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Such interactions were studied in various forms focusing on patterns of people\u2019s presence, action, and transition in the urban environment, which are defined as human-urban interactions in this paper. Using human activity datasets that utilize mobile positioning technology for tracking the locations and movements of individuals, researchers developed\u00a0stochastic models  to uncover preferential return behaviors and recurrent transitional activity structures in human-urban interactions. Ad-hoc heuristics and spatial clustering methods were applied to derive meaningful activity places in those studies. However, the lack of semantic meaning in the recorded locations makes it difficult to examine the details about how people interact with different activity places. In this study, we utilized geographic context-aware Twitter data to investigate the spatiotemporal patterns of people\u2019s interactions with their activity places in different urban settings. To test\u00a0consistency\u00a0of  our findings, we used geo-located tweets to derive the activity places in Twitter users\u2019 location histories over three major U.S. metropolitan areas: Greater Boston Area, Chicago, and San Diego, where the geographic context of each location was inferred from its closest land use parcel. The results showed striking spatial and temporal similarities in Twitter users\u2019 interactions with their activity places among the three cities. By using entropy-based predictability measures, this study not only confirmed the preferential return behaviors as people tend to revisit a few highly frequented places but also revealed detailed characteristics of those activity places.<\/jats:p>","DOI":"10.1007\/s44212-022-00020-2","type":"journal-article","created":{"date-parts":[[2022,12,19]],"date-time":"2022-12-19T04:10:57Z","timestamp":1671423057000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["A tale of three cities: uncovering human-urban interactions with geographic-context aware social media data"],"prefix":"10.1007","volume":"1","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4196-2439","authenticated-orcid":false,"given":"Junjun","family":"Yin","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0888-7964","authenticated-orcid":false,"given":"Guangqing","family":"Chi","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2022,12,19]]},"reference":[{"issue":"1","key":"20_CR1","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1080\/10630731003597306","volume":"17","author":"R Ahas","year":"2010","unstructured":"Ahas, R., Silm, S., J\u00e4rv, O., Saluveer, E., & Tiru, M. 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