Identification method and empirical study of urban industrial spatial relationship based on POI big data: a case of Shenyang City, China

Abstract

The industrial spatial relationship is a cross-cutting subject of economic geography and geographical information science which has considerable significance to promote a sustainable planning and development of regional economic system. Taking Shenyang City as our study area and using the location information of manufacturing units and automobile sales outlets extracted from points of interest (POI), we investigated the spatial relationship between the two industries from integration, correlation and coordination perspective. Based on spatial statistical analyses, the equipment manufacturing industry and the automobile sales industry in Shenyang City showed a spatial complementary integration, weak spatial correlation, and coordination with scale dependence and spatial heterogeneity in 2018. This distribution characteristic is attributed to: 1) local policy factors (i.e., that industrial land should be located in the periphery of the city or outside the Second Ring Road), and 2) the economic factors (i.e., that the degree of dependence of the equipment manufacturing industry and automobile sales industry were also influenced by external factors such as costs). These results improved the current industrial spatial relationship analysis by developing a new framework based on POI big data in order to accelerate a coordinated development between manufacturing and service industries and to promote the construction of industrial ecosystem.

Keywords

Human-land relationship / POI / Space organization / Industrial ecology

Declaration of Competing Interest

The authors declare no conflict of interest.

Acknowledgments

This study was supported by the National Natural Science Foundation of China (Grant No. 41971166; 41701466), the Shenyang Young and Middle-aged Scientific and Technological Talents Program (Grant No. RC190444), and the Youth Innovation Promotion Association of Chinese Academy of Sciences (Xue Bing, Grant No. 2016181).

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