基于移动通信数据的城市可视分析研究

doi:10.11959/j.issn.2096-0271.2021013

大数据 ›› 2021, Vol. 7 ›› Issue (2): 32-60.doi: 10.11959/j.issn.2096-0271.2021013

• 专题：大数据可视分析应用 • 上一篇下一篇

基于移动通信数据的城市可视分析研究

王桂娟¹^,², 周锐¹, 蔡梦杰¹, 汤勇²^,³, 李茸茸¹, 陈华容¹^,², 吴亚东⁴

¹ 西南科技大学计算机科学与技术学院，四川绵阳 621000
² 西南科技大学信息工程学院，四川绵阳 621000
³ 四川轻化工大学自动化与信息工程学院，四川自贡 643000
⁴ 四川轻化工大学计算机科学与工程学院，四川自贡 643000

出版日期:2021-03-15 发布日期:2021-03-01
作者简介:王桂娟（1981- ），女，西南科技大学信息工程学院博士生，西南科技大学计算机科学与技术学院教师，主要研究方向为可视化与可视化分析、自动可视化。
周锐（1997- ），男，西南科技大学计算机科学与技术学院硕士生，主要研究方向为网络可视化。
蔡梦杰（1994- ），男，西南科技大学计算机科学与技术学院硕士生，主要研究方向为城市计算可视分析。
汤勇（1988- ），男，西南科技大学信息工程学院博士生，四川轻化工大学自动化与信息工程学院教师，主要研究方向为可视化与可视分析。
李茸茸（1996- ），女，西南科技大学计算机科学与技术学院硕士生，主要研究方向为城市计算可视分析。
陈华容（1977- ），女，西南科技大学信息工程学院博士生，西南科技大学计算机科学与技术学院讲师，主要研究方向为可视化与可视化分析。
吴亚东（1979- ），男，博士，四川轻化工大学计算机科学与工程学院教授、博士生导师，主要研究方向为可视化、可视分析、人机交互、虚拟现实。
基金资助:
国家自然科学基金资助项目(61872304);四川轻化工大学人才引进项目(2020RC20)

A survey on mobile communication data based urban visual analysis

Guijuan WANG¹^,², Rui ZHOU¹, Mengjie CAI¹, Yong TANG²^,³, Rongrong LI¹, Huarong CHEN¹^,², Yadong WU⁴

¹ School of Computer Science and Technology, Southwest University of Science and Technology, Mianyang 621000, China
² School of Information Engineering, Southwest University of Science and Technology, Mianyang 621000, China
³ School of Automation and Information Engineering, Sichuan University of Science and Engineering, Zigong 643000, China
⁴ School of Computer Science and Engineering, Sichuan University of Science and Engineering, Zigong 643000, China

Online:2021-03-15 Published:2021-03-01
Supported by:
The National Natural Science Foundation of China(61872304);Talent Introduction Project of Sichuan University of Science and Engineering(2020RC20)

摘要/Abstract

摘要：

随着移动电话的深入普及，大规模通信数据给人们提供了前所未有的观测城市微观结构和动态的机会，而如此大规模的高维异构时空关系数据又给高效数据解读带来了挑战。作为重要的大数据分析手段，可视化被越来越多地应用到这一领域。回顾近年来基于通信数据的城市可视分析研究工作，首先归纳了移动通信数据的主要来源、特征和常用的数据处理方法，然后从通信数据的内在对象“人”“通信设备”和“城市空间”3个方面阐述相应的可视化方法，并对基于通信数据的城市可视分析面向的任务、方法和特点进行了梳理，最后对基于通信数据的城市可视分析进行了展望。

关键词: 通信数据, 可视分析, 智能城市感知, 人类行为模式, 城市动态

Abstract:

With the popularity of mobile phones, large-scale communication data has brought people unprecedented opportunities to observe the urban microstructure and dynamics.However the complexity of such large-scale high-dimensional heterogeneous spatio-temporal data has brought challenges to effective data interpretation.Accordingly, visualization, as an important means of big data analysis, is being applied to this field more and more.The recent research work on urban visual analysis based on communication data was reviewed.Firstly, the major sources, characteristics and common data processing methods of the mobile phone data were summarized.Secondly, the corresponding visualization methods from three aspects were elaborated, namely, the internal objects of mobile communication data such as human, communication equipment and urban space, and the frequent urban visual analysis task and features based on communication data were summarized.Finally, the prospect of urban visual analysis based on communication data was presented.

Key words: communication data, visual analysis, intelligent urban sensing, human behavior pattern, urban dynamics

中图分类号:

TP311

王桂娟, 周锐, 蔡梦杰, 汤勇, 李茸茸, 陈华容, 吴亚东. 基于移动通信数据的城市可视分析研究[J]. 大数据, 2021, 7(2): 32-60.

Guijuan WANG, Rui ZHOU, Mengjie CAI, Yong TANG, Rongrong LI, Huarong CHEN, Yadong WU. A survey on mobile communication data based urban visual analysis[J]. Big Data Research, 2021, 7(2): 32-60.

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数据		属性	代表文献
来源	类型	属性	代表文献
通信运营商	通话记录详单	主被叫用户ID	[2][6-12]
		通话开始时间
		通话持续时间
		关联基站
	基站数据	基站编号	[13-15]
		基站地址
		基站经纬度坐标
	手机网络数据（信令数据、基站切换）	用户ID	[5][16]
		访问网址
		接入起止时间
		关联基站位置
机构和个人	定制App/开放项目	App使用情况	[17-18]
		智能手机事件 log
		Wi-Fi
		GPS
		POI
		源-目的地(origindestination，OD）数据

可视化对象	可视化焦点	数据特征	可视化方法
城市用户	用户通话模式	时间、空间、数量	折线图、日历图、径向图、地图、平行坐标、雷达图
	城市用户画像	时间、空间、关系	甘特图、线图、词云、ego画像
	城市用户关系	关系、数量	甘特图、节点-连接图、矩阵图、星形图、ego网络、社会网络图
通信设备	基站模式	时间、空间、数量	柱状图、像素矩阵、地图
	基站覆盖范围	空间、关系	维诺图、饼图
城市空间	城市空间热度	空间、数量	热力地图、渐变色地图
	城市区域划分	空间、时间、数量	六边形分隔地图、正方形分隔地图、维诺图、节点-连接图
	城市区域动态	时间、空间、轨迹	时空立方体

城市可视分析类别	可视分析任务	分析方法	代表性工作
城市语义感知	城市人的语义感知	统计方法、多视图协同、相似性分析、相关性分析、群体状态立方体	[18][28][42][46][50][57]
	城市空间语义感知	非负矩阵分解（nonnegative matrix factorization， NMF）、词嵌入、相似性计算、统计方法、聚类分析	[13][19][48]
	城市动态语义感知	时空相似性计算、POI向量、t-SNE降维、统计方法、蓝噪声采样、词嵌入、3D时空路径建模、哈希、聚类	[14][17][27][53][58]
城市预测与异常分析	通信设备异常检测	统计方法、聚类、假设检验	[6][42][59-60]
	社会群体异常检测	社区发现算法、空间概率密度、分类算法、ego网络	[28][34][61]
	社会事件异常检测	聚类、相似性计算、预测算法、纯可视分析	[5][7][62]
城市资源优化	基站和通信设施优化	MuLSTM预测、聚类、统计方法	[6][16][63]
	城市交通设施优化	K-means、DBSCAN、OD流聚合、统计方法	[1][9][64]
	城市配套资源优化	统计方法、机器学习SVM、决策树	[65-67]

基于移动通信数据的城市可视分析研究

A survey on mobile communication data based urban visual analysis

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