Knowledge of people's locations and related mobility patterns are important for many decision-making processes. How to efficiently use the available space, is a common problem in many fields. Wireless Local Area Networks (WLAN) are widely used for locating mobile devices within this network. This study attempts to identify movement from Wi-Fi log data on the Delft University of Technology campus. The proposed method automatically explores people’s movement by firstly, extract stay places, secondly discover movement and finally, identify movement patterns. This method is studied for two spatial levels: (1) at building level, movement between, from and to buildings can be detected, (2) at building-part level, movement between, from and to large indoor regions can be detected. For indoor analysis, the travelled path is estimated using a network graph of the underlying floorplan. This paper shows promising results for mining people’s movement patterns between buildings and indoor building-parts.
|Title of host publication||Proceedings of the 20th AGILE Conference on Geographic Information Science|
|Subtitle of host publication||Societal Geo-innovation|
|Editors||Arnold Bregt, Tapani Sarjakoski, Ron van Lammeren, Frans Rip|
|Number of pages||6|
|Publication status||Published - 2017|
|Event||AGILE 2017: 20th AGILE International Conference on Geographic Information Science - Wageningen, Netherlands|
Duration: 9 May 2017 → 12 May 2017
|Conference||AGILE 2017: 20th AGILE International Conference on Geographic Information Science|
|Period||9/05/17 → 12/05/17|
Griffioen, S., Vermeer, M., Dukai, B., Spek, S. V. D., & Verbree, E. (2017). Exploring indoor movement patterns through eduroam connected wireless devices. In A. Bregt, T. Sarjakoski, R. V. Lammeren, & F. Rip (Eds.), Proceedings of the 20th AGILE Conference on Geographic Information Science: Societal Geo-innovation Wageningen University.