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Solving Competitive Location Problems with Social Media Data Based on Customers’ Local Sensitivities

1
School of Geography and Tourism, Anhui Normal University, Wuhu 241003, China
2
Engineering Technology Research Center of Resources Environment and GIS, Anhui Province, Wuhu 241003, China
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State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China
4
Collaborative Innovation Center of Geospatial Technology, Wuhan University, Wuhan 430079, China
5
Faculty of Geomatics, East China University of Technology, Nanchang 330000, China
6
Wuhan Land Use and Urban Spatial Planning Research Center, Hubei Province, Wuhan 430079, China
7
Wuhan Land Resource and Planning Information Center, Hubei Province, Wuhan 430079, China
*
Author to whom correspondence should be addressed.
ISPRS Int. J. Geo-Inf. 2019, 8(5), 202; https://doi.org/10.3390/ijgi8050202
Received: 18 March 2019 / Revised: 24 April 2019 / Accepted: 2 May 2019 / Published: 4 May 2019
(This article belongs to the Special Issue Convergence of GIS and Social Media)
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PDF [3003 KB, uploaded 4 May 2019]
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Abstract

Competitive location problems (CLPs) are a crucial business concern. Evaluating customers’ sensitivities to different facility attractions (such as distance and business area) is the premise for solving a CLP. Currently, the development of location-based services facilitates the use of location data for sensitivity evaluations. Most studies based on location data assumed the customers’ sensitivities to be global and constant over space. In this paper, we proposed a new method of using social media data to solve competitive location problems based on the evaluation of customers’ local sensitivities. Regular units were first designed to spatially aggregate social media data to extract samples with uniform spatial distribution. Then, geographically weighted regression (GWR) and the Huff model were combined to evaluate local sensitivities. By applying the evaluation results, the captures for different feasible locations were calculated, and the optimal location for a new retail facility could be determined. In our study, the five largest retail agglomerations in Beijing were taken as test cases, and a possible new retail agglomeration was located. The results of our study can help people have a better understanding of the spatial variation of customers’ local sensitivities. In addition, our results indicate that our method can solve competitive location problems in a cost-effective way. View Full-Text
Keywords: competitive location problem; social media; customers’ local sensitivities; Huff model; geographically weighted regression competitive location problem; social media; customers’ local sensitivities; Huff model; geographically weighted regression
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Jiang, W.; Wang, Y.; Dou, M.; Liu, S.; Shao, S.; Liu, H. Solving Competitive Location Problems with Social Media Data Based on Customers’ Local Sensitivities. ISPRS Int. J. Geo-Inf. 2019, 8, 202.

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