Calibrating the Dynamic Huff Model for Business Analysis Using Location Big Data

This seminar comes from the SafeGraph Data Consortium.

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:

  • Prof. Song Gao presents a time-aware dynamic Huff model (T-Huff) for location-based market share analysis and demonstrates 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.
  • Several socioeconomic and demographic factors (e.g., median household income) that potentially affect people's visit decisions are examined and summarized.


Prof. Song Gao

Geospatial Data Science Lab, University of Wisconsin-Madison

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