A global analysis of cities’ geosocial temporal signatures for points of interest hours of operation

Kevin A. Sparks · Gautam Malviya Thakur · Amol Pasarkar · Marie Urban

30 citations0/0 files storedInternational Journal of Geographical Information Systems

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Human Mobility and Location-Based Analysis Urban Design and Spatial Analysis Night-time city culture

Abstract

The temporal nature of humans interaction with Points of Interest (POIs) in cities can differ depending on place type and regional location. Times when many people are likely to visit restaurants (place type) in Italy, may differ from times when many people are likely to visit restaurants in Lebanon (i.e. regional differences). Geosocial data are a powerful resource to model these temporal differences in cities, as traditional methods used to study cross-cultural differences do not scale to a global level. As cities continue to grow in population and economic development, research identifying the social and geophysical (e.g., climate) factors that influence city function remains important and incomplete. In this work, we take a quantitative approach, applying dynamic time warping and hierarchical clustering on temporal signatures to model geosocial temporal patterns for Retail and Restaurant Facebook POIs hours of operation for more than 100 cities in 90 countries around the world. Results show cities’ temporal patterns cluster to reflect the cultural region they represent. Furthermore, temporal patterns are influenced by a mix of social and geophysical factors. Trends in the data suggest social factors influence unique drops in temporal signatures, and geophysical factors influence when daily temporal patterns start and finish.

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References

2008Understanding individual human mobility patterns 2013The Origins of Scaling in Cities 2010Modelling the scaling properties of human mobility 2014Geo-located Twitter as proxy for global mobility patterns 2012A Tale of Many Cities: Universal Patterns in Human Urban Mobility 2021The Livehoods Project: Utilizing Social Media to Understand the Dynamics of a City 2017Extracting urban functional regions from points of interest and human activities on location‐based social networks 2015Mining point-of-interest data from social networks for urban land use classification and disaggregation

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