A big data approach to augmenting the Huff model with road network and mobility data for store footfall prediction

• Main Authors: TAN, Ming Hui, TAN, Kar Way, LAU, Hoong Chuin
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2023
• Subjects: Urban planning; Mobility data; Data-driven community detection; Retail strategy; Predictive analytics; Databases and Information Systems; Numerical Analysis and Scientific Computing
• Online Access: https://ink.library.smu.edu.sg/sis_research/8625; https://ink.library.smu.edu.sg/context/sis_research/article/9628/viewcontent/BigDataApproach_Huff_av.pdf
• Institution: Singapore Management University

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 It obviates the reliance on often outdated census data and government urban planning records, positioning itself as a formidable driver of informed retail strategy.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.