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作者

Sheng Hu;Yongyang Xu;Liang Wu;Xincai Wu;Run Wang;Ziwei Zhang;Rujuan Lu;Wei Mao

来源

CITIES,Vol.109,Issue1,Article 103012

语言

英文

关键字

Urban perception;Thematic place;Geospatial data;Community detection

作者单位

School of Geography and Information Engineering, China University of Geosciences, Wuhan 430074, China;National Engineering Research Center of Geographic Information System, Wuhan 430074, China;School of Geography and Information Engineering, China University of Geosciences, Wuhan 430074, China;National Engineering Research Center of Geographic Information System, Wuhan 430074, China

摘要

The best way of sensing places has long been an interest in GIScience and urban studies. Previous studies detect and understand places via traditional human participant-oriented studies, which are laborious, time-consuming, and sometimes subjective. The proliferation of multi-sourced geospatial data brings unprecedented opportunities for researchers to improve assessment and understanding of places in cities. However, it remains insufficient to explore place based on a single or naïve set of geospatial data owing to issues of completeness and bias. Of particular interest is extracting and understanding thematic place using geospatial data. In this paper, we attempt to extract thematic place at a community scale and investigate the potential capability of multi-sourced geospatial data to understand extracted place. First, thematic places were constructed by integrating urban road networks and community detection methods in a complete network field. Then, we implemented quantitative geographical semantic analyses using geo-tagged street view images and point-of-interest data to investigate thematic places at a community scale for urban perception. A case study in a high-density urban environment, Wuhan City, China, was employed to illustrate its application to communities. The approach and the results of our study demonstrate the ability to extract and understand thematic places from a community perspective. The results of our framework can aid urban planners in designing better urbanization strategies.