Evaluating representation and scale error in the maximal covering location problem using GIS and intelligent areal interpolation

A common problem in location-allocation modeling is the error associated with the representation and scale of demand. Numerous researchers have investigated aggregation errors associated with using different scaled data, and more recently, error associated with the geographic representation of model objects has also been studied. For covering problems, the validity of using polygon centroid representations of demand has been questioned by researchers, but the alternative has been to assume that demand is uniformly distributed within areal units. The spatial heterogeneity of demand within areal units thus has been modeled using one of two extremes – demand is completely concentrated at one location or demand is uniformly distributed. This article proposes using intelligent areal interpolation and geographic information systems to model the spatial heterogeneity of demand within spatial units when solving the maximal covering location problem. The results are compared against representations that assume demand is either concentrated at centroids or uniformly distributed. Using measures of scale and representation error, preliminary results from the test study indicate that for smaller scale data, representation has a substantial impact on model error whereas at larger scales, model error is not that different for the alternative representations of the distribution of demand within areal units.     

长沙城市空间破碎化的格局特征及其影响因素

空间破碎化是城市空间结构研究亟待深化的重要课题。从形态、联系以及功能3个维度构建城市空间破碎化测度指标,并运用地理探测器模型,探讨长沙城市空间破碎化的格局特征及其影响因素。主要结论：① 城市空间破碎化可从形态分割、联系阻隔、功能失序三方面进行测度,形态分割可用平均地块面积指数表征,联系阻隔可用平均阻抗指数刻画,功能失序可用功能多样指数、邻接冲突指数来反映;② 长沙城市空间破碎化呈现圈层式分异为主、扇形扩展分异为辅的格局特征,低破碎化区主要分布在城市核心区,高破碎化区沿着特定的扇面向外围扩展,且主要分布在大型封闭社区、工业园区、山地绿地及大型站场等区域;③ 长沙城市空间破碎化的空间分异是多因素综合作用的结果,其中海拔、坡度等自然因素是基础因素,土地价格、人口密度、设施投入是主导因素;④ 长沙城市空间破碎化治理可从突破市场供给约束、市场需求约束和设施丰度约束三方面着手,采取针对性调控对策,以最大程度地消减空间破碎化带来的负面效应。     

基于大数据的城市功能区人口时空聚散模式研究

城市功能区和人口流动是行为地理学和城市规划领域的研究热点,不同城市功能区的人口聚散现象更是其重点议题。该文基于POI和腾讯位置服务(LBS)大数据,以武汉市主城区为研究区,利用功能密度指数、功能优势指数识别城市功能区,并通过空间关联判断城市功能区人口流动变化规律,采用聚类分析方法归纳人口时空聚散模式。研究结果表明:1)中心城区功能混合度较高;2)受人群时空需求影响,不同城市功能区的人口流动规律呈现一定差异性;3)根据城市功能区人口流动聚散趋势并综合其构成特征,可分为公共主导-聚散波动、商务主导-持续集聚、居住主导-持续集聚、绿地主导-聚散交替、商业主导-动态平衡和工业主导-先聚后散共6种模式。研究结果对于优化城市空间布局、合理配置城市资源以及提升城市运行效率具有参考意义。