基于共享单车数据的居民出行热点区域与时空特征分析
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摘要
在大数据时代,基于用户地理位置的服务已被广泛应用于人们的日常生活,其位置信息体现了用户的活动范围、聚集、活动痕迹等内容.采用北京市摩拜单车数据,经数据处理、地图匹配及聚类分析后,以可视化的方式揭示居民出行热点区域与时空特征规律.研究发现,工作日出现早晚骑行高峰,多条地铁线路客流量较大,出行具有明显的潮汐现象,热点区域比休息日更为集中.
In the era of big data,services based on user's geographical location are widely used in people's daily lives,and their location information reflects the scope of activities of the user,aggregation,activity traces,and so on.In this article,we used the Mobike data to reveal hotspot and spatio-temporal features and patterns of residents in a visual way after data processing,map matching and cluster analysis. Studies were shown that there were morning and evening riding peaks on the working day,and there were a large number of passengers on the subway lines. There were an obvious tidal phenomena in the trips,and the hot spots were more concentrated than those on the rest day.
In the era of big data,services based on user's geographical location are widely used in people's daily lives,and their location information reflects the scope of activities of the user,aggregation,activity traces,and so on.In this article,we used the Mobike data to reveal hotspot and spatio-temporal features and patterns of residents in a visual way after data processing,map matching and cluster analysis. Studies were shown that there were morning and evening riding peaks on the working day,and there were a large number of passengers on the subway lines. There were an obvious tidal phenomena in the trips,and the hot spots were more concentrated than those on the rest day.
引文
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[24]周超,周亚男,李振世,等.基于大数据的南京市共享单车时空特征研究[J].西南师范大学学报(自然科学版),2018(10):66-73.
[25]北京清华同衡规划设计研究院,摩拜单车. 2017年共享单车与城市发展白皮书[EB/OL].(2017-04-12)[2018-03-10].http://www.3mbang.com/p-173424.html.
[2] GOODCHILD M F. Citizens as sensors:the world of volunteered geography[J]. GeoJournal,2007,69(4):211-221.
[3] GILES J. Wikipedia rival calls in the experts[J]. Nature,2006,443(7111):493.
[4] HEIPKE C. Crowdsourcing geospatial data[J]. ISPRS Journal of Photogrammetry and Remote Sensing,2010,65(6):550-557.
[5]单杰,秦昆,黄长青,等.众源地理数据处理与分析方法探讨[J].武汉大学学报(信息科学版),2014,39(4):390-396.
[6] KRINGS G,CALABRESE F,RATTI C,et al. Urban gravity:a model for inter-city telecommunication lows[J]. Journal of StatisticalMechanics:Theory and Experiment,2009(7):1-8.
[7] HOLLENSTEIN L,PURVES R. Exploring place through user-generated content:using flickr to describe city cores[J]. Journalof Spatial Information Science,2010,1(1):21-48.
[8] LIA L,GOODCHILD M F,XU Bo. Spatial,temporal,and socioeconomic patterns in the use of twitter and flickr[J]. Cartography&Geographic Information Science,2013,40(2):61-77.
[9]李春明,王亚军,刘尹,等.基于地理参考照片的景区游客时空行为研究[J].旅游学刊,2013,28(10):30-36.
[10]隋正伟,邬伦,刘瑜.基于签到数据的城市间交互网络研究[J].地理与地理信息科学,2013,29(6):1-5.
[11]何宗宜,苗静,彭将,等.结合微博数据挖掘的时空特征分析[J].测绘通报,2015(10):60-64.
[12]王明,胡庆武,李清泉,等.基于位置签到数据的城市分层地标提取[J].计算机学报,2016,39(2):405-413.
[13]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,2106,71(6):928-939.
[14]陈冉,王海起,孟斌,等.基于位置签到数据的城市空间分析与可视化[J].地理信息世界,2017,25(3):85-91.
[15]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.
[16]韩华瑞,代侦勇.湖北省微博签到活动空间差异分析——以新浪微博为例[J].测绘与空间地理信息,2016(10):159-162.
[17]聂琦.基于微博签到数据的出行行为分析[J].山东科学,2017(6):87-93.
[18]马云飞.基于出租车轨迹点的居民出行热点区域与时空特征研究——以昆山市为例[D].南京:南京师范大学,2014.
[19]蔡柳.基于出租车轨迹数据挖掘的居民出行特征研究[D].西安:长安大学,2017.
[20]孟祥玉,郑新奇.北京地区居民时空行为的大数据分析[J].测绘学报,2015,44(b12):1-5.
[21]陈宏飞,李君轶,秦超,等.基于微博的西安市居民夜间活动时空分布研究[J].人文地理,2015(3):57-63.
[22]陈宏飞,张心萍,赵艳慧,等.基于微博的西安市交通拥堵状况时空分布研究[J].陕西师范大学学报(自然科学版),2015(6):83-88.
[23]陈宏飞,刘广,孙九林.基于微博的西安市居民周末活动空间研究[J].陕西师范大学学报(自然科学版),2017,45(5):101-106.
[24]周超,周亚男,李振世,等.基于大数据的南京市共享单车时空特征研究[J].西南师范大学学报(自然科学版),2018(10):66-73.
[25]北京清华同衡规划设计研究院,摩拜单车. 2017年共享单车与城市发展白皮书[EB/OL].(2017-04-12)[2018-03-10].http://www.3mbang.com/p-173424.html.