Modeling Interest Networks in Urban Areas: A Comparative Study of Google Places and Foursquare Across Countries

Location-Based Social Networks (LBSNs) are valuable for understanding urban behavior and providing useful data on user preferences. Modeling their data into graphs like interest networks (iNETs) offers important insights for urban area recommendations, mobility forecasting, and public policy development. This study uses check-ins and venue reviews to compare the iNETs resulting from two distinct LBSNs, Foursquare and Google Places. Although these two LBSNs differ in nature, with data varying in regularity and purpose, their resulting iNETs reveal similar urban behavior patterns. When analyzing the impact of socioeconomic, political, and geographic factors on iNET edges — each edge representing users' interests in a pair of regions — only geographic factors showed a significant influence. When studying the granularity of area sizes to model iNETs, we highlight important trade-offs between larger and smaller sizes. Additionally, we propose a methodology to identify clusters of geographically neighboring areas where user interest is strongest, which can be advantageous for understanding urban space usage.