TROPICAL GEOGRAPHY ›› 2019, Vol. 39 ›› Issue (1): 117-124.doi: 10.13284/j.cnki.rddl.003097

Previous Articles     Next Articles

Extraction of Urban Hotspots and Analysis of Spatial interaction Based on Trajectory Data Field: A Case Study of Shenzhen City

Zhou Bo1,2, Ma Linbing1,2, Hu Jihua3, Wu Sujie1 and He Guilin1   

  1. (1. School of Geography and Planning, Sun Yat-Sen University, Guangzhou 510275, China; 2. Guangdong Key Laboratory for Urbanization and Geo-Simulation, Guangzhou 510275, China; 3.Public Experiment Teaching Center, Sun Yat-Sen University, Guangzhou 510006, China)
  • Received:2018-07-20 Revised:2018-09-27 Online:2019-01-05 Published:2019-01-05

Abstract: As a kind of important public transportation, taxi trajectory data contain abundant information about urban functions and citizen activities owing to its long service time, wide coverage of city area and freedom of its motion. Based on taxi GPS data of Shenzhen city, in this study, we extract urban traffic hotspots using the clustering method of trajectory data field based on space-time development. The hotspot area spatio-temporal distribution of typical time periods of the two time types of holidays and workdays are selected for visual graphic representation, and analyze hot spots combining POI data and the present situation of urban development. Based on the view of complex network, the interaction analysis index is calculated and the spatial interaction network of hot spots is visualized, a qualitative and quantitative analysis method is used to explore the spatial and temporal rules of urban traffic and residents’ travel. The results are showed as follows. 1) The hotspot area spatio-temporal distribution of urban traffic hotspots in holidays and workdays is significantly different. Transportation hub (like airports, railway stations and ports), a comprehensive business circle, area around urban major road and central business district are continuous hot spots during holidays and workdays, and the locations of other hotspots change over time. 2) The distribution of hot spots in holidays is disperse, mainly reflects the personalized travel demands of residents. Urban tourist attractions and other leisure areas have become hot spots, and it is clear that that outflow of Baoan airport and Shenzhen north station are obviously great than the inflow in the holiday season, which means that they are relatively strong. 3) The distribution of hot spots in workdays is convergent, which reflects the commuting mode of separation of work and residence. Residents' travel shows a tidal movement between the work place and the residence, the synchronism of inflow and outflow in hot spots is stronger than that in holidays, and residents travel more regularly. 4) Different hotspots in space interaction network have obviously difference in their significance, which spatial interaction reflects the distance-decay effect and partial aggregation phenomenon. Shennan avenue has become the dividing line between hot spots with close connections. Moreover, it was found that that space interaction network between Shenzhen north station, Futian district and Luohu district is of high importance in the space interaction network, and the links between the three are frequent. The hotspots network of residents travel have the characteristics of small-world effect and scale-free.

Key words: taxi trajectory data, urban hotspots, data field, spatiotemporal clustering, spatial interaction, Shenzhen City