How does sprawl differ across cities in China? A multi-scale investigation using nighttime light and census data

Gao, Bin; Huang, Qingxu; He, Chunyang; Sun, Zexiang; Zhang, Da

doi:10.1016/j.landurbplan.2015.12.006

Cited by 173 publications

(79 citation statements)

References 54 publications

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“…This finding is based on the results of urban land change calculations for the 31 cities. (2) Small and medium cities in the MYRB experienced the highest level of expansion, which is consistent with the situation of China according to a previous study [78]. This is supported by our findings that the expansion rates and intensities of small and medium city were larger over the period [2000][2001][2002][2003][2004][2005][2006][2007][2008][2009][2010][2011][2012][2013][2014][2015][2016].…”

Section: Discussionsupporting

confidence: 88%

Satellite Monitoring of Urban Land Change in the Middle Yangtze River Basin Urban Agglomeration, China between 2000 and 2016

Liu

Chen

2017

Remote Sensing

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Detailed studies on the spatiotemporal patterns of urban agglomeration in the Middle Yangtze River Basin (MYRB) are rare. This paper analyzed the spatiotemporal patterns of urbanization in the MYRB using multi-temporal remote sensing data circa 2000, 2008 and 2016 integrated with geographic information system (GIS) techniques and landscape analysis approaches. A multi-level analysis of the rate and intensity, type as well as the landscape changes of urban expansion at regional, prefectural and inner-city levels was performed. Results show that the MYRB experienced rapid urban expansion with an annual expansion rate of 3.199%, especially in the Chang-Zhu-Tan and Poyang Lake metropolitan areas. The small and medium cities presented faster urban expansion than the larger cities with annual growth rates three times the average level. Urban expansion within the three capital cities was further analyzed in detail. It is found that outlying expansion and edge-expansion were the dominant growth patterns at all the three levels. Although urbanization in the MYRB has a remarkable increase in the past sixteen years, its annual growth rate of urban land expansion has fallen behind the three other large urban agglomerations in China as a result. Finally, the spatial evolution of the socioeconomic structure of the MYRB was further explored. It indicated that urban land was distributed mainly along the "northwest-southeast" direction and that the economic spatial interactions among cities showed a pattern of "multi-polarization and fragmentation", which illustrates the weak radiative driving forces of the central cities. The MYRB urban agglomeration faces a great challenge to manage trades-offs between narrowing the intra-regional disparity and maintaining synergetic development among cities.

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Section: Discussionsupporting

confidence: 88%

Satellite Monitoring of Urban Land Change in the Middle Yangtze River Basin Urban Agglomeration, China between 2000 and 2016

Liu

Chen

2017

Remote Sensing

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“…2017, 9, 571 13 of 17 patterns were examined by employing all lit-pixels instead of the urban-pixels [4,32,38]; (3) growth patterns were modeled by integrating intensification and expansion together instead of concentrating on either urban expansion [30,31,47] or urbanization [25,28].…”

Section: Growthmentioning

confidence: 99%

“…On one hand, previous findings had suggested strong relationships between digital number (DN) values with urban population density [16], built area density [17], house vacancy rate [18], and other socio-economic indicators [19][20][21][22][23]; On the other hand, they have a relative high temporal frequency of observation. Previous studies mainly focused on the dynamics of urbanization in general [24][25][26][27][28] or urban expansion itself in particular [29][30][31], and they ignored the influence from the non-urban lit-pixels [3,8,32,33]. In addition, there were many other studies focusing on the dynamics of urbanization or urban expansion from the pixel perspective [26], for individual megacities [34,35], at a local or region level [13,36], at a country level [27,37], or even in a long time period [38].…”

Section: Introductionmentioning

confidence: 99%

“…At the metropolitan level, this study differs from the previous work in two folds. Firstly, it employs every single lit-pixel to examine the growth patterns instead of placing its emphasis only on the urban lit-pixels as most previous studies; Secondly, the growth patterns are modeled from the aspects of both intensification and expansion, which is different from most previous studies concentrating either on dynamics of urbanization [24,28] or on urban expansion itself [30,31]. In our study, intensification is characterized as the change of DN values, which is related to human activities in terms of demographic dynamics and economic development; expansion is presented as the change of DN extent and its spatial types, which is also related to human activities by consuming unexploited land.…”

Section: Introductionmentioning

confidence: 99%

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Characterizing the Growth Patterns of 45 Major Metropolitans in Mainland China Using DMSP/OLS Data

2017

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Abstract:Understanding growth patterns at the metropolitan level is instructive for better planning and policy making on sustainable urban development. Using DMSP/OLS data from 1992 to 2013, this article aims to investigate growth patterns of major metropolitans in Mainland China from the aspects of intensification and expansion. We start by calibrating the DMSP/OLS data and selecting 45 major metropolitans. On intensification, results suggest that aggregately, metropolitans displayed cyclical pattern over time and large metropolitans tended to have higher levels of intensification than moderate or small ones. Individually, metropolitans with similar intensification over time could be clustered together using Dendrogram, and evolution pattern of the clusters exhibited similarity to the aggregated one. On expansion, results show that aggregately metropolitans displayed a decreasing trend over time, and moderate or small metropolitans tended to have higher levels of expansion than large ones. Particularly, moderate metropolitans were more likely to expand adjacently, and small ones were more likely to experience scatter or corridor expansion. Each metropolitan can be represented by a mixed expansion model over time, which might tell where and how much expansion occurred in the current year. Furthermore, intensification is highly correlated with expansion over time for small metropolitans, but they are poorly correlated for large or moderate ones. Lastly, the high correlation of intensification and expansion with the change of GDP in each year indicates the reliability of our work.

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“…We divided all the cities into four groups (i.e., mega-, large, medium, and small cities) according to their urban population in 2010 [48]. The relative errors for the UPs in large cities and megacities were higher than the results in medium and small cities (Table 3).…”

Section: Simulation Accuracy Levels Varied Across Cities Of Differentmentioning

confidence: 99%

A Simulation Study on the Urban Population of China Based on Nighttime Light Data Acquired from DMSP/OLS

2016

Self Cite

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Abstract:The urban population (UP) measure is one of the most direct indicators that reflect the urbanization process and the impacts of human activities. The dynamics of UP is of great importance to studying urban economic, social development, and resource utilization. Currently, China lacks long time series UP data with consistent standards and comparability over time. The nighttime light images from the Defense Meteorological Satellite Program's (DMSP) Operational Linescan System (OLS) allow the acquisition of continuous and highly comparable long time series UP information. However, existing studies mainly focus on simulating the total population or population density level based on the nighttime light data. Few studies have focused on simulating the UP in China. Based on three regression models (i.e., linear, power function, and exponential), the present study discusses the relationship between DMSP/OLS nighttime light data and the UP and establishes optimal regression models for simulating the UPs of 339 major cities in China from 1990 to 2010. In addition, the present study evaluated the accuracy of UP and non-agricultural population (NAP) simulations conducted using the same method. The simulation results show that, at the national level, the power function model is the optimal regression model between DMSP/OLS nighttime light data and UP data for 1990-2010. At the provincial scale, the optimal regression model varies among different provinces. The linear regression model is the optimal regression model for more than 60% of the provinces. In addition, the comparison results show that at the national, provincial, and city levels, the fitting results of the UP based on DMSP/OLS nighttime light data are better than those of the NAP. Therefore, DMSP/OLS nighttime light data can be used to effectively retrieve the UP of a large-scale region. In the context of frequent population flows between urban and rural areas in China and difficulty in obtaining accurate UP data, this study provides a timely and effective method for solving this problem.

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How does sprawl differ across cities in China? A multi-scale investigation using nighttime light and census data

Cited by 173 publications

References 54 publications

Satellite Monitoring of Urban Land Change in the Middle Yangtze River Basin Urban Agglomeration, China between 2000 and 2016

Satellite Monitoring of Urban Land Change in the Middle Yangtze River Basin Urban Agglomeration, China between 2000 and 2016

Characterizing the Growth Patterns of 45 Major Metropolitans in Mainland China Using DMSP/OLS Data

A Simulation Study on the Urban Population of China Based on Nighttime Light Data Acquired from DMSP/OLS

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