Volume II-4/W2
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., II-4/W2, 131-135, 2015
https://doi.org/10.5194/isprsannals-II-4-W2-131-2015
© Author(s) 2015. This work is distributed under
the Creative Commons Attribution 3.0 License.
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., II-4/W2, 131-135, 2015
https://doi.org/10.5194/isprsannals-II-4-W2-131-2015
© Author(s) 2015. This work is distributed under
the Creative Commons Attribution 3.0 License.

  10 Jul 2015

10 Jul 2015

DETECTING HOTSPOTS FROM TAXI TRAJECTORY DATA USING SPATIAL CLUSTER ANALYSIS

P. X. Zhao1, K. Qin1, Q. Zhou1, C. K. Liu1, and Y. X. Chen2 P. X. Zhao et al.
  • 1School of Remote Sensing and Information Engineering, Wuhan University, Wuhan, China
  • 2College of Geographic and Biologic Information, Nanjing University of Posts and Telecommunications, Nanjing, China

Keywords: Taxi Trajectory, Decision Graph, Data Field, Trajectory Clustering, Urban Hotspots

Abstract. A method of trajectory clustering based on decision graph and data field is proposed in this paper. The method utilizes data field to describe spatial distribution of trajectory points, and uses decision graph to discover cluster centres. It can automatically determine cluster parameters and is suitable to trajectory clustering. The method is applied to trajectory clustering on taxi trajectory data, which are on the holiday (May 1st, 2014), weekday (Wednesday, May 7th, 2014) and weekend (Saturday, May 10th, 2014) respectively, in Wuhan City, China. The hotspots in four hours (8:00-9:00, 12:00-13:00, 18:00-19:00 and 23:00-24:00) for three days are discovered and visualized in heat maps. In the future, we will further research the spatiotemporal distribution and laws of these hotspots, and use more data to carry out the experiments.