In this paper, we present a systematic analysis of large-scale human mobility patterns obtained from a passive Wi-Fi tracking system, deployed across different location typologies. We have deployed a system to cover urban areas served by public transportation systems as well as very isolated and rural areas. Over 4 years, we collected 572 million data points from a total of 82 routers covering an area of 2.8 km². In this paper we provide a systematic analysis of the data and discuss how our low-cost approach can be used to help communities and policymakers to make decisions to improve people’s mobility at high temporal and spatial resolution by inferring presence characteristics against several sources of ground truth. Also, we present an automatic classification technique that can identify location types based on collected data.

在本文中，我们对从部署在不同位置类型的被动 Wi-Fi 跟踪系统获得的大规模人类移动模式进行了系统分析。我们已经部署了一个系统来覆盖由公共交通系统服务的城市地区以及非常偏远的农村地区。4 年多来，我们从 82 台路由器中收集了 5.72 亿个数据点，覆盖面积为 2.8 平方公里². 在本文中，我们对数据进行了系统分析，并讨论了如何使用我们的低成本方法来帮助社区和政策制定者做出决策，通过根据几个基本事实来源推断存在特征，以提高人们在高时间和空间分辨率下的流动性. 此外，我们提出了一种自动分类技术，可以根据收集的数据识别位置类型。