寻求有关公民如何与城市空间互动的时空模式对于理解城市的功能至关重要。以各种形式研究了这种相互作用,重点是人们的存在模式,行动,和城市环境的转变,本文将其定义为人与城市的互动。使用人类活动数据集,利用移动定位技术来跟踪个人的位置和运动,研究人员开发了随机模型来揭示人与城市互动中的优先回报行为和经常性的过渡活动结构。在这些研究中,采用了Ad-hoc启发式方法和空间聚类方法来得出有意义的活动地点。然而,在记录的位置缺乏语义含义,这使得很难检查有关人们如何与不同活动地点互动的细节。在这项研究中,我们利用地理情境感知的Twitter数据来调查不同城市环境中人们与活动场所的互动的时空模式。为了测试我们发现的一致性,我们使用地理定位推文来得出Twitter用户在美国三大大都市地区的位置历史记录中的活动地点:大波士顿地区,芝加哥,还有圣地亚哥,其中每个位置的地理环境是从其最接近的土地利用地块推断出来的。结果显示,在三个城市中,Twitter用户与活动场所的互动在空间和时间上具有惊人的相似性。通过使用基于熵的可预测性度量,这项研究不仅证实了人们倾向于重访一些经常光顾的地方的优先返回行为,而且还揭示了这些活动地方的详细特征。
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.