Tropical Geography ›› 2023, Vol. 43 ›› Issue (6): 1098-1110.doi: 10.13284/j.cnki.rddl.003691

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Mapping of Spatial Distribution of Street Vendors Based on Street-View Images and Deep-Learning Technology

Yuchen Liu1(), Xiaochun Chen1, Yilun Liu1,3(), Xiaofang Wu2, Feixiang Chen2   

  1. 1.The School of Public Administration, College of Natural Resources and Environment, South China Agricultural University, Guangzhou 510642, China
    2.Key Laboratory of Natural Resources Monitoring in Tropical and Subtropical area of South China, Ministry of Natural Resources, Guangzhou 510700, China
  • Received:2022-12-29 Revised:2023-02-20 Online:2023-06-05 Published:2023-07-10
  • Contact: Yilun Liu E-mail:674599716@qq.com;ealenliu@foxmail.com

Abstract:

Street vendors are an indispensable part of the urban social ecosystem, but due to a lack of comprehensive understanding, many cities have adopted simple eviction policies, resulting in the gradual marginalization and stigmatization of the street economy. The efficient governance of street vendors requires the comprehensive investigation of their business scale and spatial distribution information. However, traditional methods have limitations in terms of automatically surveying large-scale street vendor information, particularly spatial distribution. This paper proposes a method for the automatic investigation spatial distribution of street vendors based on street-view images and a deep-learning object recognition model. Street-view images were collected at fixed intervals according to the urban road network, and 1,957 images containing one or more vendors were selected through human-machine interaction to establish street vendor label data. To achieve high recognition model accuracy, the category labels were subdivided into four categories: ground stalls, table stalls, tricycle stalls, and small truck stalls, based on the goods carriers used by street vendors. A deep neural-network-based image object detection model based on YOLO v4 was constructed to identify street vendors in the street-view image library, with an average F1 value of 0.77 and an mAP of 0.67. The accuracy of the model was satisfactory for investigating the number and location of street vendors covering the main roads in the city and then applying a kernel density distribution model to evaluate the spatial distribution pattern of street vendors. Using street vendors in Guangzhou as a case study, the proposed automatic investigation model identified 26,119 street vendors from 3,339,062 street-view images. The results showed that the street vendors were distributed in a multicenter aggregation pattern in the central urban area, mainly concentrated in areas with high pedestrian traffic, such as subway stations and urban villages; their numbers increased as road grades decreases. Street vendors were mainly distributed in areas with medium rents. The proposed method is helpful for performing the efficient, low-cost, and city-scale mapping of street vendors; the results obtained provide suggestions for formulating and implementing spatial governance policies for the informal economy and further provide suggestions for improving and implementing spatial governance policies for open and diverse urban street-view images. The results can be used as a reference for the location preference analysis of practitioners, the exploration of NIMBY syndrome, and the determination of the formalization zone. Although street-view images have an insufficient spatiotemporal coverage, using them to perform street vendor investigations is a low-cost and efficient method compared with the use of traditional investigation methods and data sources. In addition, the method proposed in this article can be coupled with multitask deep learning algorithms to investigate additional dimensions of street vendor information, such as the sex, age, and type of business of street vendors. Relevant research needs to be conducted in the future.

Key words: informal economy, street vendor, street-view image, deep learning, YOLO deep neural network, Guangzhou

CLC Number: 

  • F727