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POI数据在中国城市研究中的应用
引用本文:张景奇,史文宝,修春亮. POI数据在中国城市研究中的应用[J]. 地理科学, 2021, 41(1): 140-148. DOI: 10.13249/j.cnki.sgs.2021.01.015
作者姓名:张景奇  史文宝  修春亮
作者单位:1.东北大学文法学院,辽宁 沈阳 110169
2.东北大学江河建筑学院,辽宁 沈阳 110169
基金项目:中央高校基本科研业务专项资金;2021年度辽宁省社科联课题;国家自然科学基金面上项目;2020年度辽宁省社会科学基金重点项目
摘    要:兴趣点(Point of Interest,POI)数据的兴起带动了城市研究的革新.为梳理中国POI数据在城市研究的应用进展,阶段性总结其应用方向、数据分析方法及尚存不足,并为未来POI数据在中国城市发展中的应用提供思路和借鉴.应用CiteSpace工具对中国知网2010-2019年625篇相关文献进行知识图谱分析,结...

关 键 词:POI  地理大数据  城市功能区划分  业态集聚  边界识别
收稿时间:2019-09-19
修稿时间:2019-12-04

Urban Research Using Points of Interest Data in China
Zhang Jingqi,Shi Wenbao,Xiu Chunliang. Urban Research Using Points of Interest Data in China[J]. Scientia Geographica Sinica, 2021, 41(1): 140-148. DOI: 10.13249/j.cnki.sgs.2021.01.015
Authors:Zhang Jingqi  Shi Wenbao  Xiu Chunliang
Affiliation:1. School of Humanities and Law, Northeastern University, Shenyang 110169, Liaoning China
2. School of Jangho Architecture, Northeastern University, Shenyang 110169, Liaoning China
Abstract:The rising of POI (Point of Interest) data drives an innovation of urban research. In order to sort out the progress of urban research using POI data in China, summarize the directions of research, methods of data analysis and shortcomings, and provide references for the future application of POI data in China’s urban development, CiteSpace was used to analyze 625 related literatures in CNKI (China National Knowledge Infrastructure) database from 2010 to 2019. Result shows that a large number of literatures in urban research using POI data emerged since 2013, and boomed in 2017. According to the results of knowledge map analysis, the main applications of POI data in urban research are identification of urban functional areas, division of urban central areas and boundaries, identification of business agglomeration and recommendation of interested points. While the main methods for analyzing POI data are kernel density analysis, spatial correlation analysis and DBSCAN algorithm. Plenty researches show that POI data is a kind of effective data for urban research, and very helpful for researchers to better understand the spatial structures, distribution patterns and development rules of cities. In future, it can be combined with machine learning and the other algorithms to provide a decision-making method for a long-term development of urban expansion and internal functional structure adjustment. However, POI data can not replace the shape data in some scenarios, and the impact of public awareness should be seriously taken into account individually for different researches.
Keywords:POI  geographic big data  division of urban functional areas  business agglomeration  identification of urban boundaries  
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