Earth indicates that ISA in the USA covers an average of 43% of the city areas (Nowak & Greenfield, 2012). Between 1984 and 2010, the ISA in metropolitan Baltimore increased by 295 km2 from 881 km2 to 1176 km2 (Sexton et al., 2013). Taubenböck et al. (2012) used Landsat images from 1975, 1990, 2000, and 2010 to examine 27 metropolis urban expansions. In another study, they examined the spatiotemporal evolution from a poly-nuclei area to a mega-region in the Hong Kong–Shenzhen–Guangzhou area in southern China (Taubenböck

et al., 2014). Yin et al. (2011) employed Landsat images to map ISA distribution in Shanghai from 1979 to 2009 for examining urban expansion. Although many studies have been conducted on the monitoring of ISA dynamic changes in megacities (Taubenböck et al., 2012) and on spatiotemporal forms of urban expansion (Deng, Wang, Hong, & Qi, 2009; Herold, Goldstein, & Clarke, 2003; Huang, Lu, & Sellers, 2007; Kuang, 2012a; Liu, He, Zhang, Huang, & Yang, 2012; Schneider & Woodcock, 2008; Seto & Fragkias, 2005; Taubenböck et al., 2014), the analysis of ISA extents, spatial patterns, and growth rates, as well as the driving mechanisms, have not been fully investigated (Huang, Lu, & Sellers, 2007; Kuang, 2012b). In particular, a comparative analysis of the differences in ISA change characteristics, socioeconomic drivers, and impacts of policies across Chinese and American megacities has not been conducted. This kind of research may provide new understanding of the interactions of socioeconomic systems, different development stages, and urbanization patterns, thus providing valuable insights for better urban planning, management, and sustainability. Therefore, the objective of this research is to quantitatively evaluate the ISA characteristics by analyzing its change patterns and intensities in three Chinese megacities and three U.S. megacities over three decades based on time-series ISA data developed from Landsat images. Through this comparative study, we can improve our understanding of the relationship between ISA and population or gross domestic product (GDP), and the development stages and mechanisms behind urbanization to reveal different expansion magnitudes and spatial patterns under different systems between China and the USA.

2. Study areas In our comparative analysis of urban expansion patterns, the three largest megacities in China (i.e., Beijing, Shanghai, and Guangzhou, which are located in the Beijing–Tianjin–Hebei urban agglomerations, the Yangtze River delta, and the Pearl River delta, respectively) and the three largest megacities in the USA (i.e., New York, Chicago, and Los Angeles, which are located at the Atlantic coast, the Pacific coast, and the Great Lakes, respectively) were selected, as illustrated in Fig. 1. They have some similarities such as long histories of urban development, large populations and urban extents, and large GDPs. However, these megacities also have significantly different characteristics in urban spatial patterns and urbanization trends due to their different population densities and impacts of economic conditions and politics. Beijing lies on flat land with elevations of 20–60 m and has the potential to expand urban extent in a concentric circle structure. Shanghai and Guangzhou are located in coastal regions with the potential of urban expansion along the rivers. The three megacities in the USA have sector configurations of urban spatial patterns due to the constraints of the oceans and Lake Michigan. For the sake of this comparative analysis, it is necessary to define an urban extent for each megacity (Van de Voorde, Jacquet, & Canters, 2011). In general, an urban extent is determined by the administrative boundary. Because of different urban expansion rates for a city in different historical periods, the administrative boundary may be modified, especially in China due to the rapid urbanization during the past three decades. Therefore, the urban extents for the megacities are based on the latest administrative boundaries (see Fig. 1) with minimal discussion of historical changes. From a historical viewpoint, American and Chinese megacities have had different stages of urbanization processes. Empowered by the steel and textile industries in the second Industrial Revolution of the 18th and early 19th centuries, American megacities expanded rapidly outward, enabled by the development of railroads, streetcars, and trolleys in the 19th century. In particular, the

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Fig. 1. Six megacities—Beijing, Shanghai, and Guangzhou in China, and Chicago, Los Angeles, and New York in the USA were selected for a comparative analysis of urban expansion patterns and rates.

popularization of personal cars and improvement of transportation facilities made suburbanization more intensive, followed by formation of metropolitan areas. China has experienced rapid urbanization since the early 1980s and continues due to population

migrations from rural to urban regions and construction of infrastructures. As summarized in Table 1, the selected megacities in China and the USA have different population sizes and growth rates as well as GDPs. Because of the significant differences of

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Table 1 A summary of population and economic conditions in the past three decades in the selected largest megacities between China and the United States. Year

Beijing

Shanghai

Year

New York

Urban population (million)

1978 1990 2000 2010

4.79 7.98 10.57 16.86

6.45 8.65 9.86 12.55

Guangzhou 2.06 2.91 3.43 6.63

1978 1990 2000 2010

15.60 16.09 17.85 20.10

Los Angeles 9.51 10.88 11.81 13.22

Chicago 7.22 7.37 8.33 9.55

GDP (billion $)

1978 1990 2000 2010

1.78 8.18 51.64 230.51

4.46 12.77 77.92 280.36

0.70 5.22 40.71 175.54

1978 1990 2000 2010

566.45 – 914.00 1249.08

307.44 – 525.10 720.77

263.37 – 403.53 528.81

Data sources: the population and GDP data in China are from the 2011 statistical yearbook for Beijing, Shanghai and Guangzhou, and those in the United States are from the United States census data at the years of 1980, 1990, 2000 and 2010.

many aspects such as population density, social and political systems, and development stages, comparative studies of the megacities may provide new insights for guiding urban planning and management and urban sustainability. Beijing, the capital of China and the nation’s political, cultural, educational, and transportation center, is located in the northern part of the roughly triangular North China Plain. The climate in Beijing is a continental monsoon type, with cold and dry winters influenced by the vast Siberian anticyclone, hot and humid summers due to the East Asian monsoon, and relatively short spring and fall seasons. Shanghai, situated on the estuary of Yangtze River, is a center for economy, finance, trade, culture, science, and technology at national and global scales. This megacity has a humid subtropical climate with four distinct seasons: winter is chilly and damp, summer is hot and humid, spring is the most pleasant season but is changeable and often rainy, and fall season is generally sunny and dry (Sui & Lan, 2006). Guangzhou, located at Pearl River, is the capital of Guangdong Province. Because Guangzhou is close to Hong Kong and Macau, it has become a key national transportation hub and trading port (Seto & Fragkias, 2005). Guangzhou has a humid subtropical climate influenced by the East Asian monsoon. Summer is wet with high temperatures, high humidity, and a high heat index; winter is mild and relative dry. Guangzhou has a long monsoon season, spanning from April to September. Chicago is located at the southwestern shore of Lake Michigan on flat terrain with an average elevation of 176 m. As the third-largest city by population in the USA, Chicago is an international hub for finance, commerce, industry, telecommunications, and transportation. Chicago lies within the humid continental climate zone and has four distinct seasons with a hot and humid summer, cold and snowy winter, and mild spring and fall with low humidity. Los Angeles, located in southern California and ranked sixth in the Global Cities Index and ninth in the Global Economic Power Index, is a global city with strengths in culture, business, media, and international trade, and the center of the world’s television, motion picture, and recording industry. Los Angeles has a subtropical-Mediterranean climate with seasonal change in rainfall – dry summers and moderately rainy winters (only 35 days with measurable precipitation annually) – and relatively modest transition in temperature. New York, located in northeastern USA, is the most populous city in the country and the world’s largest financial center. New York has a humid subtropical climate with cold and damp winters and warm and humid summers. Spring and fall are unpredictable and can range from chilly to warm, although they are usually mild with low humidity. 3. Methods 3.1. Data collection and preprocessing A total of 31 scenes of time-series Landsat MSS/TM/ETM+ images covering six megacities in China and the USA in the past three

decades were used in this research (Table 2). QuickBird images with 0.61-m spatial resolutions were used to evaluate the latest ISA results (2009–2011 depending on the data availability in different megacities). All images were georeferenced to the Universal Transverse Mercator coordinate system for U.S. megacities and Albers (105, 25, 47) for Chinese megacities with root mean square errors (RMSEs) of less than 0.5 pixel. Landsat Multispectral Scanner (MSS) images from the 1970s were resampled to 30 m for matching the spatial resolution of Landsat TM/ETM+ images. The digital number values of Landsat images were converted to at-satellite reflectance using the dark-object subtraction method (Chander, Markham, & Helder, 2009; Chavez, 1988). Population and GDP data for Chinese megacities were obtained from the China Urban Construction Statistical Yearbook, which is published by the Ministry of Housing and Urban–Rural Development, and the data for U.S. megacities were extracted from the U.S. Census Bureau, and are summarized in Table 1. Ancillary data such as urban planning and management, and urban land-use data were also collected for examining how different urban development policies influence urbanization patterns and rates. 3.2. Impervious surface mapping and evaluation The urban landscape is a complex composition of different land covers such as building roofs, roads, trees, grass, water, and soils. The patch size of many land covers is less than the cell size of Landsat images, generating the mixed-pixel problem that affects land-cover mapping performance (Lu & Weng, 2004). The conceptual V–I–S (vegetation, impervious surface, and soil) model proposed by Ridd (1995) is often used to explain the composition of land covers in an urban landscape. The model provides a guideline to solve mixed-pixel problems in remote sensing images. The linear spectral mixture analysis (LSMA) approach is regarded as an effective way to solve the mixed-pixel problem and has been widely used to map ISA distribution in urban landscapes (Deng, Fan, & Chen, 2012; Lu & Weng, 2006b; Lu et al., 2014; Weng, 2007; Wu & Murray, 2003). An urban landscape is basically a composition of four components: ISA, vegetation, bare soil, and water (Lu & Weng, 2004). However, ISA is very complex and its spectral signatures have wide variations; thus, identification of individual ISA as an endmember is difficult (Lu & Weng, 2006b). Previous research has indicated that a high-albedo fraction image mainly contains the ISA with high spectral signatures such as bright building roofs, while a low-albedo fraction image contains dark ISA and water/wetlands with low spectral signatures; thus, ISA can be regarded as a combination of high-albedo and low-albedo fractions (Lu & Weng, 2006b; Lu et al., 2011c; Wu & Murray, 2003). The key is to remove the non-ISA pixels in the high-albedo and low-albedo fraction images; that is, some bare soils in the high-albedo fraction image and water/wetland and shadows in the low-albedo fraction image should be removed (Lu et al., 2011c).

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Table 2 A summary of satellite images used in research. Megacity

Datasets

Image acquisition date

Spatial resolution (m)

Path/row

Beijing

Landsat2-MSS Landsat5-TM Landsat5-TM Landsat5-TM QuickBird

1976-09-21 1991-06-17 2000-10-15 2011-06-08 2011-06-26

80 30 30 30 0.61

132/32 123/33 123/33 123/33

Shanghai

Landsat3-MSS Landsat5-TM Landsat5-TM Landsat5-TM QuickBird

1979-08-04/1979-08-04 1989-08-11/1989-08-11 2000-05-21/2000-06-06 2009-09-19/2009-07-17 2009-08-16

80 30 30 30 0.61

127/38-39 118/38-39 118/38-39 118/38-39

Guangzhou

Landsat3-MSS Landsat5-TM Landsat5-TM Landsat5-TM QuickBird

1978-11-29 1990-10-13 2001-12-30 2009-11-02 2009-09-24

80 30 30 30 0.61

131/44 122/44 122/44 122/44

New York

Landsat1-MSS Landsat5-TM Landsat5-TM Landsat5-TM QuickBird

1976-08-31 1989-09-28 2002-05-11 2010-10-08 2010-09-20

80 30 30 30 0.61

14/32 13/32 13/32 13/32

Los Angeles

Landsat2-MSS Landsat5-TM Landsat7-ETM Landsat5-TM QuickBird

1975-05-06 1990-09-03/1991-03-30 2000-05-01/2000-09-06 2010-11-13/2010-09-26 2010-10-12

80 30 30 30 0.61

44/36 41/36-37 41/36-37 41/36-37

Chicago

Landsat2-MSS Landsat5-TM Landsat5-TM Landsat5-TM QuickBird

1979-06-24 1989-09-18 2000-08-31 2010-05-23 2010-07-18

80 30 30 30 0.61

24/31 23/31 23/31 23/31

In this study, LSMA was applied to Landsat multispectral images for developing ISA data. The major steps for ISA mapping are illustrated in Fig. 2. In order to identify good-quality endmembers, minimum noise fraction (MNF) is used to transform Landsat multispectral (e.g., MSS and TM/ETM+) images into an orthogonal dataset. The first three components are used to identify four endmembers: high-albedo object, low-albedo object, green vegetation, and soil (Lu & Weng, 2006b). A constrained least-squares solution is then used to unmix the multispectral images into fraction images. The resulting fraction images represent the proportion (%) of each endmember within a pixel. High-albedo and low-albedo fraction images are then refined by eliminating vegetation-cast shade and water based on normalized difference vegetation index (NDVI) and modified normalized difference water index (MNDWI) (Xu, 2005). The refined high-albedo and low-albedo images are then combined to generate the ISA fraction image. Ideally, the extracted ISA data should be evaluated by comparing them with true values from a high spatial resolution image. A scatterplot consisting of ISA estimates from Landsat multispectral images and reference data from QuickBird images in this research is used to measure the linear relationship between these two variables, and the point distribution on the scatterplot can be used to explain underestimation or overestimation of the results. Because of the constraints of data availability of high spatial resolution data, especially for early periods, this study evaluated only the ISA results at the latest dates by comparing them to the QuickBird images, which were captured circa 2010. A total of 130 samples were collected using the stratified random sampling technique in each city. In order to allocate the samples to each stratum, the ISA fraction data were grouped into 10 levels: 1–10, 11–20,. . ., up to 91–100. A minimum of 10 samples for each level were randomly selected with a window size of 3 by 3 pixel (90 m by 90 m) for each plot. Within

each plot, ISA is digitized on screen by visual interpretation based on QuickBird images and the ISA percent is then calculated for each plot. RMSE, system error, and correlation coefficient (R) were used to evaluate the extracted ISA results (Wu & Murray, 2003). RMSE is defined as the standard deviation of the difference between the estimates and reference data. System error is defined as the sum of differences between the estimates and reference data divided by the total number of samples, representing the underestimation or overestimation of the overall estimates. R can be used to represent the strength of a linear relationship between the estimates and reference data. The three methods are used to evaluate the estimation performance of the ISA results. 3.3. Dynamic changes of time-series ISA datasets ISA dynamic changes are mainly represented by the changes in intensity and area caused by human activities and habitation. Intensity change of ISA during a particular time period is calculated by subtracting the ISA fraction of the earlier date from that of the later date, and the area change within a pixel is obtained through timing the intensity change by its area (900 m2 ). The following variables are calculated for further analyzing the ISA dynamic change: Overall ISA changed area (OICA) = ISA (t2) − ISA (t1) , Annual expansion area (AEA) = Expansion rate (ER) =

ISA (t2) − ISA (t1) , t2 − t1

ISA (t2) − ISA (t1) × 100, ISA (t1)

Annual expansion rate (AER) =

ER , t2 − t1

and

(1) (2) (3)

(4)

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Fig. 2. Flowchart for mapping impervious surface distribution with Landsat multispectral images.

where ISA(t2) and ISA(t1) are ISA amounts (km2 ) at posterior year (t2) and prior year (t1). An urban land-use model usually assumes that urban expansion from a town center to the suburbs yields a concentric circle structure. The innermost ring represents the central business district (CBD), and the city expands outward from there in rings with different land uses. The ring theory was widely used in spatiotemporal land-use change analysis along an urban–rural gradient (e.g., Tian, Jiang, Yang, & Zhang, 2011). It also provides the theoretical interpretation of the ISA distribution pattern along an urban–rural gradient at a certain extent. Based on the ring theory, we analyzed the ISA variation within a radius of 50 km as distance from a CBD. Concentric buffer structure is a common method used to analyze phenomena changes, such as population and land use, across an area (Seto & Fragkias, 2005; Tian et al., 2011). This method is used in this research to investigate the spatial patterns of the ISA changes from the CBD outward. We created a series of 5-km buffer zones out to our established radial 50-km limit (see Fig. 3), which covers most of the urban and rural areas of the six megacities. The ISA percentage at each buffer zone with an increment of 2 km was calculated and used to analyze the relationships between the distance from the CBD and ISA distribution. 3.4. Relationship between ISA dynamics and demographic/economic conditions Population and socioeconomic conditions are closely related to urbanization pace and spatial patterns. However, their roles in

influencing ISA changes in developed and developing countries have not been fully investigated. In this study, we examined the impacts of population increase and GDP growth on ISA changes by exploring linear and nonlinear regression models for six megacities, and evaluated the effects of urban planning policies on ISA change patterns under different political and cultural systems. The population (or GDP) densities are calculated for a comparative analysis among the megacities: Pop D =

Number of persons in thousand , ISA in square kilometers

GDP D =

GDP in million USD ISA in square kilometers

and

(5) (6)

where Pop D (population density) and GDP D (GDP density) represent the number of persons in a unit (thousand per ISA square kilometer) and GDP amount in a unit (million USD per ISA square kilometer). 4. Results and discussions 4.1. Evaluation of the ISA results The evaluation of circa 2010 ISA results using RMSE, system error, and R shows the reliability of using the LSMA approach for the ISA estimation for all six megacities, with the RMSE values between 0.151 and 0.175, system errors between −0.085 and −0.113, and R values between 0.89 and 0.92 (Table 3). The negative system errors indicated that the ISA results from Landsat images

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Fig. 3. Buffer zones at 5-km intervals within a 50-km radius in the six megacities to show the analysis of ISA distribution and dynamic change at spatial and temporal scales.

were slightly underestimated, implying that the LSMA-based method underestimated overall ISA estimates in megacities, a similar conclusion to previous studies (Lu & Weng, 2006b; Wu & Murray, 2003). When the ISA estimate and reference data for each megacity are drawn in a scatterplot (Fig. 4), a linear trend indicates

high estimation performance using the LSMA-based method. Fig. 4 also shows that ISA was underestimated in the developed regions when ISA percent in a unit (90 m by 90 m in this study area) was greater than 80%, but ISA was overestimated in the less developed regions when ISA was less than 10%. Although evaluation of other

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Fig. 4. Relationships between ISA estimates and reference data in the six megacities.

dates of ISA estimates was not conducted in this research due to the lack of reference data, our previous research has indicated that the LSMA-based method can provide reliable ISA results using the time-series Landsat TM images (Lu et al., 2011c); therefore, the time-series ISA results are used for further analyzing ISA dynamic changes.

4.2. Analysis of ISA spatial distribution and dynamic changes The ISA distributions in the past three decades indicate that the three megacities in China have considerably higher ISA expansion magnitudes with different spatial patterns than the megacities in the USA (Fig. 5). The Chinese megacities appear much

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Fig. 5. The ISA distributions in six megacities at different years from the 1970s to circa 2010.

more compact than the ones in the USA. Although the three megacities in the USA have much higher ISA magnitudes than those in China (Table 4), the ISA expansion areas and rates in China were much higher than in the USA during the past three decades, revealing different urbanization processes and urban

development stages between the developing and developed countries. The urban expansion areas in three Chinese megacities increased annually by 15.4–32.5 km2 in the past three decades, but in American megacities they were only 1.6–8.1 km2 annually in

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Table 3 Evaluation of the ISA estimates based on circa 2010 Landsat TM images among the six megacities. Study area

RMSE

System Error

R

Beijing Shanghai Guangzhou Chicago Los Angeles New York

0.151 0.175 0.154 0.159 0.155 0.170

−0.085 −0.113 −0.095 −0.087 −0.096 −0.107

0.91 0.90 0.92 0.89 0.91 0.89

the same period. The slowest annual expansion in China occurred in the early 1980s, at the beginning of the reform and openingup policies of 1978. However, upon entering the 21st century, Chinese megacities began a fast expansion period, especially in Shanghai. In contrast, during the 1990s, New York and Los Angeles had annual expansion areas of only 0.64–0.68 km2 /year. In the past three decades, the expansion rates in China reached 348–498% compared to only 2.6–18.8% in the USA, and the annual expansion rates were as high as 10–16.1% in China but only 0.07–0.61% in the USA. The overall ISA expansion area in Chinese megacities was five times larger than that in the USA in the past three decades, especially in the first decade of the 21st century, when it reached 11 times larger, as shown in Table 4. The considerably different ISA expansion amounts between the megacities in China and the USA result in different spatial patterns of urbanization. Examining the distribution of ISA expansion (Fig. 3) indicates that Chinese megacities expanded outward from urban to rural areas with concentric ring structures, but ISA changes in the American megacities occurred mostly in the inner cities. Beijing has the most prominent ring pattern due to strong attractiveness of the CBD and development of a series of ring roads. Guangzhou stands out with obvious sector patterns due to terrain constraints. Spatiotemporal patterns of ISA changes indicated that the three Chinese megacities presented rapid diffusion from the urban core to the periphery with a pie shape, although megacities are overall

compact, which is a typical characteristic of Chinese urban expansion. This implies the influences of traditional theories in modern urban development process at certain degrees. Modern city development appears in more complicated patterns to mitigate pressure and to satisfy various needs of economic, environmental, and social aspects. Such patterns include urban agglomeration of peripherals and development of scattered satellite cities in outskirts (Kaza, 2013). The ISA areas in the three American megacities remained relatively stable during the past three decades, and most ISA expansion areas occurred in the inner cities by filling open space or through redevelopment, thus the ISA spatial patterns become more connected over time (Wu & Thompson, 2013). Kaza (2013) indicated that the U.S. urbanization process had been undergoing dramatic landscape changes since 2000 but has been much slower than in China during the same period. The sizes and magnitudes as well as expansion patterns among megacities in the USA and China vary significantly. Three American megacities had formed into megalopolises with low-density urban development patterns and megacities sprawled outward in pie-shaped style and leap-frog growth patterns (Kaza, 2013). The Chinese megacities grew mainly by urban agglomeration during the 1990s. The urban growths in the recent 10 years followed a leap-frog pattern and gradually transformed to megalopolises.

4.3. Analysis of ISA dynamic changes within buffer zones The relationships between ISA intensity and the distance from a city’s CBD indicate that, overall, the ISA intensity decreases as the distance increases, but the patterns and slopes in different megacities between China and the USA vary considerably, as shown in Fig. 6. In the 1970s, the ISA intensity in Chinese megacities decreased sharply from the CBDs (about 6 km in Beijing and 4 km in both Shanghai and Guangzhou) to about 12 km. In Beijing, the distance value for a sharp change increased over time, from 12 km in the 1970s to about 22 km circa 1990 and 2000, and to 34 km

Table 4 ISA and its changes in six megacities from the 1970s to circa 2010. Megacities in China Beijing 1970s Circa 1990 Circa 2000 Circa 2010 Circa 1970s–1990 Circa 1990–circa1990 Circa 2010–circa2000 Circa 2010–1970s Circa 1990–1970s Circa 2000–circa1990 Circa 2010–circa2000 Circa 2010–1970s Circa 1990–1970s Circa 2000–circa1990 Circa 2010–circa2000 Circa 2010–1970s Circa 1990–1970s Circa 2000–circa1990 Circa 2010–circa2000 Circa 2010–1970s

Megacities in the USA Shanghai

Guangzhou

ISA results in km2 among six megacities at specific dates (year) 212.58 (1976) 209.80 (1979) 96.05 (1978) 422.75 (1991) 382.43 (1989) 133.93 (1990) 659.71 (2000) 602.38 (2000) 266.85 (2001) 952.82 (2011) 1185.34 (2009) 574.77 (2009) Overall ISA change area (km2 ) 210.17 172.63 37.88 236.96 219.95 132.92 293.11 582.96 307.92 740.24 975.54 478.72 2 Annual expansion area (km /year) 14.01 17.26 3.16 26.33 20.00 12.08 26.65 64.77 38.49 21.15 32.52 15.44 Expansion rate (%) 98.87 82.28 39.44 56.05 57.51 99.25 44.43 96.78 115.39 348.22 464.99 498.41 Annual expansion rate (%/year) 6.59 8.23 3.29 6.23 5.23 9.02 4.04 10.75 14.42 9.95 15.50 16.08

New York

Los Angeles

Chicago

1932.11 (1976) 1989.24 (1989) 1998.08 (2002) 2056.80 (2010)

2228.48 (1975) 2259.69 (1990) 2266.09 (2000) 2285.44 (2010)

1327.39 (1979) 1386.92 (1989) 1548.17 (2000) 1576.86 (2010)

57.13 8.84 58.72 124.69

31.21 6.40 19.35 56.96

59.53 161.25 28.69 249.47

4.39 0.68 7.34 3.67

2.08 0.64 1.93 1.63

5.95 14.66 2.87 8.05

2.96 0.44 2.94 6.45

1.40 0.28 0.85 2.56

4.48 11.63 1.85 18.79

0.23 0.03 0.37 0.19

0.09 0.03 0.09 0.07

0.45 1.06 0.19 0.61

Note: The ratio of overall ISA change area between China and the USA in the past three decades = (740.24 + 975.54 + 478.72)/(124.69 + 59.96 + 249.47) = 5.09 (circa 1970s–2010). The ratio of overall ISA change area between China and the USA in the latest decade (293.11 + 582.96 + 307.92)/(58.72 + 19.35 + 28.69) = 11.1 (circa 2000–2010).

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Fig. 6. A comparison of relationships between ISA intensity and distance from a city’s central business district in six megacities in China and the USA.

circa 2010. The urbanization pattern in Shanghai is much different compared to Beijing. The distance for sharp change increased from 14 km in the 1970s and circa 1990 to 22 km in circa 2000 and to 38 km in circa 2010, while the distance in Guangzhou increased from 12 km in the 1970s and circa 1990 to 18 km in circa 2000 and 2010. The ISA intensity in Beijing dropped to less than 10% circa 2010 when the distance reached 42 km, while the intensity

in Shanghai was stable at more than 32% between 38 km and 46 km. In Guangzhou, the intensity value of more than 20% was stable between 44 km and 50 km in 2010. Considering ISA dynamic changes, Beijing had a high expansion area in the 1980s within the buffer zones between 8 km and 20 km, the expansion area became relatively small but mainly in the zones between 6 km and 22 km in the 1990s and between 14 km and 30 km in the 2000s. In

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Table 5 Relationships between ISA and population and between ISA and GDP in the six megacities. Megacities

Beijing Shanghai Guangzhou New York Los Angeles Chicago

Urban population

GDP

Formula

R2

Formula

R2

y = 598.78ln (x) − 759.39 y = 33.164e0.287x y = 432.67ln (x) − 264.037 y = 412.73ln (x) + 816.94 y = 168.15ln (x) + 1852.5 y = 879.97ln (x) − 377.22

0.9822 0.9958 0.9519 0.8612 0.9730 0.8703

y = 149.14ln (x) + 112.26 y = 219.29ln (x) − 174.33 y = 2.6293x + 121.86 y = 156.03ln (x) + 940.54 y = 67.208ln (x) + 1843.9 y = 372.24ln (x) − 730.01

0.9910 0.9071 0.9839 0.9924 0.9987 0.9185

Note: y represents ISA, and x represents population or GDP.

Shanghai, the urban expansion area in the 1980s was very limited, but became high in the 1990s in the buffer zones between 6 km and 36 km, and even higher in the 2000s between 14 km and 50 km. In Guangzhou, relatively high urban expansion occurred in the buffer zones between 6 km and 24 km in the 1980s, became much higher between 8 km and 50 km in the 1990s, and then decreased to a small amount in the 2000s. The urbanization phenomena at spatial and temporal scales described above were coincident with China’s economic development and opening-up policies, implying the important roles of policies in affecting urbanization patterns and rates. Compared with the Chinese megacities, the U.S. megacities have very similar urbanization trends without obviously high or low rates at spatial and temporal scales. In Chicago, the decline slopes from the 1970s to circa 2010 look similar from the CBD to a distance of about 30 km; the intensity remains stable between 30 km and 44 km, and then decreases slightly. The intensity was still about 20% circa 2010, even out to 50 km. In Los Angeles, the slope decreased from the CBD to a distance of 36 km, but slightly increased out to 42 km, and then became stable. The intensity reached 30% at a distance of 50 km circa 2010. For New York, the slope decreased out to 30 km and had a small change between 30 km and 50 km, with ISA intensity of about 12% at 50 km in 2010. One interesting finding is the small peak of ISA intensity at a distance of around 40 km in American megacities, but not in Chinese megacities until entering the 21st century in Guangzhou and Shanghai. This is because the new satellite cities developed in outskirts of megacities from previous urban agglomerations in the USA. The megacities in the USA did not have obvious ISA dynamic changes between different periods except that Chicago had very limited urbanization in the 1980s and 1990s. The main reasons for this phenomenon lie in the following: (1) They have different urban planning strategies and practices. American megacities consist of several distinct zones successively from a city center to the outskirts, namely CBD, medium residential zone, and low residential zone. Within these functional zones, high–medium–low functions are effectively assembled, resulting in ISA intensity variation along urban–rural gradient. However, functional zones in Chinese megacities are mosaicked without distinct differences. Boundaries between urban and rural areas are very sharp with high intensity in the CBD and low intensity in suburban areas. (2) The urbanization of the three American megacities occurred in the 1950s and their sizes have remained relatively stable since then, while urbanization in Chinese megacities started in the late 1970s due to the reform and opening-up policies. Rapid urban expansion happened in the last 30 years and the megacities have sprawled greatly, resulting in highly increased ISA intensity (Liu et al., 2005). Fig. 6 also indicates that within the radius of 6 km around the CBD, ISA in all megacities accounted for approximately 80% circa 2010; however, the city functions within this radius are different between American and Chinese megacities. This area in American megacities is mainly occupied by the CBD with high density and high-rise buildings. The core of the Chinese megacities is occupied by a mixture of the CBD and residences.

4.4. Impacts of population, economy, and urban planning policies on urbanization Population and GDP growths are generally regarded as important drivers for urban sprawl associated with an increase of urban ISA. Examining the relationships between ISA and population (or GDP) at different periods among American and Chinese megacities indicated that ISA had a strongly positive relation with the logarithm of population and GDP; the coefficients of determination (R2 ) reached more than 0.86 for all megacities (Table 5). This implies that increased ISA is largely due to rural-to-urban population migration and stimulation of economic growth, a similar conclusion of previous studies (Bai, Chen, & Shi, 2012). This situation is clearly illustrated in Fig. 7, showing that population and GDP are more sensitive to the ISA in the megacities in China than to those in the USA. ISA extents in American megacities have less fluctuation with urban development approaching the saturation level and stable population (Fig. 7a). The relationship between GDP and ISA is similar to the relationship between population and ISA; that is, although GDP amounts in Chinese megacities are smaller than those in American megacities, the GDP’s effects on ISA expansion are stronger in Chinese megacities than in American megacities (Fig. 7b). These findings indicate that Chinese megacities bear higher density in population while American megacities yield higher GDP amounts, as confirmed in Table 6. Another difference is that the population size in each ISA square kilometer is decreasing in Chinese megacities, but slightly increasing in the American megacities (Table 6). This implies that urbanization speed is faster than the population growth rate in China. Different urban planning and management strategies and policies due to socioeconomic and cultural differences between the USA and China result in various effects on urbanization. The urban planning and management policies in the USA are designed and implemented largely by local governments under regulations set forth by federal and state governments. The early popularization of personal vehicles and improvements in transport infrastructures have significantly enhanced the accessibility of suburbia, and led the American megacities to expand to the low-density surrounding suburbs in leap-frog style (Kaza, 2013). On the other hand, the Chinese government plays an important role in the urbanization process. The government first formulates an urban planning frame in which the city orientation and functions are precisely determined; then detailed landscape and land-use planning and management measures are implemented (Liu et al., 2005). In the 1990s, Chinese megacities mainly underwent an agglomeration process; after 2000, urban sprawl accelerated and presented a concentric leap-frog growth pattern. Urbanization in the USA reflects postindustrial/postmodern characteristics, but urbanization in China presents unprecedented speed since implementing the reform and opening-up policies (Kuang et al., 2013). The straightforward, application-orientated analyses of ISA dimensions, patterns, and growth, as well as the driving forces behind population, economy, and policies in the USA

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133

Fig. 7. Relationships between ISA and population (a) and between ISA and GDP (b) for six American and Chinese megacities.

Table 6 A comparison of population (GDP) densities among the six megacities. Population density (in thousand/km2 ) Beijing 1970s Circa 1990 Circa 2000 Circa 2010 1970s Circa 1990 Circa 2000 Circa 2010

Shanghai

22.53 30.74 18.88 22.62 16.02 16.37 17.69 10.59 GDP density (USD in million/km2 ) 8.37 21.26 19.35 33.39 78.28 129.35 241.92 236.52

Guangzhou

New York

Los Angeles

Chicago

21.45 21.73 12.85 11.54

8.07 8.09 8.93 9.77

4.27 4.81 5.21 5.78

5.44 5.31 5.38 6.06

7.29 38.98 152.56 305.41

293.18

137.96

198.41

457.44 607.29

231.72 315.37

260.65 335.36

and China under different development stages enable us to learn lessons from previous practices. Such lessons are especially valuable in urban planning and design and policy making to obtain a balance between urban development and environmental protection and to better serve urban citizens in the future. The rapid ISA increases in the three Chinese megacities indicate the important roles of China’s opening-up policy and the market-oriented reform since the early 1980s (Li & Yeh, 2004). Urban planning was more orientated to international standards. For instance, in 1990, the Chinese government decided to set up a special economic zone called Pudong New Area, formerly a lessdeveloped agricultural area east of Huangpu River, where most of old Shanghai is located. Since then, Pudong has become a major economic development zone, and has emerged as China’s financial and commercial hub. As an integral part of Shanghai, Pudong has driven Old Shanghai’s reconstruction and development, enhanced Shanghai as an international center of economy, finance, and trade, brought along surrounding megacities on the Yangtze River delta, and strengthened Shanghai’s global competitive capacity (Yin et al., 2011). Urban ISA expansion patterns are largely affected by urban planning philosophies and cultures. With the influence of Chinese traditional urban planning philosophies, Beijing, Shanghai, and Guangzhou have been expanding outward from the city centers in pie-style but compact patterns. The rapid urbanization pace in Chinese cities during the past three decades results in huge extents of megacities and high building density. In order to alleviate this problem, Chinese urban morphology becomes leapfrog expansion trend in recent years through construction of satellite cities (Kuang et al., 2013; Liu et al., 2005; Seto & Fragkias, 2005). Western urban planning was influenced by the idea of a garden city. TheGarden City of Tomorrow published in 1902 launched

the practice of urban planning in the United States in the 19th century (Howard, 1902). The heart of the garden city’s ideals is holistically planned into new settlements that enhance the natural environment, providing high-quality, affordable housing and locally accessible jobs. In the United States, city planning incorporates this idea and emphasizes the harmonious arrangements of distinct functional urban structures, such as the CBD, residential areas, and suburbs. The United States entered into a large-scale suburbanization development period after 1920 because of constructions of highways and rapid increase of cars and the overall autonomous management between central cities and surrounding municipalities (Fallah, Partridge, & Olfert, 2011; Kane, Tuccillo, York, Gentile, & Ouyang, 2014; Pickett et al., 2011; Wiechmann & Pallagst, 2012). Therefore, the suburbs in the United States have sprawl expansion with low density of population and house, resulting in low ISA density (Nowak & Greenfield, 2012). The ideas of urban smart growth proposed in 1997 play an important role in preventing low density urban sprawl and enhancing the development of compact urban spatial structure (Handy, 2005). 5. Conclusions The Chinese megacities have significantly different urbanization patterns and rates compared to the American megacities. Overall ISA expansion areas in Chinese megacities have been five times larger than those in American megacities in the past three decades, particularly in the first decade of the 21st century, when they were as much as 11 times larger. The Chinese megacities expand outward from the CBD to the periphery in concentric rings, especially in Beijing and Shanghai, thus urban extents sprawl rapidly. In contrast, the American megacities increase ISA mainly in the inner cities with patch-filling patterns; thus, urban extent remained stagnant

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or grew slowly. The Chinese megacities are in the development stage where population and economic conditions as well as policies significantly influence urban expansion patterns and rates, but the American megacities are in the developed stage where population and economic conditions are not important forces driving the ISA expansion. Urban planning philosophy, policies for reform and economic development, and rapid population growth accelerated China’s urbanization process dramatically and intensively. However, under the ideals of a garden city, urban expansion in American megacities is much slower, and impacts of socioeconomic conditions on urbanization rates in the USA are not as strong as in China. The urban areas within the 6-km radius have ISA greater than 80% and the ISA intensity decreases as the distance from the CBD increases. The ISA intensity in the American megacities decreases constantly and smoothly due to the distinct functions of urban structures; in contrast, ISA intensity in Chinese megacities decays abruptly within certain distances, depending on different cities and years. The most obvious urban expansions were in the 1980s within the range of 8–20 km in Beijing, in the 2000s within the range of 14–50 km in Shanghai, and in the 1990s within the range of 8–18 km in Guangzhou, implying the important roles of policies on urbanization patterns and rates. Approximately 40 km from the CBD, there was an ISA increase in the American megacities, Shanghai, and Guangzhou in the first decade of the 21st century, implying the advent of satellite cities surrounding the megacities. The comparative analysis of patterns, rates and driving forces among megacities’ urban expansions between China and the United States provides valuable lessons and experiences for urban planning and management at different development phases.

Acknowledgments The authors are grateful for the financial support from National Natural Science Foundation of China (41371408), the Zhejiang A&F University’s Research and Development Fund for the talent startup project (2013FR052), the National Basic Research Program of China (2010CB950900), National Key Technology R&D Program (2012BAJ15B02), and One-Three-Five technology project of the Institute of Geographic Sciences and Natural Resource Research, Chinese Academy of Sciences (2012ZD002). The authors also thank Jing Wang and Meng Kong for their support in data collection and processing, and three reviewers for their constructive suggestions and comments for revising this paper.

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