期刊文献+

基于POI数据的城市功能区划分和识别——以泉州市主城区为例 被引量:4

Division and Identification of Urban Functional Areas based on POI-take Main Urban Area of Quanzhou as an Example
下载PDF
导出
摘要 随着社会经济发展,识别城市功能区,理解城市功能区的空间分布特征,对城市的科学规划和政府决策具有非常重要的作用.采用一种新型的城市功能区分区模型,利用城市路网划分出不规则格网作为研究单元,根据POI(point of mterest)的建筑面积、公众认知度对POI数据进行重分类和权重赋值.在此基础上使用核密度估计方法,并利用格网分割核密度,进而定量分析,实现城市功能区的划分和识别.最后将研究结果与泉州市地图进行对比,验证实验的准确率. With the development of social economy,identifying urban functional areas and understanding the spatial distribution characteristics of urban functional areas play a very important role in urban scientific planning and government decision-making.This paper adopts a new type of urban functional area zoning model to construct irregular grid as the research unit based on the road network,and reclassifies the poing of interest(POI)data and assign the weights according to their building area and public awareness of the POI.On the basis of that,kernel density of POI is calculated and then assigned to the irregular grid to make quantitative analysis,based on which,division and identification of urban functional areas is performed.Finally,the research results are compared with the map of Quanzhou to verify the accuracy of this experiment.
作者 毋亭 汤志伟 WU Ting;TANG Zhi-wei(College of Resources and Environmental Science,Fujian Agriculture and Forestry University,Fuzhou 350002,China)
出处 《辽宁大学学报(自然科学版)》 CAS 2021年第1期28-37,共10页 Journal of Liaoning University:Natural Sciences Edition
基金 福建省本科高校教改研究一般项目(111419005)。
关键词 POI 核密度估计 城市功能分区 空间识别 POI kernel density estimation urban functional zoning spatial identification
  • 相关文献

参考文献7

二级参考文献61

  • 1王芳,高晓路,许泽宁.基于街区尺度的城市商业区识别与分类及其空间分布格局——以北京为例[J].地理研究,2015,34(6):1125-1134. 被引量:70
  • 2CHENFei,DUDaosheng.Application of Integration of Spatial Statistical Analysis with GIS to Regional Economic Analysis[J].Geo-Spatial Information Science,2004,7(4):262-267. 被引量:12
  • 3芮建勋.上海市城市绿地景观的信息图谱[J].上海师范大学学报(自然科学版),2007,36(1):95-101. 被引量:4
  • 4Ankerst M, Breunig M M, Kriegel H P and Sander J. 1999. OPTICS: ordering points to identify the clustering structure.ACM SIGMOD Record, 28(2): 49-60 DOI: 10.1145/304181.304187.
  • 5Caduff D and Timpf S. 2008. On the assessment of landmark salience for human navigation. Cognitive Processing, 9(4): 249-267 DOI: 10.1007/s10339-007-0199-2.
  • 6Daniel M P and Denis M. 1998. Spatial descriptions as navigational aids: a cognitive analysis of route directions. Kognitionswissenschaft, 7(1): 45-52 DOI: 10.1007/s001970050050.
  • 7Dong P L. 2008. Generating and updating multiplicatively weighted Voronoi diagrams for point, line and polygon features in GIS. Computers and Geosciences, 34(4): 411-421 DOI: }0.1016/j.cageo.2007.04.005.
  • 8Elias B. 2003. Extracting landmarks with data mining methods. Spatial Information Theory: Cognitive and Computational Foundations of Geographic Information Science. Vol. 2825 of Lecture Notes in Computer Science. Berlin: Springer-Verlag.
  • 9ISO. 2004. Intelligent Transport Systems-Geographic Data Files (GDF)-Overall Data Specifications. ISO 14825.
  • 10Kettani D and Moulin B. 1999. A spatial model based on the notions of spatial conceptual map and of object's influence areas. Spatial Information Theory: Cognitive and Computational Foundations of Geographic Information Science. Vol. 1661 of Lecture Notes in Computer Science. Berlin: Springer-Verlag.

共引文献426

同被引文献52

引证文献4

二级引证文献13

相关作者

内容加载中请稍等...

相关机构

内容加载中请稍等...

相关主题

内容加载中请稍等...

浏览历史

内容加载中请稍等...
;
使用帮助 返回顶部