Developing a dynamic Huff model for business analysis using location big data

Abstract

The Huff model has been widely used in location-based business analysis for delineating a trade area containing potential customers to a store. Calibrating the Huff model and its extensions requires empirical location visit data. Many studies rely on labor-intensive surveys. With the increasing availability of mobile devices, users in location-based platforms share rich multimedia information about their locations in a fine spatiotemporal resolution, which offers opportunities for business intelligence. In this research, I present a time-aware dynamic Huff model (T-Huff) for location-based market share analysis and calibrate this model using large-scale store visit patterns based on mobile phone location data across the ten most populated U.S. cities. By comparing the hourly visit patterns of two types of stores, I demonstrate that the calibrated T-Huff model is more accurate than the original Huff model in predicting the market share of different types of business (e.g., supermarkets vs. department stores) over time. In addition, I applied the developed T-Huff model to understand how mobility has changed due to the lockdown policies during the COVID-19 pandemic. When the pandemic of the novel coronavirus disease (COVID-19) was announced in March 2020, people around the world scattered to stores for groceries and supplies. The dynamics of shopping changed dramatically due to the preparation for quarantine and the following lockdown policies. I use the T-Huff model to estimate and compare the visiting probability of different stores over different time periods (before/after the lockdowns). I am able to identify certain retail/grocery stores that have more/less visits due to the lockdown policies, and I detect whether there are any trends in visiting certain retail establishments and how the visiting patterns have adjusted with lockdowns. I also identify the regional variability where people in large metropolitan areas with a well-developed transit system show less sensitivity to long-distance visits. In addition, several socioeconomic and demographic factors (e.g., median household income) that potentially affect people’s visit decisions are examined and summarized.