Publication Type

Journal Article

Version

acceptedVersion

Publication Date

4-2018

Abstract

Economic and urban planning agencies have strong interest in tackling the hard problem of predicting the odds of survival of individual retail businesses. In this work, we tap urban mobility data available both from a location-based intelligence platform, Foursquare, and from public transportation agencies, and investigate whether mobility-derived features can help foretell the failure of such retail businesses, over a 6 month horizon, across 10 distinct cities spanning the globe. We hypothesise that the survival of such a retail outlet is correlated with not only venue-specific characteristics but also broader neighbourhood-level effects. Through careful statistical analysis of Foursquare and taxi mobility data, we uncover a set of discriminative features, belonging to the neighbourhood's static characteristics, the venue-specific customer visit dynamics, and the neighbourhood's mobility dynamics. We demonstrate that classifiers trained on such features can predict such survival with high accuracy, achieving approximately 80% precision and recall across the cities. We also show that the impact of such features varies across new and established venues and across different cities. Besides achieving a significant improvement over past work on business vitality prediction, our work demonstrates the vital role that mobility dynamics plays in the economic evolution of a city.

Keywords

Urban computing, location-based services, predictive modeling, spatio-temporal patterns

Discipline

Databases and Information Systems | E-Commerce | Numerical Analysis and Scientific Computing

Research Areas

Software and Cyber-Physical Systems

Publication

Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies

Volume

2

Issue

3

First Page

100: 1

Last Page

22

ISSN

2474-9567

Identifier

10.1145/3264910

Publisher

Association for Computing Machinery (ACM)

Copyright Owner and License

Authors

Additional URL

https://doi.org/10.1145/3264910

Download

Share

COinS