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Article

Exploring the Attractiveness of Residential Areas for Human Activities Based on Shared E-Bike Trajectory Data

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School of Surveying and Land Information Engineering, Henan Polytechnic University, Jiaozuo 454000, China
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State Key Laboratory of Desert and Oasis Ecology, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi 830011, China
3
Chinese Academy of Surveying and Mapping, Beijing 100830, China
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Key Laboratory of Sediment, Ministry of Water Resources, Yellow River Institute of Hydraulic Research, Yellow River Conservancy Commission, Zhengzhou 450003, China
*
Author to whom correspondence should be addressed.
ISPRS Int. J. Geo-Inf. 2020, 9(12), 742; https://doi.org/10.3390/ijgi9120742
Received: 29 October 2020 / Revised: 9 December 2020 / Accepted: 10 December 2020 / Published: 11 December 2020
Human activities generate diverse and sophisticated functional areas and may impact the existing planning of functional areas. Understanding the relationship between human activities and functional areas is key to identifying the real-time urban functional areas based on trajectories. Few previous studies have analyzed the interactive information on humans and regions for functional area identification. The relationship between human activities and residential areas is the most representative for urban functional areas because residential areas cover a wide range and are closely connected with human life. The aim of this paper is to propose the CARA (Commuting Activity and Residential Area) model to quantify the correlation between human activities and urban residential areas. In this model, human activities are represented by hot spots extracted by the Gaussian Mixture Model algorithm while residential areas are represented by POI (point of interest) data. The model shows that human activities and residential areas present a logarithmic relationship. The CARA model is further assessed by retrieving urban residential areas in Tengzhou City from shared e-bike trajectories. Compared with the actual map, the accuracy reaches 83.3%, thus demonstrating the model’s reliability and feasibility. This study provides a new method for functional areas identification based on trajectory data, which is helpful for formulating the urban people-oriented policies. View Full-Text
Keywords: shared e-bike trajectory; commuting activity; urban residential area; machine learning; urban hot spots shared e-bike trajectory; commuting activity; urban residential area; machine learning; urban hot spots
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MDPI and ACS Style

Cheng, X.; Du, W.; Li, C.; Yang, L.; Xu, L. Exploring the Attractiveness of Residential Areas for Human Activities Based on Shared E-Bike Trajectory Data. ISPRS Int. J. Geo-Inf. 2020, 9, 742. https://doi.org/10.3390/ijgi9120742

AMA Style

Cheng X, Du W, Li C, Yang L, Xu L. Exploring the Attractiveness of Residential Areas for Human Activities Based on Shared E-Bike Trajectory Data. ISPRS International Journal of Geo-Information. 2020; 9(12):742. https://doi.org/10.3390/ijgi9120742

Chicago/Turabian Style

Cheng, Xiaoqian, Weibing Du, Chengming Li, Leiku Yang, and Linjuan Xu. 2020. "Exploring the Attractiveness of Residential Areas for Human Activities Based on Shared E-Bike Trajectory Data" ISPRS International Journal of Geo-Information 9, no. 12: 742. https://doi.org/10.3390/ijgi9120742

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