Does China's City-Size Distribution Present a Flat Distribution Trend? A Socioeconomic and Spatial Size Analysis From DMSP-OLS Nighttime Light Data

Inconsistent measurements of city-size and a lack of time-series information on urban socioeconomic development have hindered determining whether China's city-size distribution (CD) follows a Pareto distribution according to multiple perspectives. This article has attempted to evaluate China's CD based on the defense meteorological satellite program- operational line-scan system (DMSP-OLS) nighttime light data in terms of socioeconomic size (SS) and spatial size (SC). First, city size was defined from the DMSP-OLS data. Then, whether China's CD followed a Pareto distribution was evaluated from different perspectives. The results show that China's CD from 1995 to 2015 presents a flat distribution trend; the flat distribution trend of the SC is more obvious than that of the SS; “borrowed size” has become an important reason for the flat trend of China's CD; and residential suburbanization, transportation cost reductions, local government policies, and land finances could effectively explain the CD differences in the flat trends between the SS and SC. This article offers an effective means for quantifying and comparing CD in long time series at a large scale (e.g., national scale or regional scale) and provides a scientific decision basis for governments to build a reasonable CD system in China.

[1]  Chen Jin,et al.  The Study on Urbanization Process in China Based on DMSP/OLS Data: Development of a Light Index for Urbanization Level Estimation , 2003 .

[2]  Kaifang Shi,et al.  Does China’s Urban Development Satisfy Zipf’s Law? A Multiscale Perspective from the NPP-VIIRS Nighttime Light Data , 2020, International journal of environmental research and public health.

[3]  J. Martori,et al.  Understanding Airbnb spatial distribution in a southern European city: The case of Barcelona , 2020 .

[4]  Guanghua Wan,et al.  The size distribution of cities in China: Evolution of urban system and deviations from Zipf’s law , 2020 .

[5]  G. Henebry,et al.  Contemporary evolution and scaling of 32 major cities in China. , 2018, Ecological applications : a publication of the Ecological Society of America.

[6]  X. Gabaix.  Zipf's Law for Cities: An Explanation , 1999 .

[7]  P. Krugman.  Confronting the Mystery of Urban Hierarchy , 1996 .

[8]  Paolo Veneri,et al.  City size distribution across the OECD: Does the definition of cities matter? , 2016, Comput. Environ. Urban Syst..

[9]  Bin Jiang,et al.  Zipf’s law for all the natural cities around the world , 2014, Int. J. Geogr. Inf. Sci..

[10]  Deren Li,et al.  Anisotropic characteristic of artificial light at night – Systematic investigation with VIIRS DNB multi-temporal observations , 2019, Remote Sensing of Environment.

[11]  Zhifeng Liu,et al.  Detecting the 20 year city-size dynamics in China with a rank clock approach and DMSP/OLS nighttime data , 2015 .

[12]  Zhe Zhu,et al.  Understanding an urbanizing planet: Strategic directions for remote sensing , 2019, Remote Sensing of Environment.

[13]  Ying Ge,et al.  The size distribution of Chinese cities , 2005 .

[14]  Juan F. Vargas,et al.  Measuring the size and growth of cities using nighttime light , 2020 .

[15]  Yanguang Chen,et al.  Scaling laws and indications of self-organized criticality in urban systems , 2008 .

[16]  Yan Sun,et al.  Spatial and Temporal Dimensions of Urban Expansion in China. , 2015, Environmental science & technology.

[17]  Yaoqiu Kuang,et al.  China׳s 19-year city-level carbon emissions of energy consumptions, driving forces and regionalized mitigation guidelines , 2014 .

[18]  Chunyang He,et al.  How does sprawl differ across cities in China? A multi-scale investigation using nighttime light and census data , 2016 .

[19]  G. Chi,et al.  The impacts of population and agglomeration development on land use intensity: New evidence behind urbanization in China , 2020 .

[20]  Cao Ziyan,et al.  Correction of DMSP/OLS Night-time Light Images and Its Application in China , 2015 .

[21]  Bailang Yu,et al.  Detecting spatiotemporal dynamics of global electric power consumption using DMSP-OLS nighttime stable light data , 2016 .

[22]  Michael Jendryke,et al.  A preliminary investigation of Luojia-1 night-time light imagery , 2019, Remote Sensing Letters.

[23]  Taotao Deng,et al.  What caused differential impacts on high-speed railway station area development? Evidence from global nighttime light data , 2020 .

[24]  M. Modica.  The impact of the European Union integration on the city size distribution of the Member States , 2017 .

[25]  M. Bennett,et al.  Advances in using multitemporal night-time lights satellite imagery to detect, estimate, and monitor socioeconomic dynamics , 2017 .

[26]  Wei Li,et al.  Modeling population density based on nighttime light images and land use data in China , 2018 .

[27]  Annemarie Schneider,et al.  Expansion and growth in Chinese cities, 1978–2010 , 2014 .

[28]  Lin Zhang,et al.  Spatiotemporal Analysis of Venezuela's Nighttime Light During the Socioeconomic Crisis , 2020, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[29]  Pareto’s Law and City Size in China: Diverging Patterns in Land and People , 2016 .

[30]  Yun Chen,et al.  Modeling spatiotemporal CO2 (carbon dioxide) emission dynamics in China from DMSP-OLS nighttime stable light data using panel data analysis , 2016 .

[31]  Jianping Wu,et al.  Evaluating the Ability of NPP-VIIRS Nighttime Light Data to Estimate the Gross Domestic Product and the Electric Power Consumption of China at Multiple Scales: A Comparison with DMSP-OLS Data , 2014, Remote. Sens..

[32]  Yan-sui Liu,et al.  The spatio-temporal patterns of urban–rural development transformation in China since 1990 , 2016 .

[33]  C. Ding.  Policy and praxis of land acquisition in China , 2007 .

[34]  C. Elvidge,et al.  Remote sensing of night lights: A review and an outlook for the future , 2020, Remote Sensing of Environment.

[35]  M. Ma,et al.  A multiscale analysis of the effect of urban expansion on PM2.5 concentrations in China: Evidence from multisource remote sensing and statistical data , 2020 .

[36]  S. Devadoss,et al.  Do the world’s largest cities follow Zipf’s and Gibrat’s laws? , 2014 .

[37]  Jianping Wu,et al.  Estimating House Vacancy Rate in Metropolitan Areas Using NPP-VIIRS Nighttime Light Composite Data , 2015, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[38]  Deren Li,et al.  Monitoring hourly night-time light by an unmanned aerial vehicle and its implications to satellite remote sensing , 2020 .

[39]  Adam Okulicz-Kozaryn,et al.  The city size distribution debate: Resolution for US urban regions and megalopolitan areas , 2012 .

[40]  Xizhe Peng,et al.  China’s Demographic History and Future Challenges , 2011, Science.

[41]  City Size Distribution in China: Are Large Cities Dominant? , 2009 .

[42]  Mikhail Zhizhin,et al.  A Fifteen Year Record of Global Natural Gas Flaring Derived from Satellite Data , 2009 .

[43]  Bailang Yu,et al.  A Spatial-Socioeconomic Urban Development Status Curve from NPP-VIIRS Nighttime Light Data , 2019, Remote. Sens..

[44]  Zhifeng Liu,et al.  Urban expansion dynamics and natural habitat loss in China: a multiscale landscape perspective , 2014, Global change biology.

[45]  Tingwei Zhang.  Land market forces and government's role in sprawl , 2000 .

[46]  Zhongqi Deng,et al.  Re-study on Chinese city size and policy formation , 2020 .

[47]  Shasha Lu,et al.  Assessment on the urbanization strategy in China: Achievements, challenges and reflections , 2018 .

[48]  Massimo Riccaboni,et al.  Distribution of City Size: Gibrat, Pareto, Zipf , 2019, The Mathematics of Urban Morphology.

[49]  J. Bogaert,et al.  Analysis of city size distribution in Ethiopia: Empirical evidence from 1984 to 2012 , 2019, Journal of Urban Management.

[50]  Shougeng Hu,et al.  Zipf’s law and city size distribution: A survey of the literature and future research agenda , 2018 .

[51]  Bin Jiang,et al.  Zipf's law for all the natural cities in the United States: a geospatial perspective , 2010, Int. J. Geogr. Inf. Sci..

[52]  George Kingsley Zipf,et al.  Human behavior and the principle of least effort , 1949 .

[53]  X. Gabaix,et al.  Rank − 1 / 2: A Simple Way to Improve the OLS Estimation of Tail Exponents , 2007 .

[54]  Chunyang He,et al.  Environmental degradation in the urban areas of China: Evidence from multi-source remote sensing data , 2017 .

[55]  C. Elvidge,et al.  Spatial analysis of global urban extent from DMSP-OLS night lights , 2005 .

[56]  Huadong Guo,et al.  Urban sprawl in provincial capital cities in China: evidence from multi-temporal urban land products using Landsat data. , 2019, Science bulletin.

[57]  Jianguo Wu,et al.  Behind the rapid expansion of urban impervious surfaces in China: Major influencing factors revealed by a hierarchical multiscale analysis , 2016 .

[58]  J. Eeckhout.  Gibrat's Law for (All) Cities , 2004 .

[59]  Rasmus Fensholt,et al.  Detecting and monitoring long-term landslides in urbanized areas with nighttime light data and multi-seasonal Landsat imagery across Taiwan from 1998 to 2017 , 2019, Remote Sensing of Environment.

[60]  Kenneth T. Rosen,et al.  The Size Distribution of Cities: An Examination of the Pareto Law and Primacy , 1980 .

[61]  Yanguang Chen.  The rank-size scaling law and entropy-maximizing principle , 2011, 1104.5630.