Using Nighttime Light Data and POI Big Data to Detect the Urban Centers of Hangzhou

Ge Lou; Qiuxiao Chen; Kang He; Yue Zhou; Zhou Shi

doi:10.3390/rs11151821

Remote Sensing (Aug 2019)

Using Nighttime Light Data and POI Big Data to Detect the Urban Centers of Hangzhou

Ge Lou,
Qiuxiao Chen,
Kang He,
Yue Zhou,
Zhou Shi

Affiliations

Ge Lou: Department of Regional and Urban Planning, College of Civil Engineering and Architecture, Zhejiang University, Hangzhou 310058, China
Qiuxiao Chen: Department of Regional and Urban Planning, College of Civil Engineering and Architecture, Zhejiang University, Hangzhou 310058, China
Kang He: Institute of Agricultural Remote Sensing and Information Technology Application, College of Environmental and Resource Sciences, Zhejiang University, Hangzhou 310058, China
Yue Zhou: Institute of Agricultural Remote Sensing and Information Technology Application, College of Environmental and Resource Sciences, Zhejiang University, Hangzhou 310058, China
Zhou Shi: Institute of Agricultural Remote Sensing and Information Technology Application, College of Environmental and Resource Sciences, Zhejiang University, Hangzhou 310058, China

DOI: https://doi.org/10.3390/rs11151821
Journal volume & issue: Vol. 11, no. 15
p. 1821

Abstract

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The worldwide development of multi-center structures in large cities is a prevailing development trend. In recent years, China’s large cities developed from a predominantly mono-centric to a multi-center urban space structure. However, the definition and identification city centers is complex. Both nighttime light data and point of interest (POI) data are important data sources for urban spatial structure research, but there are few integrated applications for these two kinds of data. In this study, visible infrared imaging radiometer suite (NPP-VIIRS) nighttime imagery and POI data were combined to identify the city centers in Hangzhou, China. First, the optimal parameters of multi-resolution segmentation were determined by experiments. The POI density was then calculated with the segmentation results as the statistical unit. High−high clustering units were then defined as the main centers by calculating the Anselin Local Moran’s I, and a geographically weighted regression model was used to identify the subcenters according to the square root of the POI density and the distances between the units and the city center. Finally, a comparison experiment was conducted between the proposed method and the relative cut-off_threshold method, and the experiment results were compared with the evaluation report of the master plan. The results showed that the optimal segmentation parameters combination was 0.1 shape and 0.5 compactness factors. Two main city centers and ten subcenters were detected. Comparison with the evaluation report of the master plan indicated that the combination of nighttime light data and POI data could identify the urban centers accurately. Combined with the characteristics of the two kinds of data, the spatial structure of the city could be characterized properly. This study provided a new perspective for the study of the spatial structure of polycentric cities.

Published in Remote Sensing

ISSN: 2072-4292 (Online)
Publisher: MDPI AG
Country of publisher: Switzerland
LCC subjects: Science
Website: http://www.mdpi.com/journal/remotesensing/

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