期刊文献+

基于在线地图数据的城市混合功能二三维划分方法研究 被引量:2

Quantitative Identification of Urban Mixed Functions Based on Multidimensional Data Obtained from Online Map
原文传递
导出
摘要 提出了基于规则的混合功能区二三维划分方法:依据提出的三类判别规则,将POI(points of interest)数据与人造区域、绿地、水体及建筑物体数据结合划分评价单元的二三维评价空间面积,以面积占比确定评价单元混合功能类型,并以混合度与混合强度指标分析混合功能区的差异性。结果显示北京市金融街实验区以商务、金融、公管为主,混合度与混合强度识别结果具有一定正相关关系,居住、公管主导的评价单元混合强度较高。与POI数量占比识别法对比,本方法能够合理提升金融、商务、居住等功能类型的面积占比,降低POI数量较多的商服功能的面积占比,能够更为合理精细地揭示城市混合功能的实际混合情况。 In this paper,We set up a rule-based method coupled with POI data,water,green land,3D buildings and artificial areas to divide judgment space area within evaluation unit.Then we identify mixed functional type of evaluation units and define index of mixing degree and mixing intensity based on divided area within evaluation units.According to the results,business,finance and public management are three main mixed functions of Jingrongjie.Mixing degree is correlated with mixing intensity to some extent and average mixing intensity of study area’s units with living and public management as their major function is higher than units with other functions as their major function.Compared with previous study,our rulebased method can reasonably improve the ratio of finance,business and living function which is represented by minor POI data and reduce the ratio of commerce function which is represented by major POI data.This improvement discloses more real situation of urban mixed functions.
作者 郑力夫 沈凌云 刘慧平 刘若琳 陈曦 ZHENG Lifu;SHEN Lingyun;LIU Huiping;LIU Ruoling;CHEN Xi(Faculty of Geographical Science,Beijing Normal University,Beijing 100875,China)
出处 《测绘地理信息》 CSCD 2022年第S01期188-193,共6页 Journal of Geomatics
基金 国家自然科学基金(40671127)
关键词 兴趣点 在线地图多维数据 城市功能区 混合功能区 单一功能区 points of interest(POI) multidimensional online map data urban functional area urban mixed functional area urban single functional area
  • 相关文献

参考文献5

二级参考文献38

  • 1黄继风.基于Delaunay三角网的城市多边形合并算法[J].计算机工程与设计,2004,25(7):1220-1222. 被引量:16
  • 2Ankerst 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.
  • 3Caduff 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.
  • 4Daniel 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.
  • 5Dong 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.
  • 6Elias 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.
  • 7ISO. 2004. Intelligent Transport Systems-Geographic Data Files (GDF)-Overall Data Specifications. ISO 14825.
  • 8Kettani 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.
  • 9Klippel A, Richter K F and Hansen S. 2009. Cognitively ergonomic route directions. Handbook of Research on Geoinforrnatics. IGI: Information Science Reference.
  • 10Klippel A and Winter S. 2005. Structural salience of landmarks for route directions. Spatial Information Theory: Cognitive and Computational Foundations of Geographic Information Science. Vol. 3693 of Lecture Notes in Computer Science. Berlin: Springer-Verlag.

共引文献317

同被引文献27

引证文献2

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