数字城市感知：多层次方法
Digital Urban Sensing: A Multi-layered Approach
摘要：
对人类流动的研究越来越依赖于数字传感，即数字技术促进的人类活动的大规模记录。然而，在从这些来源看到的模式中，变异性和人口代表性问题仍然是解释从这些记录中收集的任何结果的主要挑战。本研究通过比较出租车、地铁和花旗自行车旅行、移动应用程序记录、地理标记的Twitter数据以及纽约市五个行政区的311个服务请求的空间和时间活动分布来探讨这些问题。比较揭示了这些数据集之间的空间和时间模式有很大的不同，强调了单个数据集的能力的局限性，以代表城市动态的整体。我们进一步解释这些差异，通过分解的空间分布与工作-居住平衡和不同的人口群体使用不同的活动的倾向。然而，这些差异也突出了利用多元性来创建城市动态的多层次模型的机会。我们展示了这种模型的能力，以推进城市分区和社会经济建模-数字城市传感的两个常见的应用。
英文摘要：
Studies of human mobility increasingly rely on digital sensing, the large-scale recording of human activity facilitated by digital technologies. Questions of variability and population representativity, however, in patterns seen from these sources, remain major challenges for interpreting any outcomes gleaned from these records. The present research explores these questions by providing a comparison of the spatial and temporal activity distributions seen from taxi, subway and Citi Bike trips, mobile app records, geo-tagged Twitter data as well as 311 service requests in the five boroughs of New York City. The comparison reveals substantially different spatial and temporal patterns amongst these datasets, emphasizing limitations in the capacity of individual datasets to represent urban dynamics in their entirety. We further provide interpretations on these differences by decomposing the spatial distributions with working-residential balance and different propensities for demographic groups to use different activities. Nevertheless, the differences also highlight the opportunity to leverage the plurality to create multi-layered models of urban dynamics. We demonstrate the capacity of such models to advance urban zoning and socio-economic modeling - two common applications of digital urban sensing.
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