Publication Type
Conference Proceeding Article
Version
publishedVersion
Publication Date
11-2020
Abstract
According to Huff, trade area is defined as “a geographically delineated region containing potential customers for whom there exists a probability greater than zero of their purchasing a given class of products or services offered for sale by a particular firm or by a particular agglomeration of firms”. Several methods to delineate a store trade area have been proposed over the years. For drive-time or travel distance analysis method, the trade area is delineated according to how far or how long the customers are willing to travel to patronise the store. Another commonly used method is the Huff Model which assumes that consumer decisions are probabilistic and not deterministic. This model derived the probability (p_ij) that a consumer at location j will patronize the store in location i. In ArcGIS Network Analyst, the location-allocation function helped to find the optimal locations for facilities to serve a set of demand points base on the user-defined objective such as maximising market share.
Keywords
Geospatial Analytics, Spatial Lagged Sum, Spatially Constrained Clustering, Trade Area Delineation, MITB student
Discipline
Numerical Analysis and Scientific Computing | Sales and Merchandising
Publication
ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems 28th SIGSPATIAL GIS 2020
First Page
661
Last Page
662
ISBN
9781450380195
Identifier
10.1145/3397536.3428352
Publisher
ACM
City or Country
New York
Embargo Period
5-10-2021
Citation
LIM, Hui Ting.
A geospatial analytics approach to delineate trade areas for Quick Service Restaurants (QSR) in Singapore. (2020). ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems 28th SIGSPATIAL GIS 2020. 661-662.
Available at: https://ink.library.smu.edu.sg/sis_research/5918
Copyright Owner and License
Publisher
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Additional URL
https://doi.org/10.1145/3397536.3428352