Detecting the city-scale spatial pattern of the urban informal sector by using the street view images: A street vendor massive investigation case

《Detecting the city-scale spatial pattern of the urban informal sector by using the street view images: A street vendor massive investigation case》

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作者: Yilun Liu;Yuchen Liu

来源: CITIES,Vol.131,Issue1,Article 103959

语言: 英文

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作者单位: School of Public Administration, South China Agricultural University, Guangzhou 510642, PR China;Key Laboratory of the Ministry of Natural Resources for Natural Resources Monitoring in Tropical Subtropics of South China, Ministry of Natural Resources, Guangzhou 510700, PR China;College of Natural Resources and Environment, South China Agricultural University, Guangzhou 510642, PR China;School of Public Administration, South China Agricultural University, Guangzhou 510642, PR China;Key Laboratory of the Ministry of Natural Resources for Natural Resources Monitoring in Tropical Subtropics of South China, Ministry of Natural Resources, Guangzhou 510700, PR China;College of Natural Resources and Environment, South China Agricultural University, Guangzhou 510642, PR China;Department of Public Finance and Infrastructure Policy, Vienna University of Technology, Austria;Research Unit of Urban and Regional Studies, Vienna University of Technology, Austria;College of Forestry and Landscape Architecture, South China Agricultural University, Guangzhou 510642, China;School of Geography and Planning, Sun Yat-sen University, Guangzhou 510275, China;Guangdong Provincial Engineering Research Center for Public Security and Disaster, Guangzhou 510275, China;Guangdong Key Laboratory for Urbanization and Geo-simulation, Guangzhou 510275, China;School of Energy, Construction and Environment, Sir John Laing Building, Room No. JL141, Coventry University, Priory St, Coventry, West Midlands CV1 5FB, United Kingdom;School of Energy, Construction and Environment, Sir John Laing Building, Coventry University, Priory St, Coventry, West Midlands CV1 5FB, United Kingdom;National Tsing Hua University, Institute of Technology Management, No 101, Sec. 2, Kuang- Fu Rd, 30013 Hsinchu, Taiwan;Suffolk University, Sawyer Business School, 73 Tremont Street, Boston, MA 02108, United States of America;Department of Geography, University of Utah, 260 S Central Campus Dr, Rm 4625, Salt Lake City, UT 84112, USA;Rocky Mountain Research Station, USDA Forest Service, 240 W Prospect, Fort Collins, CO 80526, USA;Department of Architecture, National University of Singapore, Singapore;Department of Real Estate, National University of Singapore, Singapore

摘要: Automatically obtaining information on informal practitioners, especially their spatial distribution, has proven challenging when using traditional methods. This study addresses this issue by presenting a street view deep learning method, called the Street Informal Practitioners Spatial Investigation (SIPSI) methodology. This paper's application of this technology focuses on the study case of the street vendor, which is one of the most visible occupations in the informal economy. There were 3907 street vendors that were detected using this method; as well, the kernel density estimation indicated that they agglomerated in a multi-core cluster pattern in the city. Further analysis of the factors that influence agglomeration shows that the street vendors prefer premises that are near the lower level of the road and the higher density population sites, whereas the NIMBY (Not In My Back Yard) syndrome keeps these vendors away from the central City Business Districts and high-rent regions. The presented methodology and the study results contribute to high-efficiency investigations of informal economy employment, and it further assists in advising for the spatial governance policies improvement and implementation in any cities whose street view images are abundant and open-access.