论文推送 | 基于非参数方法和兴趣点数据识别城市主次中心：以284个中国城市为例
本期为大家推介的内容为论文《基于非参数方法和兴趣点数据识别城市次中心：以284个中国城市为例》(Identifying subcenters with a nonparametric method and ubiquitous point-of-interest data: A case study of 284 Chinese cities)，发表在《Environment and Planning B: Urban Analytics and City Science》，欢迎大家学习与交流。
城市空间结构主要被定义为就业和居住的空间分布，这是有充分理由引起城市经济学家，地理学家和规划师的长期关注的。本文提出了一种非参数方法，该方法结合了Jenks自然断点法和Moran's I来使用兴趣点密度来识别城市的多中心空间结构（主中心和次中心）。具体来说，一个多中心城市由一个主中心和至少一个子中心组成。一个合格的（子）中心应具有比其周围环境（局部较高）明显更高的人类活动密度，并且比该城市中所有其他子区域（全部较高）具有更高的密度。以中国城市为主题，我们最终从中国284个地级市中确定了70个具有多中心空间结构的城市。此外，进行回归分析以揭示受试者之间多中心性的预测因子。回归结果表明，一个城市的总人口，GDP，平均工资和城市土地面积均显著预测了多中心性。
总体而言，本文提供了一种可替代的可转移方法，用于识别整个城市的主要中心和子中心，并揭示多中心性的常见预测因素。所提出的方法避免了常规方法中的一些潜在问题，例如阈值的任意性。设置和对空间尺度的敏感性。 这也可以相当方便地复制，因为它的输入数据（例如兴趣点数据）已广泛为公众使用，并且可以通过实地考察或其他传统数据源（例如土地利用地图、人口普查数据）有效地检查数据的有效性。
题目：《基于非参数方法和兴趣点数据识别城市次中心：以284个中国城市为例》
（Identifying subcenters with a nonparametric method and ubiquitous point-of-interest data: A case study of 284 Chinese cities）
作者：Ying Long，Yimeng Song，Long Chen
发表刊物：《》
DOI：10.1177/2399808321996705
摘要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.
更多内容，请点击微信下方菜单即可查询。
请搜索微信号“Beijingcitylab”关注。
Email：BeijingCityLab@gmail.com
Emaillist: BCL@freelist.org
新浪微博：北京城市实验室BCL
微信号：beijingcitylab
网址: http://www.beijingcitylab.com
责任编辑：孟庆祥