基于电子地图兴趣点数据的城市可持续发展水平分析——以绍兴市为例

Abstract

PDF HTML阅读 XML下载 导出引用 引用提醒 基于电子地图兴趣点数据的城市可持续发展水平分析——以绍兴市为例 DOI: 10.5846/stxb201706021015 作者: 作者单位: 中国科学院大学 中国科学院生态环境研究中心,中国科学院大学 中国科学院生态环境研究中心,中国科学院大学 中国科学院生态环境研究中心,中国科学院大学 中国科学院生态环境研究中心,中国科学院大学 中国科学院生态环境研究中心,中国科学院生态环境研究中心 作者简介: 通讯作者: 中图分类号: 基金项目: 国家重点研发计划（2016YFC0503605） Analysis of urban sustainable development level based on POI: a case study in Shaoxing Author: Affiliation: University of Chinese Academy of Sciences. Research Center for Eco-Environmental science, Chinese Academy of Sciences,University of Chinese Academy of Sciences. Research Center for Eco-Environmental science, Chinese Academy of Sciences,University of Chinese Academy of Sciences. Research Center for Eco-Environmental science, Chinese Academy of Sciences,University of Chinese Academy of Sciences. Research Center for Eco-Environmental science, Chinese Academy of Sciences,University of Chinese Academy of Sciences. Research Center for Eco-Environmental science, Chinese Academy of Sciences,Research Center for Eco-Environmental science, Chinese Academy of Sciences Fund Project: Supported by the National Key Research and Development Program of China (No. 2016YFC0503605) 摘要 | 图/表 | 访问统计 | 参考文献 | 相似文献 | 引证文献 | 资源附件 | 文章评论 摘要:在当前大数据时代背景下，电子地图兴趣点（POI，Point of Interest）作为地理空间大数据的重要类型，能直接反映城市人口密度、发展程度与强度等各类型要素的聚集状况。基于当前普遍使用的兴趣点数据源，运用ArcGIS软件中的空间分析工具，提取了绍兴市各区（县、市）的POI数据分类信息；结合绍兴市各区（县、市）的面积及人口数据，评估各区（县、市）的单位面积POI和单位人口POI指标，以分析绍兴市各区（县、市）POI的均衡分布水平。进一步利用核密度分析、空间回归分析和近邻分析的手段，从不同角度研究了各类POI在地理空间分布中的特征规律。研究结果表明：从不同区县来看，城市化发展水平较高的地域POI总数明显较高，其核函数密度值也明显较大；从单位面积POI数量及单位人口POI数量来看，各区县发展水平较不均衡，表现为越城区最高，上虞区、柯桥区和诸暨市次之，新昌县和嵊州市较低，其表现在不同区县在提供生活、生产服务的基础设施具有区域间差异性，不符合可持续发展的公平性原则。最后，针对解决这一问题提出一些促进绍兴各区县协调公平可持续发展的城市建设与规划建议。 Abstract:With the arrival of the era of big data, Point of Interest (POI), as an important type of geospatial big data, can reflect aggregation conditions of various elements such as urban population density, urban development level and urban development strength directly. Using the geospatial analysis tool in ArcGIS, the POI data classification information of each district (county, city) in Shaoxing was extracted from one popular POI data source. In order to evaluate the balanced distribution level among districts in Shaoxing, POI data combined with population and area information of each district was analyzed. Furthermore, several analysis methods including kernel density analysis, spatial regression analysis and nearest neighbor analysis were used to dig characteristics of POI's geospatial distribution from different perspectives. Our results indicated that among several districts in Shaoxing, the quantity and the kernel density of POI were larger where there was a higher urbanization level. From the perspective of the number of POI per unit area and per unit population, there appeared an unbalanced development level among several districts. Specifically, the Yue District was most developed, followed by Shangyu District, Keqiao District and Zhuji City, while Xinchang County and Shengzhou City were least developed. The regional discrepancy of infrastructure that provides life and production services didn't accord with the fairness principle of sustainable development. Finally, some suggestions about urban construction and planning in Shaoxing were presented for creating a coordinated, fair and sustainable development among different districts. 参考文献 相似文献 引证文献