Trade area analysis using user generated mobile location data

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Human Mobility and Location-Based Analysis Consumer Market Behavior and Pricing Data Management and Algorithms

Abstract

In this paper, we illustrate how User Generated Mobile Location Data (UGMLD) like Foursquare check-ins can be used in Trade Area Analysis (TAA) by introducing a new framework and corresponding analytic methods. Three key processes were created: identifying the activity center of a mobile user, profiling users based on their location history, and modeling users' preference probability. Extensions to traditional TAA are introduced, including customer-centric distance decay analysis and check-in sequence analysis. Adopting the rich content and context of UGMLD, these methods introduce new dimensions to modeling and delineating trade areas. Analyzing customers' visits to a business in the context of their daily life sheds new light on the nature and performance of the venue. This work has important business implications in the field of mobile computing.

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References

2008Understanding individual human mobility patterns 2010Limits of Predictability in Human Mobility 2021Exploring Millions of Footprints in Location Sharing Services 1963A Probabilistic Analysis of Shopping Center Trade Areas 2021The Livehoods Project: Utilizing Social Media to Understand the Dynamics of a City 2021An Empirical Study of Geographic User Activity Patterns in Foursquare 2009Understanding individual human mobility patterns 2011Identifying Important Places in People’s Lives from Cellular Network Data

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