城市邻近基站间人群流动时空变化同步性分析

doi:10.12082/dqxxkx.2018.170550

地球信息科学学报 ›› 2018, Vol. 20 ›› Issue (6): 844-853.doi: 10.12082/dqxxkx.2018.170550

• 2017年中国地理信息科学理论与方法学术年会优秀论文专辑 • 上一篇下一篇

城市邻近基站间人群流动时空变化同步性分析

朱菁玮¹(), 方志祥^1,^2,^*(), 杨喜平³, 尹凌⁴

1. 武汉大学测绘遥感信息工程国家重点实验室,武汉 430079
2. 地球空间信息技术协同创新中心,武汉 430079
3. 陕西师范大学地理科学与旅游学院,西安 710119
4. 中国科学院深圳先进技术研究院,深圳 518055

收稿日期:2017-11-20 修回日期:2018-03-18 出版日期:2018-06-20 发布日期:2018-06-20
通讯作者: 方志祥 E-mail:zhujw@whu.edu.cn;zxfang@whu.edu.cn
作者简介:
作者简介：朱菁玮(1993-),女,硕士生,主要从事时空地理信息科学研究。E-mail: zhujw@whu.edu.cn
基金资助:
国家自然科学基金项目(41231171、41771473);中央高校基本科研业务费专项资金项目（2042017kf0235、

Flow Synchronization of Mobile Communication Network in Cities Areas

ZHU Jingwei¹(), FANG Zhixiang^1,^2,^*(), YANG Xiping³, YIN Ling⁴

1. State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan, 430079, China
2. Collaborative Innovation Center of Geospatial Technology, Wuhan 430079, China
3. School of Geography and Tourism, Shaanxi Normal University, Xi′an, 710119, China;
4. Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China.

Received:2017-11-20 Revised:2018-03-18 Online:2018-06-20 Published:2018-06-20
Contact: FANG Zhixiang E-mail:zhujw@whu.edu.cn;zxfang@whu.edu.cn
Supported by:
National Natural Science Foundation of China, No.41231171, 41771473;The Fundamental Research Funds for the Central Universities, No.2042017kf0235, GK201803049

摘要/Abstract

摘要：

不同区域人群流量随时间的变化可以反映城市结构的空间差异。现有对于城市人群空间分布特性的研究大都以人群密度计算为基础,注重时空切片尺度,但是不能有效刻画邻域空间单元间流量变化的时空过程同步特性。本文提出一种基于基站间流量变化过程相似度的城市邻域基站流量变化同步性度量方法,量化分析不同区域的人群进出流量过程的相似程度,研究城市中具有相同人群流量变化过程的同步性区域空间分布规律。以深圳市为例,对城市同步性区域的空间分布与特点进行剖析。实验发现：计算同步性时参数选择需根据城市本身基站分布及流量特点分析,一般研究中,城市基站平均距离可作为邻近区域半径d,描述基站间流量变化相似度的特征阈值λ选取与邻近区域半径有关,半径越小,阈值取值越小。通过基站人群流量同步性得到的城市同步区域的空间分布不同于行政区域划分结果,同步区域面积小,划分更为精细;且规划级别越高的中心区,其范围内基站同步区域数目越多。最后,将同步区域结果与流量密度图对比,发现该方法不仅能够发现流量变化大的同步区域,并且能够发现城市中流量变化小的同步区域。本文提出的方法能量化衡量区域流量变化同步性并发现具有不同流量变化特点的同步性整体区域,对城市人群空间变化特点进行分析,可用于指导和评价城市规划与实际人群活动区域效果,以及城市服务设施布局等。

关键词: 手机数据, 时空轨迹相似度, 流量同步性, 同步区域, 流量变化过程

Abstract:

Distribution and movement of people in urban areas are important information for studying urban dynamics and crowd displacement rhythm. The temporal variation of people flow between different spatial regions shows how people interact with physical locations and is highly related to its function and structure in the city. Previous researches usually use density-based approaches to investigate the temporal variation of people flows in different spatial regions. This kind of approach can present time slice-based hot-spot maps but cannot reflect the consistency of changing processes. Therefore, this paper proposes an approach of synchronization measurement to fill in this gap. Our method is designed to measure the similarity of temporal people flow processes between stations in the 3D feature space and quantify property of synchronization of communication network areas based on the average similarity. An experiment of measuring synchronization was conducted using a dataset of mobile phone data in Shenzhen. The people flow processes within this city was derived from the mobile phone dataset. The results show: firstly, the neighboring-area radius and feature space threshold depend on the distribution of mobile station and flow process in corresponding serving areas. In most cases, the neighboring-area radius can be set as the average distance between mobile stations. The feature space threshold depends on the neighboring area radius, and the smaller the radius, the smaller the threshold should be. Secondly, different from administrative areas, the synchronized areas show the characteristics of human dynamics in the city with a smaller spatial unit. We found that the centers with higher level of planning have more synchronized regions with relatively small area in them. Finally, compared with the density map result, our approach indicates that the synchronized regions not only exist in the city centers with high flow changes but also in rural areas with relatively small flow changes. Combined with additional information such as land use attributes, the synchronized areas clarify the spatial structure of the population and its aggregated boundary effect in the city. This approach can be used to assess the output of urban planning and optimize the distribution of service facilities such as emergency management and transportation network design.

Key words: mobile phone data, space-time trajectory similarity, flow synchronization, synchronized areas, flow processes

朱菁玮, 方志祥, 杨喜平, 尹凌. 城市邻近基站间人群流动时空变化同步性分析[J]. 地球信息科学学报, 2018, 20(6): 844-853.DOI:10.12082/dqxxkx.2018.170550

ZHU Jingwei,FANG Zhixiang,YANG Xiping,YIN Ling. Flow Synchronization of Mobile Communication Network in Cities Areas[J]. Journal of Geo-information Science, 2018, 20(6): 844-853.DOI:10.12082/dqxxkx.2018.170550

图/表 11

图1

图2

图3

图4

表1

图5

表2

图6

图7

图8

表3

参考文献 23

[1]	张仁军,Daniel Z Sui,惠红.多粒度的人群移动模拟通用模型及其应用[J].地理与地理信息科学,2011,27(2):11-15.
	[ Zhang R J, Daniel Z S, Hui H.A general model for people movements based on multi-granularity and its application[J]. Geography and Geo-information Science, 2011,27(2):11-15. ]
[2]	宋宏权. 区域人群状态与行为的时空感知方法[D].南京:南京师范大学,2013.
	[ Song H Q.Spatial-temporal perception methods for regional crowd[D]. Nanjing: Nanjing Normal University, 2013. ]
[3]	徐赞新,王钺,司洪波,等.基于随机矩阵理论的城市人群移动行为分析[J].物理学报,2011,60(4):53-59.
	[ Xu Z X, Wang Y, Si H B, et al.Analysis of urban mobility behavior based on random matrix theory[J]. Acta Physica Sinica, 2011,60(4):53-59. ]
[4]	Calabrese F, Diao M, Di Lorenzo G, et al.Understanding individual mobility patterns from urban sensing data: A mobile phone trace example[J]. Transportation research part C: Emerging technologies, 2013,26:301-313. doi: 10.1016/j.trc.2012.09.009
[5]	Wang Y, Li L, Yu J, et al.Identifying the urban transportation corridor based on mobile phone data[C]. Smart Cities Conference (ISC2), 2015 IEEE First International, 2015:1-6.
[6]	包婷,章志刚,金澈清.基于手机大数据的城市人口流动分析系统[J].华东师范大学学报(自然科学版),2015(5):162-171. doi: 10.3969/j.issn.1000-5641.2015.05.014
	[ Bao T, Zhang Z G, Jin C Q.Into digitization: Some concepts and methods of Chinese historical geographic information system[J]. Historical Geography, 2002(5):162-171. ] doi: 10.3969/j.issn.1000-5641.2015.05.014
[7]	秦昆,周勍,徐源泉,等.城市交通热点区域的空间交互网络分析[J].地理科学进展,2017,36(9):1149-1157.
	[ Qin K, Zhou Q, Xu Y Q,et al.Spatial interaction network analysis of urban traffic hotspots[J]. Progress in Geography, 2017,36(9):1149-1157. ]
[8]	班雷雨,霍欢,徐彪.基于移动数据的人群活动热点区域的发现[J].中国科学技术大学学报,2015,45(10):829-835,863. doi: 10.3969/j.issn.0253-2778.2015.10.005
	[ Ban L Y, Huo H, Xu B.Discovery of hot regions about crowd activities based on mobility data[J]. Journal of University of Science and Technology of China, 2015,45(10):829-835,863. ] doi: 10.3969/j.issn.0253-2778.2015.10.005
[9]	丁亮,钮心毅,宋小冬.利用手机数据识别上海中心城的通勤区[J].城市规划,2015,39(9):100-106. doi: 10.11819/cpr20150917a
	[ Ding L, Niu X Y, Song X D.Identifying the commuting area if shanghai central city using mobile phone data[J]. Urban Planning, 2015,39(9):100-106. ] doi: 10.11819/cpr20150917a
[10]	刘瑜. 社会感知视角下的若干人文地理学基本问题再思考[J].地理学报,2016,71(4):564-575. doi: 10.11821/dlxb201604003
	[ Liu Y.Revisiting several basic geographical concepts: A social sensing perspective[J]. Acta Geographica Sinica, 2016,71(4):564-575. ] doi: 10.11821/dlxb201604003
[11]	萧世伦,方志祥.从时空GIS视野来定量分析人类行为的思考[J].武汉大学学报·信息科学版,2014,39(6):667-670. doi: 10.13203/j.whugis20140127
	[ Shaw S L, Fang Z X.Rethinking behavior research from the perspective of space-time GIS[J]. Geomatics and Information Science of Wuhan University, 2014,39(6):667-670. ] doi: 10.13203/j.whugis20140127
[12]	郭璨,甄峰,朱寿佳.智能手机定位数据应用于城市研究的进展与展望[J].人文地理,2014,29(6):18-23.
	[ Guo C, Zhen F, Zhu S J.Progress and prospect of the application of smart phone LBS data in urban researches[J]. Human Geography, 2014,29(6):18-23. ]
[13]	王富臣.论城市结构的复杂性[J].城市规划汇刊,2002(4):26-28,78-79.
	[ Wang F C. On complexity of urban structure[J]. Urban Planning Forum, 2002(4):26-28,78-79. ]
[14]	Liu X, Kang C, Gong L, et al.Incorporating spatial interaction patterns in classifying and understanding urban land use[J]. International Journal of Geographical Information Science, 2016,30(2):334-350. doi: 10.1080/13658816.2015.1086923
[15]	Ratti C, Frenchman D, Pulselli R M, et al.Mobile landscapes: using location data from cell phones for urban analysis[J]. Environment and Planning B: Planning and Design, 2016,33(5):727-748.
[16]	戴蓉蓉,朱海红,李霖.基于ARIMA模型的市内人群移动预测[J].测绘工程,2016,25(2):38-41.
	[ Dai R R, Zhu H H, Li L.Intra-urban human mobility prediction based on ARIMA model[J]. Engineering of Surveying and Mapping, 2016,25(2):38-41. ]
[17]	Candia J, González M C, Wang P, et al.Uncovering individual and collective human dynamics from mobile phone records[J]. Journal of Physics A: Mathematical and Theoretical, 2008,41(22):224015. doi: 10.1088/1751-8113/41/22/224015
[18]	张仕学,李石君,余伟,等.突发事件人群异常聚集热点区域预测[J].中国安全科学学报,2015,25(9):159-164.
	[ Zhang S X, Li S J, Yu W, et al. Prediction on hot region of crowd abnormal gathering on unexpected event[J]. China safety Science Journal, 2015,25(9):159-164. ]
[19]	Dodge S, Weibel R, Laube P.Trajectory similarity analysis in movement parameter space[C]. Processing of 19th GIS Research UK Annual Conference, 2011:27-29.
[20]	Gao S, Liu Y, Wang Y, et al.Discovering spatial interaction communities from mobile phone data[J]. Transactions in GIS, 2015,17(3):463-481. doi: 10.1111/tgis.12042
[21]	Keogh E.Fast similarity search in the presence of longitudinal scaling in time series databases[C]. Processing on 9th IEEE International Conference-Tools with Artificial Intelligence, 1997:578-584.
[22]	汪伟,尹凌.基于频繁活动点集的手机位置数据隐私保护方法[J].计算机应用研究,2017,34(3):867-870. doi: 10.3969/j.issn.1001-3695.2017.03.053
	[ Wang W, Yin L.Privacy protection method for mobile phone location data based on matching point sets of frequent activity locations[J]. Application Research of Computer, 2017,34(3):867-870. ] doi: 10.3969/j.issn.1001-3695.2017.03.053
[23]	《深圳市城市总体规划(2010-2020)》[EB/LO].
	[ The comprehensive Plan of Shenzhen City [EB/LO]. ]

d值/m	λ值
d值/m	统计	0.025	0.03	0.035	0.04	0.045
200	数目	312	327	338	334	340
200	面积	18.63	21.05	23.30	26.42	28.91
300	数目	350	362	356	336	329
300	面积	26.13	31.77	38.34	46.04	52.60
400	数目	329	364	349	324	280
400	面积	43.82	46.68	54.79	68.42	87.11
500	数目	334	338	317	289	258
500	面积	48.21	57.95	76.13	98.03	103.47
600	数目	320	349	299	293	292
600	面积	57.25	62.11	70.36	66.23	73.33
700	数目	336	339	323	292	295
700	面积	56.52	47.55	47.03	49.74	57.72
800	数目	315	326	309	300	308
800	面积	44.12	48.85	53.84	63.26	70.57
900	数目	314	324	346	327	320
900	面积	50.19	61.41	57.70	56.31	58.89
1000 m	数目	312	334	315	322	321
1000 m	面积	47.51	49.40	52.27	55.14	39.33

中心级别	中心区名称	数量/个	平均面积/10⁴ m²
主中心	福田	53	37.129 13
	罗湖	36	21.741 72
	南山	42	65.253 07
副中心	光明新城	24	143.683 70
	龙岗	28	104.982 20
	龙华	38	56.657 80
	坪山新城	18	128.857 20
	盐田	13	75.796 65
组团中心	布吉	18	123.960 40
	观澜	13	81.174 19
	航空城	41	27.042 90
	横岗	8	175.219 70
	葵涌	4	72.110 76
	平湖	6	156.920 10
	沙井	25	28.574 77
	松岗	13	56.319 55

同步级别	流量级别
同步级别	1	2	3	4	5	6	7	8	9	10	11
1	0	0	13	22	72	5099	5153	34	1	3	0
2	4	1	6	8	35	1209	1259	37	5	9	4
3	0	2	6	17	53	1236	1304	67	17	14	0
4	0	3	8	13	62	1395	1502	72	17	11	0
5	1	2	4	16	75	1297	1326	69	9	7	0
6	0	3	4	8	48	1072	1054	54	4	7	0
7	0	0	0	0	6	653	712	9	0	0	0
8	0	0	0	0	1	465	451	3	0	0	0
9	0	0	0	0	2	328	335	2	0	0	0
10	0	0	0	0	1	699	772	0	0	0	0

城市邻近基站间人群流动时空变化同步性分析

Flow Synchronization of Mobile Communication Network in Cities Areas

RichHTML

PDF (PC)

赞

可视化

被引次数

摘要/Abstract

引用本文

使用本文

图/表 11

参考文献 23

相关文章 5

Metrics

本文评价

推荐阅读 0

[1]	方志祥, 倪雅倩, 黄守倩. 顾及上网行为特征的手机用户停留行为预测方法[J]. 地球信息科学学报, 2020, 22(1): 136-144.
[2]	陈丽娜, 吴升, 陈洁, 李明晓, 陆锋. 基于手机定位数据的城市人口分布近实时预测[J]. 地球信息科学学报, 2018, 20(4): 523-531.
[3]	尹凌, 姜仁荣, 赵志远, 宋晓晴, 李晓明. 利用手机通话位置数据估计城市24 h人口分布误差[J]. 地球信息科学学报, 2017, 19(6): 763-771.
[4]	方志祥, 倪雅倩, 张韬, 冯明翔, 于冲. 利用终端位置时空转移概率预测通讯基站服务用户规模[J]. 地球信息科学学报, 2017, 19(6): 772-781.
[5]	徐金垒, 方志祥, 萧世伦, 尹淩. 城市海量手机用户停留时空分异分析——以深圳市为例[J]. 地球信息科学学报, 2015, 17(2): 197-205.