Tracking urban activity growth globally with big location data

Area of Science:

• Urban studies
• Geographic Information Science
• Computational Social Science

Background:

• Unprecedented urban growth is reshaping global environments.
• An increasing majority of the world's population resides in urban areas.

Purpose of the Study:

• To analyze urban growth patterns across 100 major cities using a location-based social network dataset.
• To investigate spatial correlations in urban growth profiles between cities.
• To characterize intra-city growth variations and venue competition dynamics.

Main Methods:

• Utilized a longitudinal dataset from Foursquare, a location-based social network.
• Analyzed urban growth across 100 major cities worldwide.
• Developed a metric to assess venue competition based on changes in throughput before and after new venue openings.

Main Results:

• Strong spatial correlation observed: nearby cities share more similar growth profiles than distant ones.
• Higher-than-expected urban growth within cities is highly localized.
• Lower-than-expected growth is more diffuse.
• Bookstores exhibit competitive dynamics, with new openings reducing footfall for existing ones.
• Museums demonstrate a cooperative effect, increasing traffic for nearby venues of the same type.

Conclusions:

• Urban growth exhibits significant spatial dependencies at the city level.
• Intra-city growth patterns are influenced by local density and exhibit distinct localized and diffuse characteristics.
• Venue competition varies by type, with some types competing intensely while others foster synergistic effects.