Spatiotemporal patterns and mechanisms of street vending from the social sensing perspective: A comparison between law-enforcement reported and residents complain events

Abstract

Street vending is an important part of the urban informal economy, especially in developing countries. How to control its negative externalities while augmenting its positive roles in the urban socio-economic system poses challenges to urban planning and governance. Existing studies have insufficiently addressed the issue largely due to the lack of high-frequent observation data for street vending events. By introducing socially sensed big data from the smart urban governance platform, as well as records from the “12345” urban problem complaint hotline of Jiangbei District, Ningbo, China, this paper examines and compares the spatiotemporal patterns and occurrence mechanisms of street vending events from both the urban managers' “top-down” and the urban residents' “bottom-up” points of view. Statistical and machine learning models show that the distribution of street vending activities as sensed by the two subjects does not overlap. The former concentrates in central urban areas and work times, while the latter is scattered distributed in everyday life-related places and times. The findings reaffirm the existence of perception bias in social sensing data and show the potential for utilizing such bias to nudge better urban governance practices. Theoretically and empirically, this research has contributed to promoting the people-oriented transformation of urban governance.