Volume XLII-2/W7
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2/W7, 149-154, 2017
https://doi.org/10.5194/isprs-archives-XLII-2-W7-149-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 4.0 License.
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2/W7, 149-154, 2017
https://doi.org/10.5194/isprs-archives-XLII-2-W7-149-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 4.0 License.

  12 Sep 2017

12 Sep 2017

A CASE STUDY: EXPLORING INDUSTRIAL AGGLOMERATION OF MANUFACTURING INDUSTRIES IN SHANGHAI USING DURANTON AND OVERMAN’S K-DENSITY FUNCTION

S. Tian1, J. Wang1, Z. Gui1,2,3, H. Wu2,3, and Y. Wang2,3 S. Tian et al.
  • 1School of Remote Sensing and Information Engineering, Wuhan University, Wuhan, China
  • 2The State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan, China
  • 3Collaborative Innovation Center of Geospatial Technology, Wuhan University, Wuhan, China

Keywords: Industrial Agglomeration, Point Pattern Analysis, K-density function, Geoprocessing Services, Decision Support in Smart City

Abstract. There has wide academic and policy attention on the issue of scale economy and industrial agglomeration, with most of the attention paid to industrial geography concentration. This paper adopted a scale-independent and distance-based measurement method, K-density function or known as Duranton and Overman (DO) index, to study the manufacturing industries localization in Shanghai, which is the most representative economic development zone in China and East Asia. The result indicates the industry has a growing tendency of localization, and various spatial distribution patterns in different distances. Furthermore, the class of industry also show significant influence on the concentration pattern. Besides, the method has been coded and published on GeoCommerce, a visualization and analysis portal for industrial big data, to provide geoprocessing and spatial decision support.