Journal cover Journal topic
AGILE: GIScience Series Open-access proceedings of the Association of Geographic Information Laboratories in Europe
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Volume 1
AGILE GIScience Ser., 1, 9, 2020
https://doi.org/10.5194/agile-giss-1-9-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
AGILE GIScience Ser., 1, 9, 2020
https://doi.org/10.5194/agile-giss-1-9-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

  15 Jul 2020

15 Jul 2020

Extraction of The Spatio-temporal Activity Patterns Using Laser-scanner Trajectory Data

Hiroyuki Kaneko1 and Toshihiro Osaragi2 Hiroyuki Kaneko and Toshihiro Osaragi
  • 1Kajima Technical Research Institute, 19-1, Tobitakyu 2-chome, Chofu-shi, Tokyo, Japan
  • 2School of Environment and Society, Tokyo Institue of Technology, 2-12-1-M1 Ookaya-ma, Meguroku, Tokyo, Japan

Keywords: Pedestrian trajectory data, Laser-Scanner, Spatio-temporal activity, Information-loss, PLSI (Probabilistic latent semantic indexing),Big data

Abstract. A pedestrian tracking system on highly accurate laser scanners is an effective method to understand the usage of the facility space. While this system is capable of gathering an enormous volume of tracking data, specialized skills and significant amounts of labor are needed to get a reliable bird’s-eye view of the spatio-temporal characteristics of the observed data. In this paper, two methods to extract patterns of spatio-temporal activity are described. These can provide a broad overview of the office-worker’s activities in the office throughout a workday and an easily under-stood visualization that indicates what time segment, what location and what activities are taking place. One is a time segment extraction model that identifies characteristic time intervals in the time series data of office-worker’s activities using a classification model based on information loss minimization model. The other is a day scene extraction model that identifies daily scenes from simultaneous behavior patterns in spatio-temporal distributions using a latent class model with PLSI (Probabilistic latent semantic indexing). These methods provide viewpoints for separating their activities of a workday into time segments of appropriate size in order to obtain a grasp of how the activities vary with the time of day. Simultaneous behavior patterns in time, space, and activity are extracted, thereby allowing representation of typical scenes such as morning meetings and extended conversations between co-workers.

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