Understanding crowd activities at large-scale and diagnosing existing problems of planning on densely-populated campus are fundamentally hard through traditional ways of measurement and management. In this paper, we demonstrate how to collect data from ubiquitous WiFi networks (WLAN), and further to characterize the mobility of campus residents by exploring time-frequency patterns with spatial context. On the campus of Tsinghua University (where everyday nearly 60000 mobile devices appear in the public areas of more than 110 buildings), we obtain large-scale observations on physical activities, and provide insights for better diagnosing of WiFi hotspots.<\/p>\n","protected":false},"excerpt":{"rendered":"
Understanding crowd activities at large-scale and diagnosing existing problems of planning on densely-populated campus are fundamentally hard through traditional ways of measurement and management. In this paper, we demonstrate how to collect data from ubiquitous WiFi networks (WLAN), and further to characterize the mobility of campus residents by exploring time-frequency patterns with spatial context. On 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