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作者

Kang Liu;Peiyuan Qiu;Song Gao;Feng Lu;Jincheng Jiang;Ling Yin

来源

CITIES,Vol.97,Issue1,Article 102561

语言

英文

关键字

Metro station;Cognitive region;Place;Point of interest;Web page;TF-IDF

作者单位

Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China;State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, China;Department of Geography, University of Wisconsin-Madison, Madison, USA;University of Chinese Academy of Sciences, Beijing, China;Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China;State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, China;Department of Geography, University of Wisconsin-Madison, Madison, USA;University of Chinese Academy of Sciences, Beijing, China

摘要

The significance of urban metro stations extends beyond their roles as transport nodes in a city. Their surroundings are usually well-developed and attract a lot of human activities, which make the metro station areas important cognitive places characterized by vague boundaries and rich semantics. Current studies mainly define metro station areas based on an estimation of walking distance to the stations (e.g., 700 m) and investigate these areas from the perspectives of transportation and land use instead of as cognitive places perceived by the crowd. To fill this gap, this study proposes a novel framework for extracting and understanding the cognitive regions of urban metro stations based on points of interest (POIs). First, we extract the cognitive regions of metro stations based on co-occurrence patterns of the stations and their surrounding POIs on web pages by proposing a cohesive approach combined of spatial clustering, web page extraction, knee-point detection, and polygon generation techniques. Second, we identify the semantics of metro stations based on POI types inside the regions using the term frequency-inverse document frequency (TF-IDF) method. In total 166 metro stations along with more than one million POIs in Shenzhen, China are utilized as data sources of the case study. The results indicate that our proposed framework can well detect the place characteristics of urban metro stations, which enriches the place-based GIS research and provides a human-centric perspective for urban planning and location-based-service (LBS) applications.