POI数据在中国城市研究中的应用

doi:10.13249/j.cnki.sgs.2021.01.015

地理科学 ›› 2021, Vol. 41 ›› Issue (1): 140-148.doi: 10.13249/j.cnki.sgs.2021.01.015

POI数据在中国城市研究中的应用

张景奇¹(), 史文宝¹, 修春亮²^,^*()

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

收稿日期:2019-09-19 修回日期:2019-12-04 出版日期:2021-01-25 发布日期:2021-03-04
通讯作者: 修春亮 E-mail:15_zhangjingqi@mail.neu.edu.cn;xiuchunliang@mail.neu.edu.cn
作者简介:张景奇（1982‒），男，辽宁鞍山人，教授，博导，主要从事城市治理研究。E-mail: 15_zhangjingqi@mail.neu.edu.cn
基金资助:
国家自然科学基金面上项目(41871162);中央高校基本科研业务专项资金(N2014008);2020年度辽宁省社会科学基金重点项目(L20AGL005);2021年度辽宁省社科联课题(2021lslwzzkt-038)

Urban Research Using Points of Interest Data in China

Zhang Jingqi¹(), Shi Wenbao¹, Xiu Chunliang²^,^*()

1. School of Humanities and Law, Northeastern University, Shenyang 110169, Liaoning China
2. School of Jangho Architecture, Northeastern University, Shenyang 110169, Liaoning China

Received:2019-09-19 Revised:2019-12-04 Online:2021-01-25 Published:2021-03-04
Contact: Xiu Chunliang E-mail:15_zhangjingqi@mail.neu.edu.cn;xiuchunliang@mail.neu.edu.cn
Supported by:
National Natural Science Foundation of China(41871162);Fundamental Research Funds for the Central Universities(N2014008);2020 Liaoning Social Science Foundation(L20AGL005);2021 Liaoning Provincial Fedration Social Science Circle Foundation(2021lslwzzkt-038)

摘要/Abstract

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，结合分析结果对POI数据应用方向和数据分析方法进行梳理总结。结果表明:时间上，国内应用POI数据进行城市研究的文献在2013年后大量涌现，2017年呈现爆发式增长；应用上，主要用于城市功能区划分、城市中心区和边界识别、查明业态集聚分布以及兴趣点推荐4个方面；方法上，常用的有核密度分析、DBSCAN聚类分析和空间自相关分析3类。研究表明，POI地理大数据是一种研究城市发展的有效数据，有助于研究者深入了解城市的空间结构、分布格局和发展规律，未来可进一步与机器学习等算法结合，为城市外部扩张和内部功能结构调整在更长期的发展上提供一个决策分析手段，但POI数据尚无法代替面数据，研究时也要充分考虑到公众认知度高低对研究的影响。

关键词: POI, 地理大数据, 城市功能区划分, 业态集聚, 边界识别

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.

Key words: POI, geographic big data, division of urban functional areas, business agglomeration, identification of urban boundaries

中图分类号:

F293.2

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.

Zhang Jingqi, Shi Wenbao, Xiu Chunliang. Urban Research Using Points of Interest Data in China[J]. SCIENTIA GEOGRAPHICA SINICA, 2021, 41(1): 140-148.

图/表 2

参考文献 47

[1]	程昌秀, 史培军, 宋长青, 等. 地理大数据为地理复杂性研究提供新机遇[J]. 地理学报, 2018, 73 (8): 5-14
	Cheng Changxiu, Shi Peijun, Song Changqing et al. Geographic big-data: A new opportunity for geography complexity study. Acta Geographica Sinica, 2018, 73 (8): 5-14
[2]	裴韬, 刘亚溪, 郭思慧, 等. 地理大数据挖掘的本质[J]. 地理学报, 2019, 74 (3): 586-598 doi: 10.11821/dlxb201903014
	Pei Tao, Liu Yaxi, Guo Sihui et al. Principle of big geodata mining. Acta Geographica Sinica, 2019, 74 (3): 586-598 doi: 10.11821/dlxb201903014
[3]	浩飞龙, 王士君, 冯章献, 等. 基于POI数据的长春市商业空间格局及行业分布[J]. 地理研究, 2018, 37 (2): 366-378
	Hao Feilong, Wang Shijun, Feng Zhangxian et al. Spatial pattern and its industrial distribution of commercial space in Changchun based on POI data. Geographical Research, 2018, 37 (2): 366-378
[4]	许泽宁, 高晓路. 基于电子地图兴趣点的城市建成区边界识别方法[J]. 地理学报, 2016, 71 (6): 928-939 doi: 10.11821/dlxb201606003
	Xu Zening, Gao Xiaolu. A novel method for identifying the boundary of urban built-up areas with POI data. Acta Geographica Sinica, 2016, 71 (6): 928-939 doi: 10.11821/dlxb201606003
[5]	吴康敏, 张虹鸥, 王洋, 等. 广州市多类型商业中心识别与空间模式[J]. 地理科学进展, 2016, 35 (8): 963-974 doi: 10.18306/dlkxjz.2016.08.005
	Wu Kangmin, Zhang Hongou, Wang Yang et al. Identify of the multiple types of commercial center in Guangzhou and its spatial pattern. Progress in Geography, 2016, 35 (8): 963-974 doi: 10.18306/dlkxjz.2016.08.005
[6]	黄伟力. 基于POI的城市空间结构分析——以北京市为例[J]. 现代城市研究, 2017, (12): 87-95 doi: 10.3969/j.issn.1009-6000.2017.12.013
	Huang Weili. Ananalysis of urban spatial structure based on POI: A case study of Beijing. Modern Urban Research, 2017, (12): 87-95 doi: 10.3969/j.issn.1009-6000.2017.12.013
[7]	薛冰, 赵冰玉, 肖骁, 等. 基于POI大数据的资源型城市功能区识别方法与实证——以辽宁省本溪市为例[J]. 人文地理, 2020, 35 (4): 81-90
	Xue Bing, Zhao Bingyu, Xiaorao et al. A poi data-based study on urban functional areas of the resourcesbased city: A case study of Benxi, Liaoning. Human Geography, 2020, 35 (4): 81-90
[8]	池娇, 焦利民, 董婷, 等. 基于POI数据的城市功能区定量识别及其可视化[J]. 测绘地理信息, 2016, 41 (2): 68-73
	Chi Jiao, Jiao Limin, Dong Ting et al. Quantitative identification and visualization of urban functional area based on POI data. Journal of Geomatics, 2016, 41 (2): 68-73
[9]	冉钊, 周国华, 吴佳敏, 等. 基于POI数据的长沙市生活性服务业空间格局研究[J]. 世界地理研究, 2019, 28 (3): 163-172 doi: 10.3969/j.issn.1004-9479.2019.03.2018225
	Ran Zhao, Zhou Guohua, Wu Jiamin et al. Study on spatial pattern of consumer service industry in Changsha based on POI data. World Regional Studies, 2019, 28 (3): 163-172 doi: 10.3969/j.issn.1004-9479.2019.03.2018225
[10]	黄怡敏, 邵世维, 雷英哲, 等. 运用网络核密度估计与克里格插值识别城市功能区[J]. 测绘地理信息, 2019, 44 (4): 14-18
	Huang Yimin, Shao Shiwei, Lei Yingzhe et al. Idengtification of urban functional zones using network kernel density estimation and Kriging interpolation. Journal of Geomatics, 2019, 44 (4): 14-18
[11]	蒋云良, 董墨萱, 范婧, 等. 基于POI数据的城市功能区识别方法研究[J]. 浙江师范大学学报(自然科学版), 2017, 40 (4): 398-405
	Jiang Yunliang, Dong Moxuan, Fan Jing et al. Research on identifying urban regions of different functions based on POI data. Journal of Zhejiang Normal University(Natural Science), 2017, 40 (4): 398-405
[12]	谷岩岩, 焦利民, 董婷, 等. 基于多源数据的城市功能区识别及相互作用分析[J]. 武汉大学学报·信息科学版, 2018, 43 (7): 1113-1121
	Gu Yanyan, Jiao Limin, Dong Ting et al. Spatial distribution and interaction analysis of urban functional areas based on multi-source data. Geomatics and Information Science of Wuhan University, 2018, 43 (7): 1113-1121
[13]	龙瀛, 张宇, 崔承印. 利用公交刷卡数据分析北京职住关系和通勤出行[J]. 地理学报, 2012, 67 (10): 1339-1352 doi: 10.11821/xb201210005
	Long Ying, Zhang Yu, Cui Chengyin. Identifying commuting pattern of Beijing using bus smart card data. Acta Geographica Sinica, 2012, 67 (10): 1339-1352 doi: 10.11821/xb201210005
[14]	段亚明, 刘勇, 刘秀华, 等. 基于POI大数据的重庆主城区多中心识别[J]. 自然资源学报, 2018, 33 (5): 70-82
	Duan Yaming, Liu Yong, Liu Xiuhua et al. Identification of polycentric urban structure of central Chongqing using Points of Interest big data. Journal of Natural Resources, 2018, 33 (5): 70-82
[15]	李欣. 基于POI要素空间聚集特征的城市多中心结构识别——以郑州市为例[J]. 北京大学学报(自然科学版), 2020, 56 (4): 692-702
	Li Xin. Recognition of urban polycentric structure based on spatial aggregation characteristics of poi elements: A case of Zhengzhou City. Acta Scientiarum Naturalium Universitatis Pekinensis, 2020, 56 (4): 692-702
[16]	杨子江, 何雄, 隋心, 等. 基于POI的城市中心空间演变分析——以昆明市主城区为例[J]. 城市发展研究, 2019, 26 (2): 31-35 doi: 10.3969/j.issn.1006-3862.2019.02.005
	Yang Zijiang, He Xiong, Sui Xin et al. Analysis of the evolution of urban center space based on POI: A case study of main area in Kunming. Urban Development Studies, 2019, 26 (2): 31-35 doi: 10.3969/j.issn.1006-3862.2019.02.005
[17]	张景奇, 陈小冬, 修春亮. 基于POI数据的城市蔓延测度研究——以沈阳市为例[J]. 中国土地科学, 2019, 33 (4): 93-102
	Zhang Jingqi, Chen Xiaodong, Xiu Chunliang. Study on the measurement of urban sprawl based on the POI data: A case of Shenyang city, China. China Land Sciences, 2019, 33 (4): 93-102
[18]	梁泽, 黄姣, 韦飞黎, 等. 基于夜光遥感影像与百度POI数据的中国城市群空间范围识别方法[J]. 地理研究, 2019, 38 (6): 840-850
	Liang Ze, Huang Jiao, Wei Feili et al. Identifying the spatial range of urban agglomerations in China based on night light remote sensing and POI data. Geographical Research, 2019, 38 (6): 840-850
[19]	刘凌波, 吴昊, 赵捷, 等. 基于服务区划型兴趣点划定城市建成区边界方法[C]. 杭州: 中国城市规划年会, 2018.
	Liu Lingbo, Wu Hao, Zhao Jie et al. A new method for delimitating urban built up area with service-area based POI. Hangzhou: China Urban Planning Annual Conference, 2018.
[20]	焦利民, 李泽慧, 许刚, 等. 武汉市城市空间集聚要素的分布特征与模式[J]. 地理学报, 2017, 72 (8): 1432-1443 doi: 10.11821/dlxb201708008
	Jiao Limin, Li Zehui, Xu Gang et al. The characteristics and patterns of spatially aggregated elements in urban areas of Wuhan. Acta Geographica Sinica, 2017, 72 (8): 1432-1443 doi: 10.11821/dlxb201708008
[21]	薛冰, 肖骁, 李京忠, 等. 基于POI大数据的城市零售业空间热点分析——以辽宁省沈阳市为例[J]. 经济地理, 2018, 38 (5): 36-43
	Xue Bing, Xiao Xiao, Li Jingzhong et al. POI-based analysis on retail’s spatial hot blocks at a city level: A case study of Shenyang, China. Economic Geography, 2018, 38 (5): 36-43
[22]	王晓梦, 王锦, 朱青. 基于签到数据的城市商业空间空心化识别研究——以北京市城六区为例[J]. 城市发展研究, 2018, 25 (2): 77-84 doi: 10.3969/j.issn.1006-3862.2018.02.011
	Wang Xiaomeng, Wang Jin, Zhu Qing. Identification of hollowing phenomenon in commercial space of six district of Beijing based on checking-in data. Urban Development Studies, 2018, 25 (2): 77-84 doi: 10.3969/j.issn.1006-3862.2018.02.011
[23]	张健. 基于POI大数据的城市零售商业空间布局与人口耦合关系研究——以上海市为例[J]. 复旦学报(自然科学版), 2019, 58 (2): 151-161
	Zhang Jian. Research on the matching relationship between urban retail business spatial layout and population based on POI big data—A case study of Shanghai. Journal of Fudan University(Natural Science), 2019, 58 (2): 151-161
[24]	林清, 孙方, 王小敏, 等. 基于POI数据的北京市商业中心地等级体系研究[J]. 北京师范大学学报(自然科学版), 2019, 55 (3): 415-424
	Lin Qing, Sun Fang, Wang Xiaomin et al. Hierarchical system of Beijing commercial center judged from POI data. Journal of Beijing Normal University(Natural Science), 2019, 55 (3): 415-424
[25]	李伟, 黄正东. 基于POI的厦门城市商业空间结构与业态演变分析[J]. 现代城市研究, 2018, (4): 56-65 doi: 10.3969/j.issn.1009-6000.2018.04.009
	Li Wei, Huang Zhengdong. Commercial space structure and formate volution of Xiamen based on POI. Modern Urban Research, 2018, (4): 56-65 doi: 10.3969/j.issn.1009-6000.2018.04.009
[26]	徐薛艳, 龙涛, 孙雪飞. 上海中心城区海外游客兴趣点(POI)时空分布特征研究[J]. 世界地理研究, 2018, 27 (5): 147-156 doi: 10.3969/j.issn.1004-9479.2018.05.015
	Xu Xueyan, Long Tao, Sun Xuefei. Spatial-temporal distribution of oversea tourists’ Points of Interest(POI) in central urban area of Shanghai. World Regional Studies, 2018, 27 (5): 147-156 doi: 10.3969/j.issn.1004-9479.2018.05.015
[27]	任星怡, 宋美娜, 宋俊德. 基于用户签到行为的兴趣点推荐[J]. 计算机学报, 2017, 40 (1): 30-53
	Ren Xingyi, Song Meina, Song Junde. Point-of-interest recommendation based on the user check-in behavior. Chinese Journal of Computers, 2017, 40 (1): 30-53
[28]	徐邦智, 周亮, 蓝婷, 等. 基于POI数据的巨型城市消防站空间优化——以北京市五环内区域为例[J]. 地理科学进展, 2018, 37 (4): 535-546 doi: 10.18306/dlkxjz.2018.04.009
	Xu Bangzhi, Zhou Liang, Lan Ting et al. Spatial optimization of mega-city fire station distribution based on Point of Interest data: A case study within the 5^th Ring Road in Beijing . Progress in Geography, 2018, 37 (4): 535-546 doi: 10.18306/dlkxjz.2018.04.009
[29]	祝明明, 罗静, 余文昌, 等. 城市POI火灾风险评估与消防设施布局优化研究——以武汉市主城区为例[J]. 地域研究与开发, 2018, 37 (4): 86-91 doi: 10.3969/j.issn.1003-2363.2018.04.015
	Zhu Mingming, Luo Jing, Yu Wenchang et al. Urban fire risk evaluation and location optimization of fire station based on the POI: A case study of main urban region in Wuhan. Areal Research and Development, 2018, 37 (4): 86-91 doi: 10.3969/j.issn.1003-2363.2018.04.015
[30]	李国旗, 金凤君, 陈娱, 等. 基于POI的北京物流业区位特征与分异机制[J]. 地理学报, 2017, 72 (6): 1091-1103 doi: 10.11821/dlxb201706011
	Li Guoqi, Jin Fengjun, Chen Yu et al. Location characteristics and differentiation mechanism of logistics industry based on Points of Interest: A case study of Beijing. Acta Geographica Sinica, 2017, 72 (6): 1091-1103 doi: 10.11821/dlxb201706011
[31]	薛冰, 肖骁, 李京忠, 等. 基于POI大数据的沈阳市住宅与零售业空间关联分析[J]. 地理科学, 2019, 39 (3): 442-449
	Xue Bing, Xiao Xiao, Li Jingzhong et al. POI-based spatial correlation of the residences and retail industry in Shenyang city. Scientia Geographica Sinica, 2019, 39 (3): 442-449
[32]	薛冰, 李京忠, 肖骁, 等. 基于兴趣点(POI)大数据的人地关系研究综述: 理论、方法与应用[J]. 地理与地理信息科学, 2019, 35 (6): 51-60 doi: 10.3969/j.issn.1672-0504.2019.06.009
	Xue Bing, Li Jingzhong, Xiao Xiao et al. Overview of man-land relationship research based on POI data: Theory, method and application. Geography and Geo-Information Science, 2019, 35 (6): 51-60 doi: 10.3969/j.issn.1672-0504.2019.06.009
[33]	高岩辉, 杨晴青, 梁璐, 等 . 基于POI数据的西安市零售业空间格局及影响因素研究[J]. 地理科学, 2020, 40 (5): 710-719
	Gao Yanhui, Yang qingqing, Liang Lu et al . Urban functional zone recognition and green space evaluation of Shanghai based on poi data. Scientia Geographica Sinica, 2020, 40 (5): 710-719
[34]	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
	Yu Wenhao, Ai Tinghua. The visualization and analysis of POI features under network space supported by kernel density estimation. Acta Geodaetica et Cartographica Sinica, 2015, 44 (1): 82-90 doi: 10.11947/j.AGCS.2015.20130538
[35]	赵卫锋, 李清泉, 李必军. 利用城市POI数据提取分层地标[J]. 遥感学报, 2011, 15 (5): 973-988 doi: 10.11834/jrs.2011173
	Zhao Weifeng, Li Qingquan, Li Bijun. Extracting hierarchical landmarks from urban POI data. Journal of Remote Sensing, 2011, 15 (5): 973-988 doi: 10.11834/jrs.2011173
[36]	张铁映, 李宏伟, 许栋浩, 等. 采用密度聚类算法的兴趣点数据可视化方法[J]. 测绘科学, 2016, 41 (5): 157-162
	Zhang Tieying, Li Hongwei, Xu Donghao et al. POI data visualization based on DBSCAN algorithm. Science of Surveying and Mapping, 2016, 41 (5): 157-162
[37]	张慧杰, 王蓉, 陈斌, 等. 基于轨迹和兴趣点数据的城市功能区动态识别与时变规律可视分析[J]. 计算机辅助设计与图形学学报, 2018, 30 (9): 1728-1740
	Zhang Huijie, Wang Rong, Chen Bin et al. Dynamic identification of urban functional areas and visual analysis of time-varying patterns based on trajectory data and POIs. Journal of Computer-Aided Design&Computer Graphics, 2018, 30 (9): 1728-1740
[38]	李江苏, 梁燕, 王晓蕊. 基于POI数据的郑东新区服务业空间聚类研究[J]. 地理研究, 2018, 37 (1): 147-159
	Li Jiangsu, Liang Yan, Wang Xiaorui. Spatial clustering analysis of service industries in Zhengdong New District based on POI data. Geographical Research, 2018, 37 (1): 147-159
[39]	高旭, 桂志鹏, 隆玺, 等. KDSG-DBSCAN: 一种基于K-DTree和SparkGraphX的高性能DBSCAN算法[J]. 地理与地理信息科学, 2017, 33 (6): 1-7 doi: 10.3969/j.issn.1672-0504.2017.06.001
	Gao Xu, Gui Zhipeng, Long Xi et al. KDSG-DBSCAN: A high performance DBSCAN algorithm based on K-DT tree and Spark GraphX. Geography and Geo-information Science, 2017, 33 (6): 1-7 doi: 10.3969/j.issn.1672-0504.2017.06.001
[40]	胡健, 朱海湾, 毛伊敏. 基于自适应蜂群优化的DBSCAN聚类算法[J]. 计算机工程与应用, 2019, 55 (14): 105-114 doi: 10.3778/j.issn.1002-8331.1812-0094
	Hu Jian, Zhu Haiwan, Mao Yimin. DBSCAN clustering algorithm based on adaptive bee colony optimization. Computer Engineering and Applications, 2019, 55 (14): 105-114 doi: 10.3778/j.issn.1002-8331.1812-0094
[41]	禹文豪, 艾廷华, 杨敏, 等. 利用核密度与空间自相关进行城市设施兴趣点分布热点探测[J]. 武汉大学学报·信息科学版, 2016, 41 (2): 221-227
	Yu Wenhao, Ai Tinghua, Yang Min et al. Detecting “Hotspots” of facility POIs based on kernel density estimation and spatial auto correlation technique. Geomatics and Information Science of Wuhan University, 2016, 41 (2): 221-227
[42]	陈瑗瑗, 高勇. 利用社交媒体的位置潜语义特征提取与分析[J]. 地球信息科学学报, 2017, 19 (11): 1405-1414
	Chen Yuanyuan, Gao Yong. Extracting and analyzing latent semantic characteristics of locations using social media data. Journal of Geo-information Science, 2017, 19 (11): 1405-1414
[43]	韩春萌, 刘慧平, 张洋华, 等. 基于核密度函数的多尺度北京市休闲农业空间分布分析[J]. 农业工程学报, 2019, 35 (6): 271-278 doi: 10.11975/j.issn.1002-6819.2019.06.033
	Han Chunmeng, Liu Huiping, Zhang Yanghua et al. Multi-scale spatial distribution analysis of leisure agriculture in Beijing based on kernel density estimation. Transactions of the Chinese Society of Agricultural Engineering, 2019, 35 (6): 271-278 doi: 10.11975/j.issn.1002-6819.2019.06.033
[44]	李钢, 陈未雨, 杨兰, 等. 武汉市快递自提点的空间格局与集聚模式研究[J]. 地理科学进展, 2019, 38 (3): 407-416 doi: 10.18306/dlkxjz.2019.03.010
	Li Gang, Chen Weiyu, Yang Lan et al. Spatial pattern and agglomeration mode of parcel collection and delivery points in Wuhan City. Progress in Geography, 2019, 38 (3): 407-416 doi: 10.18306/dlkxjz.2019.03.010
[45]	Bantis T, Haworth J. Assessing transport related social exclusion using a capabilities approach to accessibility framework: A dynamic Bayesian network approach[J]. Journal of Transport Geography, 2020, (84): 102673
[46]	Bongiorno C, Santucci D, Kon F et al. Comparing bicycling and pedestrian mobility: Patterns of non-motorized human mobility in Greater Boston[J]. Journal of Transport Geography, 2019, (80): 102501
[47]	蓝振家, 郭庆胜, 董慧娟, 等. 基于海量POI数据的城市小学教育资源信息的提取与分析[J]. 测绘工程, 2016, 25 (10): 59-63
	Lan Zhenjia, Guo Qingsheng, Dong Huijuan et al. Extraction and analysis of urban primary educational resource information based on massive POI data. Engineering of Surveying and Mapping, 2016, 25 (10): 59-63

POI数据在中国城市研究中的应用

Urban Research Using Points of Interest Data in China

RichHTML

PDF (PC)

赞

可视化

摘要/Abstract

引用本文

使用本文

图/表 2

参考文献 47

相关文章 8

Metrics

本文评价

推荐阅读 0

[1]	方远平, 陆莲芯, 毕斗斗, 彭婷. 珠江三角洲港资服务业企业的空间格局及影响因素研究[J]. 地理科学, 2020, 40(9): 1421-1428.
[2]	魏宗财, 甄峰, 莫海彤, 刘晨瑜, 彭丹丽. 基于地理加权回归的中心城区共享单车出行特征及影响因素研究[J]. 地理科学, 2020, 40(7): 1082-1091.
[3]	王丹, 方斌, 张军, 陈正富. 基于社区特征分析的扬州市居住−服务业空间关联及机理[J]. 地理科学, 2020, 40(7): 1134-1141.
[4]	薛冰, 肖骁, 李京忠, 谢潇. 基于兴趣点（POI）大数据的东北城市空间结构分析[J]. 地理科学, 2020, 40(5): 691-700.
[5]	高岩辉, 杨晴青, 梁璐, 赵永宏. 基于POI数据的西安市零售业空间格局及影响因素研究[J]. 地理科学, 2020, 40(5): 710-719.
[6]	李维维, 陈田, 马晓龙. 西安城市旅游休闲业态空间热点特征及形成机制[J]. 地理科学, 2020, 40(3): 437-446.
[7]	陈卓,金凤君,王姣娥. 基于高速公路流的东北大都市区边界识别与结构特征研究[J]. 地理科学, 2019, 39(6): 929-937.
[8]	薛冰, 肖骁, 李京忠, 谢潇, 逯承鹏, 任婉侠. 基于POI大数据的沈阳市住宅与零售业空间关联分析[J]. 地理科学, 2019, 39(3): 442-449.