ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Articles | Volume V-4-2022
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., V-4-2022, 311–318, 2022
https://doi.org/10.5194/isprs-annals-V-4-2022-311-2022
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., V-4-2022, 311–318, 2022
https://doi.org/10.5194/isprs-annals-V-4-2022-311-2022
 
18 May 2022
18 May 2022

URBAN FUNCTIONAL DISTRICT IDENTIFICATION AND ANALYSIS FROM MULTI-SOURCE DATA

Y. Zhang1,2, K. Qin3, W. Liu1,2, X. Zhu1,2, Y. Peng1,2, X. Wang1,2, X. Zhai1,2, T. Zhao1,2, and R. Li1,2 Y. Zhang et al.
  • 1National Geomatics Center of China, 28 Lianhuachi West Road, Haidian District, Beijing 100830, China
  • 2Key Laboratory of Spatio-temporal Information and Intelligent Services (LSIIS), MNR, 28 Lianhuachi West Road, Haidian District, Beijing 100830, China
  • 3School of Remote Sensing and Information Engineering, Wuhan University, 129 Luoyu Road, Hongshan District, Wuhan 430079, China

Keywords: Urban Functional District, Social Sensing Data, Taxi Trajectory, OpenStreetMap, Head/tail Breaks Method, Data Mining

Abstract. Residents’ activities have a significant interaction with urban socioeconomic environment. Taxi trajectory data has been widely used to mine human activity patterns to identify urban functional districts. However, previous studies merely chose several spatiotemporal statistics of taxi pick-up and drop-off points. This paper compares seven time series statistics of taxi pick-up and drop-off points, and selects the best combination to identify urban functional districts. The basic analysis units are not only constructed based on the OpenStreetMap data, but also optimized with the fine-grained clean rasterized pixels, generated from preprocessed taxi trajectory data through the improved head/tail breaks method. The experiment conducted in Wuchang District, Wuhan, shows that the combination of the average statistics of pick-up points, the average statistics of drop-off points, and the ratio statistics of pick-up and drop-off difference achieves the best identification precision of 83.65%, the F1-score of 82.2%, and the recall score of 81.48%. The proposed approach has good scalability and can be transplant to other identification applications.