# Calibrating the dynamic Huff model for business analysis using location big data

arXiv: https://arxiv.org/abs/2003.10857 · HTML: https://mlsanity.com/paper/2003.10857/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Mar 2020.

## Built on by (most cited first, 3 of 3)

- [The role of alcohol outlet visits derived from mobile phone location data in enhancing domestic violence prediction at the neighborhood level](https://mlsanity.com/paper/2203.04088/) (2022)
- [A Deep Learning Representation of Spatial Interaction Model for Resilient Spatial Planning of Community Business Clusters](https://mlsanity.com/paper/2401.04849/) (2024)
- [Urban-STA4CLC: Urban Theory-Informed Spatio-Temporal Attention Model for Predicting Post-Disaster Commercial Land Use Change](https://mlsanity.com/paper/2508.08976/) (2025)

## Abstract (arXiv)

The Huff model has been widely used in location-based business analysis for delineating a trading 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, we 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 ten most populated U.S. cities. By comparing the hourly visit patterns of two types of stores, we 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. We 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.