The use of location based social networks—LBSNs—for diagnosing phenomena in contemporary cities is evolving at a fast pace. However, methodological frameworks for informing urban regeneration at a fine-grain neighborhood scale through LBSNs is still by and large an unchartered territory, which this research seeks to address. This research bridges the knowledge gap by proposing a method to identify urban opportunity spaces for urban regeneration that involves pre-processing, analyzing and interpreting single and overlapped LBSN data. A two-fold perspective—people-based and place-based—is adopted. Data from four LBSNs—Foursquare, Twitter, Google Places and Airbnb—represent the people-based approach as it offers an insight into individual preferences, use and activities. The place-based approach is provided by an illustrative case study. Local unexpected nuances were gathered by the interlinking of data from different LBSNs, and opportunity places for urban regeneration have been recognized, as well as potential itineraries to boost urban liveliness and connectivity at both intra and inter- neighborhood scales. Findings show that overlapping data from various LBSNs enriches the analysis that would previously have relied on a single source.
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