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

Abstract

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

CLC Number:

TP311

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.

Figures/Tables 20

References 71

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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]

A survey on mobile communication data based urban visual analysis

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