Visual analysis of multivariate spatio-temporal data for origin-destination flow

doi:10.11772/j.issn.1001-9081.2023020178

Journal of Computer Applications ›› 2024, Vol. 44 ›› Issue (2): 452-459.DOI: 10.11772/j.issn.1001-9081.2023020178

Special Issue: 数据科学与技术

• Data science and technology • Previous Articles Next Articles

Visual analysis of multivariate spatio-temporal data for origin-destination flow

Siyi ZHOU¹, Tianrui LI¹^,²^,³^,⁴()

^1.School of Computing and Artificial Intelligence，Southwest Jiaotong University，Chengdu Sichuan 611756，China
^2.Engineering Research Center of Sustainable Urban Intelligent Transportation，Ministry of Education，Chengdu Sichuan 611756，China
^3.National Engineering Laboratory of Integrated Transportation Big Data Application Technology （Southwest Jiaotong University），Chengdu Sichuan 611756，China
^4.Manufacturing Industry Chains Collaboration and Information Support Technology Key Laboratory of Sichuan Province （Southwest Jiaotong University），Chengdu Sichuan 611756，China

Received:2023-02-27 Revised:2023-04-06 Accepted:2023-04-11 Online:2024-02-22 Published:2024-02-10
Contact: Tianrui LI
About author:ZHOU Siyi， born in 1998， M. S. candidate. Her research interests include data visualization.
Supported by:
National Natural Science Foundation of China(62176221)

面向源-目的地流的多元时空数据可视分析

周思艺¹, 李天瑞¹^,²^,³^,⁴()

^1.西南交通大学计算机与人工智能学院, 成都 611756
^2.可持续城市交通智能化教育部工程研究中心, 成都 611756
^3.综合交通大数据应用技术国家工程实验室(西南交通大学), 成都 611756
^4.四川省制造业产业链协同与信息化支撑技术重点实验室(西南交通大学), 成都 611756

通讯作者: 李天瑞
作者简介:周思艺（1998—），女，四川内江人，硕士研究生，CCF会员，主要研究方向：数据可视化；
基金资助:
国家自然科学基金资助项目(62176221)

Abstract

Abstract:

Integrated Circuit （IC） card can record a resident’s mobile travel， reflecting the resident’s Origin-Destination （OD） information. However， due to the large scale of OD flow data， it is easy to cause visual clutter when visualizing the spatial distribution of OD flow directly. Moreover， multivariate data is difficult to be combined with flow data because it contains a variety of different types of data. To solve the problem that direct visualizing the spatial distribution of large-scale OD data is easy to cause visual occlusion， a flow clustering method based on Orthogonal Nonnegative Matrix Decomposition （ONMF） was proposed. The OD data was clustered before being visualized， so that unnecessary occlusion was reduced. For that it is difficult to combine and analyze multivariate spatio-temporal data with multiple types， a site multivariate time series data view for bus stop was designed. Bus stop flow and four types of multivariate data — air quality， air temperature， relative humidity， and rainfall were coded on the same time series， to improve the spatial utilization rate of the view， and could be compared and analyzed. To assist users to explore and analyze， an interactive visual analysis system was developed based on origin-destination flow and multivariate data， and a variety of interactive operations were designed to improve the efficiency of user exploration. Finally， based on the Singapore IC card dataset， the proposed clustering method was evaluated from clustering effect and running time. In the comparison experiment results， using silhouette coefficient to evaluate the clustering effect， the clustering effect of the proposed method is improved by 0.028 compared with the original method and 0.253 compared with K-means clustering method. The running time comparison results show that its running time is 254 seconds less than that of ONMFS （Orthogonal NMF through Subspace exploration） method with better clustering effect. The effectiveness of the system was verified by case analysis and system function comparison.

Key words: Integrated Circuit (IC) card, Origin-Destination (OD) flow, multivariate data, spatio-temporal data, visual analysis

摘要：

交通智能（IC）卡可以记录居民的移动出行，反映居民的源-目的地（OD）信息；但智能卡记录的OD流数据规模大，直接可视化空间分布容易导致视觉杂乱，并且多元数据类型多，更难以和流数据结合对比分析。首先，针对直接可视化大规模OD数据的空间分布容易视觉遮挡的问题，提出基于正交非负矩阵分解（ONMF）的流聚类方法。所提方法对源-目的地数据聚类后再可视化，可以减少不必要的遮挡。然后，针对多元时空数据类型多难以结合对比分析的问题，设计了公交站点多元时序数据视图。该可视化方法将公交站点的流量大小和空气质量、空气温度、相对湿度、降雨量这四类多元数据在同一时间序列上编码，提高了视图的空间利用率并且可以对比分析。再次，为了辅助用户探索分析，开发了基于OD流和多元数据的交互式可视分析系统，并设计了多种交互操作提升用户探索效率。最后，基于新加坡交通智能卡数据集，从聚类效果和运行时间对该聚类方法评估。结果显示，在用轮廓系数评估聚类效果上，所提方法比原始方法提升了0.028，比用K均值聚类方法提升了0.253；在运行时间上比聚类效果较好的ONMFS（ONMF through Subspace exploration）方法少了254 s。通过案例分析和系统功能对比验证了系统的有效性。

关键词: 交通智能卡, 源-目的地流, 多元数据, 时空数据, 可视分析

CLC Number:

TP391.41

Siyi ZHOU, Tianrui LI. Visual analysis of multivariate spatio-temporal data for origin-destination flow[J]. Journal of Computer Applications, 2024, 44(2): 452-459.

周思艺, 李天瑞. 面向源-目的地流的多元时空数据可视分析[J]. 《计算机应用》唯一官方网站, 2024, 44(2): 452-459.

Figures/Tables 13

Fig. 1 System analysis flow

Tab. 1 Data description

数据源	描述
IC卡数据	IC（id，type，up_date，down_date，up_id，down_id）
公交站点	Bus（id，name，lng，lat）
POI	POI（name，type，lng，lat）
温度	TEMP（id，name，lng，lat，value，date）
相对湿度	RH（id，name，lng.lat，value，date）
降雨量	Rain （id，name，lng，lat，value，date）
SO₂	SO2（id，name，lng，lat，value，date）
NO₂	NO2（id，name，lng，lat，value，date）
PM_2.5	PM25（id，name，lng，lat，value，date）

Tab. 2 AQI level judgment boundary values

AQI（ $I l o w$ ~ $I h i g h$ ）	SO₂/（ppb·h^-1）	NO₂/（ppb·h^-1）	PM_2.5/（μg·m^-3）
优（0~50）	0~35	0~53	0.0~12.0
良（51~100）	36~75	54~100	12.1~35.4
轻度污染（101~150）	76~185	101~360	35.5~55.4
污染（151~200）	186~304	361~649	55.5~150.4
严重污染（201~300）	305~604	650~1 249	150.5~250.4

Tab. 2 AQI level judgment boundary values

AQI（ $I l o w$ ~ $I h i g h$ ）	SO₂/（ppb·h^-1）	NO₂/（ppb·h^-1）	PM_2.5/（μg·m^-3）
优（0~50）	0~35	0~53	0.0~12.0
良（51~100）	36~75	54~100	12.1~35.4
轻度污染（101~150）	76~185	101~360	35.5~55.4
污染（151~200）	186~304	361~649	55.5~150.4
严重污染（201~300）	305~604	650~1 249	150.5~250.4

Fig. 2 Feature extraction method

Fig. 3 Hierarchical visual analysis system interface based on OD flow and multivariate data

Fig. 4 Visual design of POI distribution in hierarchical communities

Fig. 5 Sensitivity analysis experiments for mode quantity value K

Fig. 6 Determination of weighting coefficient

Fig. 7 Experiment results of method comparison

Tab. 3 Ablation experimental results

方法	轮廓系数
原始方法	0.913
只加空间平滑约束矩阵 A	0.937
只加多元数据平滑约束矩阵 B	0.936
本文方法	0.941

Fig. 8 Spaio-temporal pattern analysis of traffic flow transmission in cluster 3 of bus stops

Fig. 9 Comparative analysis of multivariate time series data of bus stops

Tab. 4 Comparison results of system application

分析任务	分析方法
分析任务	文献［3］方法	文献［21］方法	文献［22］方法	方法本文
群体移动	√	√	√	√
时空异常	√	×	√	√
隐藏关系	×	√	√	√
统计属性	√	√	√	√
多尺度时空分析	√	√	√	√
个体多元数据分析	×	×	×	√

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Visual analysis of multivariate spatio-temporal data for origin-destination flow

面向源-目的地流的多元时空数据可视分析

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