Conducting Trade Area Analysis Using Mobile Data: The Case of Michigan’s Super-Regional Shopping Centres
Files
Date
2024-01-03
Authors
Contributor
Advisor
Department
Instructor
Depositor
Speaker
Researcher
Consultant
Interviewer
Narrator
Transcriber
Annotator
Journal Title
Journal ISSN
Volume Title
Publisher
Volume
Number/Issue
Starting Page
5611
Ending Page
Alternative Title
Abstract
The increasing availability of spatial big data has revolutionized data analytics and provided valuable insights into consumer behaviour. Spatial big data has enabled retailers to optimize product assortment, pricing, site selection, and trade area analysis. Mobile location data has further enhanced the analysis of individual consumer mobility patterns, offering a more detailed understanding of movement in various contexts. However, using mobile location data for trade area analysis in retail remains understudied. This study aims to fill this gap by employing advanced methods of trade area analysis using mobile location data. Two research questions guide the study: 1) How effective is mobile location data in modelling shopping centre trade area activity? and 2) How reliable are the derived metrics in reflecting changes in trade area consumer traffic patterns during and after the global COVID-19 pandemic? By addressing these questions, this study enhances our understanding of the potential of mobile location data for trade area analysis in retail. It provides insights into consumer behaviour dynamics during the pandemic.
Description
Keywords
Geospatial Big Data Analytics, mobile location data, shopping centers, spatial big data, trade area analysis
Citation
Extent
8 pages
Format
Geographic Location
Time Period
Related To
Proceedings of the 57th Hawaii International Conference on System Sciences
Related To (URI)
Table of Contents
Rights
Attribution-NonCommercial-NoDerivatives 4.0 International
Rights Holder
Local Contexts
Collections
Email libraryada-l@lists.hawaii.edu if you need this content in ADA-compliant format.