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Abstract Seeking spatiotemporal patterns about how citizens interact with the urban space is critical for understanding how cities function. Such interactions were studied in various forms focusing on patterns of people’s 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 stochastic 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’s interactions with their activity places in different urban settings. To test consistency of our findings, we used geo-located tweets to derive the activity places in Twitter users’ 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’ 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. 

Background: The amount of literature on environmental migration is increasing. However, existing studies exhibit contradictory results. A systematic synthesis of the environment–migration relationship is much needed. Objective: This study summarizes research findings, calculates the effect sizes of environmental stressors, identifies publication bias, and investigates heterogeneous environmental effects on migration. Methods: We collected 3,380 estimates from 128 studies published between 2000 and 2020 to explore the environment–migration relationship and performed weighted instrumental variable regression to unveil the heterogeneous environmental effects on out- and net migration. Results: The majority of environmental stressors were not important predictors of out- and net migration. Among the results showing environmental impacts on migration, 58% and 68% reported that environmental stressors increased out- and net migration, respectively, while 58% reported that environmental stressors decreased in-migration. The overall environmental impact on migration was small; however, disaster-related stressors showed a medium effect, and rapid-onset stressors had a stronger impact than slow-onset ones. Multivariate meta-regression analyses demonstrated that environmental stressors were more likely to trigger internal migration than international migration and that developed countries were less likely to experience out-migration. Rapid-onset environmental stressors did not increase out-migration but played an important role in decreasing net migration toward environmentally stressed areas. Meanwhile, we also found a publication bias toward studies showing a positive relationship between environmental stressors and migration in the previous environmental migration literature. Conclusions: Environmental stressors may affect migration; however, the environmental effect depends on migration measurements, environmental stressors' forces and rapidity, and the context in which migration takes place. Contribution: This study contributes to migration studies by synthesizing and validating the environment–migration relationship and enhancing our understanding of how and under what circumstances environmental stressors may affect migration.