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ISPRS Int. J. Geo-Inf. 2016, 5(7), 112; doi:10.3390/ijgi5070112

Evaluating Trade Areas Using Social Media Data with a Calibrated Huff Model

1
State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, 129 Luoyu Road, Wuhan 430079, China
2
Collaborative Innovation Center of Geospatial Technology, Wuhan University, 129 Luoyu Road, Wuhan 430079, China
3
Department of Geography, Kent State University, 413 McGilvrey Hall, Kent, OH 44242, USA
*
Authors to whom correspondence should be addressed.
Academic Editors: Alexander Zipf, David Jonietz, Vyron Antoniou, Linda See and Wolfgang Kainz
Received: 23 May 2016 / Revised: 3 July 2016 / Accepted: 8 July 2016 / Published: 12 July 2016
(This article belongs to the Special Issue Volunteered Geographic Information)
View Full-Text   |   Download PDF [3071 KB, uploaded 12 July 2016]   |  

Abstract

Delimitating trade areas is a major business concern. Today, mobile communication technologies make it possible to use social media data for this purpose. Few studies however, have focused on methods to extract suitable samples from social media data for trade area delimitation. In our case study, we divided Beijing into regular grid cells and extracted activity centers for each social media user. Ten sample sets were obtained by selecting users based on the retail agglomerations they visited and aggregating user activity centers to each grid cell. We calculated distance and visitation frequency attributes for each user and each grid cell. The distance value of a grid cell is the average distance of user activity centers in this grid cell to a retail agglomeration. The visitation frequency of a grid cell refers to the average count of visits to retail agglomerations by user activity centers for a cell. The calculated attribute values of 10 sets were input into a Huff model and the delimitated trade areas were evaluated. Results show that sets obtained by aggregating user activity centers have a better delimitating effect than sets obtained without aggregation. Differences in the distribution and intensity of trade areas also became apparent. View Full-Text
Keywords: trade area; social media; user selection; spatial aggregation; Huff model trade area; social media; user selection; spatial aggregation; Huff model
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Wang, Y.; Jiang, W.; Liu, S.; Ye, X.; Wang, T. Evaluating Trade Areas Using Social Media Data with a Calibrated Huff Model. ISPRS Int. J. Geo-Inf. 2016, 5, 112.

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