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.