Volume III-2
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., III-2, 77-84, 2016
https://doi.org/10.5194/isprs-annals-III-2-77-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., III-2, 77-84, 2016
https://doi.org/10.5194/isprs-annals-III-2-77-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.

  02 Jun 2016

02 Jun 2016

EVENT DETECTION USING MOBILE PHONE MASS GPS DATA AND THEIR RELIAVILITY VERIFICATION BY DMSP/OLS NIGHT LIGHT IMAGE

Akiyama Yuki1, Ueyama Satoshi2, Shibasaki Ryosuke3, and Ryuichiro Adachi4 Akiyama Yuki et al.
  • 1CSIS, The University of Tokyo, Kashiwa Research Complex 4th floor #404, 5-1-5 Kashiwanoha, Kashiwa-shi, Chiba, Japan
  • 2EDITORIA, The University of Tokyo, Institute of Industrial Science Cw-503, 4-6-1 Komaba, Meguro-ku, Tokyo, Japan
  • 3CSIS, The University of Tokyo, Kashiwa Research Complex 4th floor #414, 5-1-5 Kashiwanoha, Kashiwa-shi, Chiba, Japan
  • 4ZENRIN DataCom Co.LTD , Shinagawa Intercity Tower C 6F, 2-15-3 Konan, Minato-ku, Tokyo, Japan

Keywords: GIS, Urban, Analysis, Data Mining, Visualization, Detection, Decision Support, Future

Abstract. In this study, we developed a method to detect sudden population concentration on a certain day and area, that is, an “Event,” all over Japan in 2012 using mass GPS data provided from mobile phone users. First, stay locations of all phone users were detected using existing methods. Second, areas and days where Events occurred were detected by aggregation of mass stay locations into 1-km-square grid polygons. Finally, the proposed method could detect Events with an especially large number of visitors in the year by removing the influences of Events that occurred continuously throughout the year. In addition, we demonstrated reasonable reliability of the proposed Event detection method by comparing the results of Event detection with light intensities obtained from the night light images from the DMSP/OLS night light images. Our method can detect not only positive events such as festivals but also negative events such as natural disasters and road accidents. These results are expected to support policy development of urban planning, disaster prevention, and transportation management.