Location Big Data for Business Analytics

Reference: Yunlei Liang, Song Gao, Yuxin Cai, Natasha Z. Foutz, Lei Wu. (2020) Calibrating the dynamic Huff model for business analysis using location big data. Transactions in GIS, 24(3), 681-703.

Abstract: The Huff model has been widely used in location‐based business analysis to delineate a trade area containing a store’s potential customers. 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 at a fine spatio‐temporal 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 the 10 most populated US 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 versus 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.

The Whole Foods Markets in Los Angeles with their temporal visit probability.
The spatial distributions of CBGs that have visit flows to five Whole Foods Markets.
The probability density distribution, empirical cumulative distribution, and log-log plots of visitors’ distance from home to supermarkets and grocery stores (NACIS: 445110) and to department stores (NACIS: 452210) in the top 10 most populated cities in US.
(a) Estimated market share of five Whole Foods Market stores in Los Angeles using the original Huff model; and (b) Actual market share derived from the SafeGraph visit database.

Prof. Michael F. Goodchild visited UW-Madison

Recently, Prof. Mike Goodchild was invited to visit our lab and the Department of Geography at the University of Wisconsin-Madison. Prof. Goodchild is the Emeritus Professor of Geography at the University of California, Santa Barbara. He was elected member of the National Academy of Sciences and the American Academy of Arts and Sciences, etc. He gave a talk titled “Geography and GIScience: An Evolving Relationship” in the department Yi-Fu Tuan Lecture series on Friday, April 19th, shared his view of how GIScience and Geography evolved together during the past decades.

The GeoDS lab also invited Prof. Goodchild to join our research group meeting. Four lab members presented their recent works and received insightful suggestions and comments from Prof. Goodchild.

GeoDS Lab at the 2019 AAG Annual Meeting

During the last week (April 3-7), six GeoDS lab members have actively participated in the 2019 AAG Annual meeting and successfully presented their work. Especially congratulations to Yuhao Kang who won the first place in the Robert Raskin Student best paper competition!

Yuhao Kang presented his work titled “Human Emotions at Different Places: A Ranking of Happiest Tourist Attractions around the World Based on Facial Expressions and Spatial Clustering Analysis” in the Cyberinfrastructure Specialty Group Student paper competition Session. [Abstract]. Robert Raskin Student Competition 2019: http://gis.cas.sc.edu/cisg/?page_id=126

Yunlei Liang presented her work titled “Optimizing Bus Stop Spacing Using a Spatial Interaction Coverage Model and the Maximal Covering Location Problem Model” in the Spatial Analysis and Modeling Specialty Group Student paper competition Session. [Abstract]

Mingxiao Li presented his work titled “Reconstruction of human movement trajectories from large-scale low-frequency mobile phone data” in the GeoAI and Deep Learning Symposium. [Abstract]

Yuqi Gao presented her work named “Analyzing Regional Economic Indicators from Transportation Network Analytics” in the Automated GISci for Network-based Decisions Session. [Abstract]

Timothy Prestby presented his work titled “Linking Traffic Volume to Economic Development Index Using Big Data and Gravity Models” in the Urban Geography Poster Session. [Poster]

Professor Song Gao co-organized The 2nd AAG Symposium on GeoAI and Deep Learning for Geospatial Research and was invited to the panel discussions of “Urban Data Science”.

Congrats to all of them! Go Badgers!

Group Photo