Identifying subcenters with a nonparametric method and ubiquitous point-of-interest data: A case study of 284 Chinese cities

Abstract

Urban spatial structure, which is primarily defined as the spatial distribution of employment and residences, has been of lasting interest to urban economists, geographers, and planners for good reason. This paper proposes a nonparametric method that combines the Jenks natural break method and the Moran’s I to identify a city’s polycentric structure using point-of-interest density. Specifically, a polycentric city consists of one main center and at least one subcenter. A qualified (sub)center should have a significantly higher density of human activity than its immediate surroundings (locally high) and a relatively higher density than all the other subareas in the city (globally high). Treating Chinese cities as the subject, we ultimately identified 70 cities with polycentric structures from 284 prefecture-level cities in China. In addition, regression analyses were conducted to reveal the predictors of polycentricity among the subjects. The regression results indicate that the total population, GDP, average wage, and urban land area of a city all significantly predict polycentricity. As a whole, this paper provides an alternative and transferrable method for identifying main centers and subcenters across cities and to reveal common predictors of polycentricity. The proposed method avoids some of the potential problems in the conventional approach, such as the arbitrariness of thres hold. setting and sensitivity to spatial scales. It can also be replicated rather conveniently, as its input data, such as point-of-interest data, are widely available to the public and the data’s validity can be efficiently checked by field trips or other traditional data sources, such as land-use maps or censuses.