Publication Type

Conference Proceeding Article

Version

acceptedVersion

Publication Date

12-2023

Abstract

Conventional methodologies for new retail store catchment area and footfall estimation rely on ground surveys which are costly and time-consuming. This study augments existing research in footfall estimation through the innovative integration of mobility data and road network to create population-weighted centroids and delineate residential neighbourhoods via a community detection algorithm. Our findings are then used to enhance Huff Model which is commonly used in site selection and footfall estimation. Our approach demonstrated the vast potential residing within big data where we harness the power of mobility data and road network information, offering a cost-effective and scalable alternative. It obviates the reliance on often outdated census data and government urban planning records, positioning itself as a formidable driver of informed retail strategy. In doing so, our approach is poised to deliver substantial value to the retail industry.

Keywords

Urban planning, Mobility data, Data-driven community detection, Retail strategy, Predictive analytics

Discipline

Databases and Information Systems | Numerical Analysis and Scientific Computing

Research Areas

Intelligent Systems and Optimization

Publication

2023 IEEE International Conference on Big Data: Sorrento, Italy, December 15-18: Proceedings

First Page

1533

Last Page

1538

ISBN

9798350324457

Identifier

10.1109/BigData59044.2023.10386152

Publisher

IEEE

City or Country

Piscataway, NJ

Copyright Owner and License

Authors

Creative Commons License

Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License
This work is licensed under a Creative Commons Attribution-NonCommercial-Share Alike 4.0 International License.

Additional URL

https://doi.org/10.1109/BigData59044.2023.10386152

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