Abstract
It is an important content of smart city research to study the activity track of urban residents, dig out the hot spot areas and spatial interaction patterns of different residents’ activities, and clearly understand the travel rules of urban residents' activities. This study used community detection to analyze taxi passengers’ travel hot spots based on taxi pick-up and drop-off data, combined with multisource information such as land use, in the main urban area of Nanjing. The study revealed that, for the purpose of travel, the modularity and anisotropy rate of the community where the passengers were picked up and dropped off were positively correlated during the morning and evening peak hours and negatively correlated at other times. Depending on the community structure, pick-up and drop-off points reached significant aggregation within the community, and interactions among the communities were also revealed. Based on the type of land use, as passengers' travel activity increased, travel hot spots formed clusters in urban spaces. After comparative verification, the results of this study were found to be accurate and reliable and can provide a reference for urban planning and traffic management.
Highlights
With the rapid development of information technology, spatial analysis driven by data forces geographic information science to face new challenges
Time-space analysis based on residents’ activities can explain the homogeneity of the influence of individual residents’ behaviors on urban space, and the behaviors between different individuals can reflect that they are restricted by urban space and show their differences [3]. erefore, as Harvey and Han [4] proposed the concept of geographic data mining and knowledge discovery, scholars have continued to explore knowledge in recent decades, and geography has experienced transition from an empirical paradigm to a system simulation paradigm and to a data-driven paradigm [5]
Scholz et al [8] studied urban residents’ behavioral patterns and the temporal and spatial development of urban hot spots, and Cui et al [9] studied the accessibility of urban residential areas and the distribution of low-access residential areas
Summary
With the rapid development of information technology, spatial analysis driven by data forces geographic information science to face new challenges. Research on the behavioral patterns of urban residents’ activities mainly focused on extracting residents’ activity points and on the correlation analysis of those points. Scholz et al [8] studied urban residents’ behavioral patterns and the temporal and spatial development of urban hot spots, and Cui et al [9] studied the accessibility of urban residential areas and the distribution of low-access residential areas. Zheng et al [14] studied the characteristics of cross connectivity between urban planning and taxi driving, and Wu et al [15] studied the temporal and spatial patterns of urban road traffic accidents
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