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

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