A surface network based method for studying urban hierarchies by night time light remote sensing data

ABSTRACT

Urban hierarchies are closely related to economic growth, urban planning and sustainable urban development. Due to the limited availability of reliable statistical data at fine scales, most existing studies on urban hierarchy characterization failed to capture the detailed urban spatial structure information. Previous studies have demonstrated that night time light data are correlated with many urban socio-economic indicators and hence can be used to characterize urban hierarchies. This paper presents a novel method for studying urban hierarchies from night time light data. Night time light data were first conceptualized as continuous mathematical surfaces, termed night time light surfaces. From the morphology of these surfaces the corresponding surface networks were derived. Hereafter, a night time light intensity (NTLI) graph was defined to describe the morphology of the surface network. Then, structural similarity between the night time light surfaces of any two different cities was calculated via a threshold-based maximum common induced graph searching algorithm. Finally, urban hierarchies were defined on the basis of the structural similarities between different cities. Using the 2015 annual NPP-VIIRS night time light data, the urban hierarchies of 32 major cities in China were successfully examined. The results are highly consistent with the reference urban hierarchies.

KEYWORDS:

• Structural similarity
• surface networks
• urban hierarchy
• night time light
• graph theory

Acknowledgments

We are grateful to Prof. May Yuan and the three anonymous referees for their valuable comments and suggestions.

Disclosure statement

No potential conflict of interest was reported by the authors.

Supplymentary material

Supplymental data for this article can be accessed here.

Additional information

Funding

This work was supported by the National Key R&D Program of China [No. YS2017YFGH000441]; the National Natural Science Foundation of China [No. 41701462, 41801343, and 41871331]; the Major Program of National Social Science Foundation of China [No. 17ZDA068]; The China Postdoctoral Science Foundation [No. 2018M641960 and 2018M641961]; and the Fundamental Research Funds for the Central Universities of China.

Notes on contributors

Bin Wu

Bin Wu is a postdoc researcher at the East China Normal University, China. He obtained his PhD in computer science in 2018 from the same university. His fields of interests are urban remote sensing, LiDAR, and spatio-temporal analysis.

Bailang Yu

Bailang Yu received the B.S. and Ph.D. degrees in cartography and geographic information systems from East China Normal University, Shanghai, China, in 2002 and 2009, respectively. He is currently a Professor with the Key Laboratory of Geographic Information Science, Ministry of Education, East China Normal University, Shanghai, where he is also with the School of Geographic Sciences. His research interests include urban remote sensing, nighttime light remote sensing, LiDAR, and object-based methods.

Shenjun Yao

Shenjun Yao is an Associate Professor at East China Normal University. Her research interests focus on how the geographical information science and technology can be applied to the transportation and public health, as well as application of social sensing geodata.

Qiusheng Wu

Qiusheng Wu is an Assistant Professor in the Department of Geography at Binghamton University, State University of New York. His research interests focus on Geographic Information Science (GIS), remote sensing, and environmental modeling.

Zuoqi Chen

Zuoqi Chen received the PhD degree from East China Normal University, Shanghai, China, in 2017. Currently, he is a postdoctoral fellow with the Key Laboratories of Geographic Information Science (Ministry of Education) and School of Geographic Sciences, East China Normal University, Shanghai, China. His interested research fields contain urban remote sensing, nighttime light remote sensing, and development of GIS.

Jianping Wu

Jianping Wu received the M.S. degree from Peking University, Beijing, China, in 1986, and the Ph.D. degree from East China Normal University, Shanghai, China, in 1996. He is currently a Professor with East China Normal University. His research interests include remote sensing and geographic information system.