签到数据的城市热点分布特征与成因
|
摘要
针对当前面向城市热点空间分布特征与成因的研究缺乏考虑时效性和全面性的问题,该文提出了一种基于签到数据的城市热点探测方法,精准、便捷地探测到8种业态类型的城市热点,并在此基础上运用点模式分析和地理探测器等方法,对各业态类型城市热点的空间分布特征及其影响因素展开研究。研究结果表明:各业态类型的城市热点具有不同空间范围下的集聚性特征,且在空间分布上表现出明显的差异性;业态类型、GDP、人口规模、土地价格以及交通通达性是影响城市热点空间分布的重要因素,且同类因素对不同业态类型的城市热点的影响力不同。
Aiming at the problem that current research on spatial pattern and contributing factors of urban hot spots is lack of timeliness and comprehensiveness,a systematic survey on urban hot spots detection based on check-in data was conducted in this paper.As a result,eight types of urban hot spots were detected accurately and conveniently.On this basis,the spatial pattern and contributing factors of eight types of urban hot spots were studied by using point pattern analysis,geodetector and so on.The results showed that various urban hot spots had the characteristics of agglomeration under different spatial scales,and showed obvious differences in spatial layout.The five factors such as format,GDP,population scale,land price and traffic accessibility,affected the spatial pattern of urban hot spots,and the same factors had different influences on urban hot spots of different formats.
Aiming at the problem that current research on spatial pattern and contributing factors of urban hot spots is lack of timeliness and comprehensiveness,a systematic survey on urban hot spots detection based on check-in data was conducted in this paper.As a result,eight types of urban hot spots were detected accurately and conveniently.On this basis,the spatial pattern and contributing factors of eight types of urban hot spots were studied by using point pattern analysis,geodetector and so on.The results showed that various urban hot spots had the characteristics of agglomeration under different spatial scales,and showed obvious differences in spatial layout.The five factors such as format,GDP,population scale,land price and traffic accessibility,affected the spatial pattern of urban hot spots,and the same factors had different influences on urban hot spots of different formats.
引文
[1]王士君,浩飞龙,姜丽丽.长春市大型商业网点的区位特征及其影响因素[J].地理学报,2015,70(6):893-905.(WANG Shijun,HAO Feilong,JIANG Lili.Locations and their determinants of large-scale commercial sites in Changchun,China[J].Acta Geographica Sinica,2015,70(6):893-905.)
[2]薛东前,黄晶,马蓓蓓,等.西安市文化娱乐业的空间格局及热点区模式研究[J].地理学报,2014,69(4):541-552.(XUE Dongqian,HUANG Jing,MA Beibei,et al.Spatial distribution characteristics and hot zone patterns of entertainment industry in Xi’an[J].Acta Geographica Sinica,2014,69(4):541-552.)
[3]ZHU He,LIU Jiaming,CHEN Chen,et al.A spatialtemporal analysis of urban recreational business districts:a case study in Beijing,China[J].Journal of Geographical Sciences,2015,25(12):1521-1536.
[4]胡庆武,王明,李清泉.利用位置签到数据探索城市热点与商圈[J].测绘学报,2014,43(3):314-321.(HUQingwu,WANG Ming,LI Qingquan.Urban hotspot and commercial area exploration with check-in data[J].Acta Geodaetica et Cartographica Sinica,2014,43(3):314-321.)
[5]王波,甄峰,张浩.基于签到数据的城市活动时空间动态变化及区划研究[J].地理科学,2015,35(2):151-160.(WANG Bo,ZHEN Feng,ZHANG Hao.The dynamic changes of urban space-time activity and activity zoning based on check-in data in Sina web[J].Scientia Geographica Sinica,2015,35(2):151-160.)
[6]吴升,黄智函.基于点模式的盗窃犯罪空间分布规律分析:以福州市主城区为例[J].福州大学学报(自然科学版),2015,43(5):631-635.(WU Sheng,HUANG Zhihan.Analysis on spatial distribution regularities of theft crimes based on point pattern:in main districts of Fuzhou city as an example[J].Journal of Fuzhou University(Natural Science Edition),2015,43(5):631-635.)
[7]唐建波,刘启亮,邓敏,等.空间层次聚类显著性判别的重排检验方法[J].测绘学报,2016,45(2):233-240,249.(TANG Jianbo,LIU Qiliang,DENG Min,et al.Apermutation test for identifying significant clusters in spatial dataset[J].Acta Geodaetica et Cartographica Sinica,2016,45(2):233-240,249.)
[8]王劲峰,徐城东.地理探测器:原理与展望[J].地理学报,2017,72(1):116-134.(WANG Jinfeng,XU Chengdong.Geodetector:principle and prospective[J].Acta Geographica Sinica,2017,72(1):116-134.)
[9]湛东升,张文忠,余建辉,等.基于地理探测器的北京市居民宜居满意度影响机理[J].地理科学进展,2015,34(8):966-975.(ZHAN Dongsheng,ZHANG Wenzhong,YU Jianhui,et al.Analysis of influencing mechanism of residents’livability satisfaction in Beijing using geographical detector[J].Process in Geography,2015,34(8):966-975.)
[10]廖颖,王心源,周俊明.基于地理探测器的大熊猫生境适宜度评价模型及验证[J].地球信息科学学报,2016,18(6):767-778.(LIAO Ying,WANG Xinyuan,ZHOU Junming.Suitability assessment and validation of giant panda habitat based on geographical detector[J].Journal of Geo-information Science,2016,18(6):767-778.)
[11]徐秋荣,郑新奇.一种基于地理探测器的城镇扩展影响机理分析法[J].测绘学报,2015,44(S0):96-101.(XUQiurong,ZHENG Xinqi.Analysis of influencing mechanism of urban growth using geographical detector[J].Acta Geodaetica et Cartographica Sinica,2015,44(S0):96-101.)
[12]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,AITinghua,YANG Min,et al.Detecting“hot spots”of facility POIs based on kernel density estimation and spatial autocorrelation technique[J].Geomatics and Information Science of Wuhan University,2016,41(2):221-227.)
[13]杨喜平,方志祥,赵志远,等.顾及手机基站分布的核密度估计城市人群时空停留分布[J].武汉大学学报(信息科学版),2017,42(1):49-55.(YANG Xiping,FANGZhixiang,ZHAO Zhiyuan,et al.Analyzing space-time variation of urban human stay using kernel density estimation by considering spatial distribution of mobile phone towers[J].Geomatics and Information Science of Wuhan University,2017,42(1):49-55.)
[14]朱丹,董有福.利用Moran’s I指数进行DEM地形简化[J].武汉大学学报(信息科学版),2015,40(2):280-284.(ZHU Dan,DONG Youfu.Terrain simplification from grid DEMs based on local Moran’s Iindex[J].Geomatics and Information Science of Wuhan University,2015,40(2):280-284.)
[15]马林兵,魏慧丽,曹小曙.基于FCD数据的城市有效路网密度评价:以广州荔湾区和越秀区为例[J].地理研究,2015,34(3):541-554.(MA Linbing,WEI Huili,CAO Xiaoshu.Evaluating the valid density of road network in urban based on FCD:case of Liwan and Yuexiu district in Guangzhou city[J].Geographical Research,2015,34(3):541-554.)
[2]薛东前,黄晶,马蓓蓓,等.西安市文化娱乐业的空间格局及热点区模式研究[J].地理学报,2014,69(4):541-552.(XUE Dongqian,HUANG Jing,MA Beibei,et al.Spatial distribution characteristics and hot zone patterns of entertainment industry in Xi’an[J].Acta Geographica Sinica,2014,69(4):541-552.)
[3]ZHU He,LIU Jiaming,CHEN Chen,et al.A spatialtemporal analysis of urban recreational business districts:a case study in Beijing,China[J].Journal of Geographical Sciences,2015,25(12):1521-1536.
[4]胡庆武,王明,李清泉.利用位置签到数据探索城市热点与商圈[J].测绘学报,2014,43(3):314-321.(HUQingwu,WANG Ming,LI Qingquan.Urban hotspot and commercial area exploration with check-in data[J].Acta Geodaetica et Cartographica Sinica,2014,43(3):314-321.)
[5]王波,甄峰,张浩.基于签到数据的城市活动时空间动态变化及区划研究[J].地理科学,2015,35(2):151-160.(WANG Bo,ZHEN Feng,ZHANG Hao.The dynamic changes of urban space-time activity and activity zoning based on check-in data in Sina web[J].Scientia Geographica Sinica,2015,35(2):151-160.)
[6]吴升,黄智函.基于点模式的盗窃犯罪空间分布规律分析:以福州市主城区为例[J].福州大学学报(自然科学版),2015,43(5):631-635.(WU Sheng,HUANG Zhihan.Analysis on spatial distribution regularities of theft crimes based on point pattern:in main districts of Fuzhou city as an example[J].Journal of Fuzhou University(Natural Science Edition),2015,43(5):631-635.)
[7]唐建波,刘启亮,邓敏,等.空间层次聚类显著性判别的重排检验方法[J].测绘学报,2016,45(2):233-240,249.(TANG Jianbo,LIU Qiliang,DENG Min,et al.Apermutation test for identifying significant clusters in spatial dataset[J].Acta Geodaetica et Cartographica Sinica,2016,45(2):233-240,249.)
[8]王劲峰,徐城东.地理探测器:原理与展望[J].地理学报,2017,72(1):116-134.(WANG Jinfeng,XU Chengdong.Geodetector:principle and prospective[J].Acta Geographica Sinica,2017,72(1):116-134.)
[9]湛东升,张文忠,余建辉,等.基于地理探测器的北京市居民宜居满意度影响机理[J].地理科学进展,2015,34(8):966-975.(ZHAN Dongsheng,ZHANG Wenzhong,YU Jianhui,et al.Analysis of influencing mechanism of residents’livability satisfaction in Beijing using geographical detector[J].Process in Geography,2015,34(8):966-975.)
[10]廖颖,王心源,周俊明.基于地理探测器的大熊猫生境适宜度评价模型及验证[J].地球信息科学学报,2016,18(6):767-778.(LIAO Ying,WANG Xinyuan,ZHOU Junming.Suitability assessment and validation of giant panda habitat based on geographical detector[J].Journal of Geo-information Science,2016,18(6):767-778.)
[11]徐秋荣,郑新奇.一种基于地理探测器的城镇扩展影响机理分析法[J].测绘学报,2015,44(S0):96-101.(XUQiurong,ZHENG Xinqi.Analysis of influencing mechanism of urban growth using geographical detector[J].Acta Geodaetica et Cartographica Sinica,2015,44(S0):96-101.)
[12]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,AITinghua,YANG Min,et al.Detecting“hot spots”of facility POIs based on kernel density estimation and spatial autocorrelation technique[J].Geomatics and Information Science of Wuhan University,2016,41(2):221-227.)
[13]杨喜平,方志祥,赵志远,等.顾及手机基站分布的核密度估计城市人群时空停留分布[J].武汉大学学报(信息科学版),2017,42(1):49-55.(YANG Xiping,FANGZhixiang,ZHAO Zhiyuan,et al.Analyzing space-time variation of urban human stay using kernel density estimation by considering spatial distribution of mobile phone towers[J].Geomatics and Information Science of Wuhan University,2017,42(1):49-55.)
[14]朱丹,董有福.利用Moran’s I指数进行DEM地形简化[J].武汉大学学报(信息科学版),2015,40(2):280-284.(ZHU Dan,DONG Youfu.Terrain simplification from grid DEMs based on local Moran’s Iindex[J].Geomatics and Information Science of Wuhan University,2015,40(2):280-284.)
[15]马林兵,魏慧丽,曹小曙.基于FCD数据的城市有效路网密度评价:以广州荔湾区和越秀区为例[J].地理研究,2015,34(3):541-554.(MA Linbing,WEI Huili,CAO Xiaoshu.Evaluating the valid density of road network in urban based on FCD:case of Liwan and Yuexiu district in Guangzhou city[J].Geographical Research,2015,34(3):541-554.)
© 2004-2018 中国地质图书馆版权所有 京ICP备05064691号 京公网安备11010802017129号 地址：北京市海淀区学院路29号 邮编：100083 电话：办公室：(+86 10)66554848；文献借阅、咨询服务、科技查新：66554700 |