ABSTRACT The temporal nature of humans interaction with Points of Interest (POIs) in cities can differ depending on place type and regional location. Times when many people are likely to visit restaurants (place type) in Italy, may differ from times when many people are likely to visit restaurants in Lebanon (i.e. regional differences). Geosocial data are a powerful resource to model these temporal differences in cities, as traditional methods used to study cross-cultural differences do not scale to a global level. As cities continue to grow in population and economic development, research identifying the social and geophysical (e.g., climate) factors that influence city function remains important and incomplete. In this work, we take a quantitative approach, applying dynamic time warping and hierarchical clustering on temporal signatures to model geosocial temporal patterns for Retail and Restaurant Facebook POIs hours of operation for more than 100 cities in 90 countries around the world. Results show cities’ temporal patterns cluster to reflect the cultural region they represent. Furthermore, temporal patterns are influenced by a mix of social and geophysical factors. Trends in the data suggest social factors influence unique drops in temporal signatures, and geophysical factors influence when daily temporal patterns start and finish.

中文翻译：

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城市地理社会时间特征的全球关注点运营时间分析

摘要人类与城市中的兴趣点 (POI) 交互的时间性质可能因地点类型和区域位置而异。在意大利，很多人可能去餐馆的时间（地点类型）可能与在黎巴嫩很多人可能去餐馆的时间不同（即区域差异）。地理社会数据是模拟城市中这些时间差异的强大资源，因为用于研究跨文化差异的传统方法无法扩展到全球水平。随着城市人口和经济发展的持续增长，确定影响城市功能的社会和地球物理（例如气候）因素的研究仍然重要且不完整。在这项工作中，我们采用定量方法，对时间特征应用动态时间扭曲和层次聚类，为全球 90 个国家/地区的 100 多个城市的零售和餐厅 Facebook POI 的营业时间建模地理社会时间模式。结果显示城市的时间模式集群以反映它们所代表的文化区域。此外，时间模式受到社会和地球物理因素混合的影响。数据趋势表明，社会因素会影响时间特征的独特下降，而地球物理因素会影响每日时间模式的开始和结束时间。时间模式受社会和地球物理因素的综合影响。数据趋势表明，社会因素会影响时间特征的独特下降，而地球物理因素会影响每日时间模式的开始和结束时间。时间模式受社会和地球物理因素的综合影响。数据趋势表明，社会因素会影响时间特征的独特下降，而地球物理因素会影响每日时间模式的开始和结束时间。