核密度估计法支持下的网络空间POI点可视化与分析

禹文豪; 艾廷华

doi:10.11947/j.AGCS.2015.20130538

测绘学报 >

2015 , Vol. 44 >Issue 1: 82 - 90

DOI: https://doi.org/10.11947/j.AGCS.2015.20130538

地图学与地理信息

核密度估计法支持下的网络空间POI点可视化与分析

禹文豪 ,
艾廷华

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武汉大学资源与环境科学学院, 湖北武汉 430079

禹文豪(1987-), 男, 博士, 研究方向为空间分析和空间数据挖掘. E-mail: ywh_whu@126.com

收稿日期: 2013-12-03

修回日期: 2014-05-25

网络出版日期: 2015-01-22

基金资助

国家863计划(2012AA12A404);国家科技支撑计划(2012BAJ22B02-01)

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The Visualization and Analysis of POI Features under Network Space Supported by Kernel Density Estimation

YU Wenhao ,
AI Tinghua

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School of Resources and Environmental Science, Wuhan University, Wuhan 430079, China

Received date: 2013-12-03

Revised date: 2014-05-25

Online published: 2015-01-22

Supported by

The National High-tech Research and Development Program of China (863 Program) (No. 2012AA12A404) The National Key Technology Research and Development Program of the Ministry of Science and Technology of China (No. 2012BAJ22B02-01)

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摘要

城市空间POI点的分布模式、分布密度在基础设施规划、城市空间分析中具有重要意义, 表达该特征的核密度法(kernel density estimation)由于顾及了地理学第一定律的区位影响,比其他密度表达方法(如样方密度、基于Voronoi图密度)占优.然而,传统的核密度计算方法往往基于二维延展的欧氏空间,忽略了城市网络空间中设施点的服务功能及相互联系发生于网络路径距离而非欧氏距离的事实.本研究针对该缺陷,给出了网络空间核密度计算模型,分析了核密度方法在置入网络结构中受多种约束条件的扩展模式,讨论了衰减阈值及高度极值对核密度特征表达的影响.通过实际多种POI点分布模式(随机型、稀疏型、区域密集型、线状密集型)下的核密度分析试验,讨论了POI基础设施在城市区域中的分布特征、影响因素、服务功能.

关键词： 网络核密度; POI点分析; 网络分析; 空间统计

本文引用格式

禹文豪 , 艾廷华 . 核密度估计法支持下的网络空间POI点可视化与分析[J]. 测绘学报, 2015 , 44(1) : 82 -90 . DOI: 10.11947/j.AGCS.2015.20130538

Abstract

The distribution pattern and the distribution density of urban facility POIs are of great significance in the fields of infrastructure planning and urban spatial analysis. The kernel density estimation, which has been usually utilized for expressing these spatial characteristics, is superior to other density estimation methods (such as Quadrat analysis, Voronoi-based method), for that the Kernel density estimation considers the regional impact based on the first law of geography. However, the traditional kernel density estimation is mainly based on the Euclidean space, ignoring the fact that the service function and interrelation of urban feasibilities is carried out on the network path distance, neither than conventional Euclidean distance. Hence, this research proposed a computational model of network kernel density estimation, and the extension type of model in the case of adding constraints. This work also discussed the impacts of distance attenuation threshold and height extreme to the representation of kernel density. The large-scale actual data experiment for analyzing the different POIs' distribution patterns (random type, sparse type, regional-intensive type, linear-intensive type) discusses the POI infrastructure in the city on the spatial distribution of characteristics, influence factors, and service functions.

Key words： network kernel density; POI analysis; network analysis; spatial statistics

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