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收稿：2019-01-12，
修回：2019-6-3，
录用：2019-6-10，
纸质出版：2019-12-16
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目的
2
海量城市交通事故数据可能蕴含有交通事故的空间模式，挖掘出交通事故的空间模式有助于开展交通事故的防治工作。目前交通管理部门虽然记录了交通事故发生地的空间位置信息，但没有对事故发生地进行空间语义描述，从而影响对交通事故空间模式的深入分析。因此，提出一种交通事故数据空间语义增强方法，并设计了一套可视分析系统。
方法
2
基于城市兴趣点来增强交通事故数据的空间语义。以事故发生点为中心获取周围城市兴趣点，使用特征向量刻画兴趣点的数量、类别及其与事故发生点的距离，并称此向量为空间语义特征向量。将空间语义特征向量和相应的交通事故关联，以达到增强其空间语义的目的。然后，基于空间语义特征向量，使用自组织映射聚类算法对交通事故进行聚类分析，根据其空间语义特征将交通事故分为若干类别。最后，通过使用地图视图展示事故点数据、聚类视图和平行坐标视图展示聚类分析的结果及其空间语义特征的可视化方法，对交通事故的空间模式进行分析。
结果
2
针对空间语义增强的交通事故数据以及相关分析任务，有效地使用上述数据分析方法与可视化技术，设计并实现了一套多视图关联的可视分析系统，提供了便捷的交互方式辅助用户分析。通过研发人员和交通警察共同对安徽省合肥市2018年的交通事故数据进行分析，将交通事故发生地划分9类并指出每类地点的空间语义特点，进一步分析出了事故高发区域的空间语义特性。
结论
2
本文提出的交通事故数据空间语义增强方法和可视分析方法可以帮助用户揭示交通事故的空间语义模式，有助于深入分析和认识交通事故的成因，能为交通事故防治相关的城市建设工作提供建议。
Objective
2
With the recent development of smart cities
urban big data are increasingly becoming available
including traffic accident data. Big traffic accident data may contain spatial patterns of traffic accidents and are valuable for traffic accident prevention and management by mining spatial patterns from traffic accident data. Although traffic accident position is currently available
its spatial-semantic information is missing
which is adverse for its spatial pattern analysis. This study presents a method to enhance the spatial semantics of traffic accident data and designs a visual analytic system to analyze spatial patterns from spatial semantic-enhanced traffic accident data.
Method
2
Point of interest (POI) is used to enhance the spatial semantics of traffic accidents. First
all POIs around a traffic accident are collected to form a POI collection
and a feature vector is defined according to the number of POIs
type of POIs
and distance between POIs and traffic accident. The feature vector is named the spatial-semantic feature vector because it encodes spatial semantic information. This vector is associated with traffic accident data to enhance the traffic accident data's spatial semantics. Second
self-organizing map (SOM) clustering algorithm is applied to analyze spatial semantic-enhanced traffic accident data according to the spatial-semantic feature vector
and several clusters are obtained for further analysis. Each resulting cluster implies some spatial semantic information because the spatial-semantic feature vector is used for clustering. Finally
a visual analytic system with linked views is designed and implemented to analyze the spatial semantic-enhanced traffic accident data and the resulting clusters. Map view using heat map and glyphs is applied to visualize the distribution of traffic accident data. Histogram view and parallel coordinate view are used to visualize clusters and spatial-semantic feature vectors
respectively. Several interaction methods are provided to help users filter data of interest for the traffic accidents' spatial pattern.
Result
2
Through cooperation with two traffic policemen from Hefei Traffic Police Division
the authors analyze the traffic accidents in Hefei City using the presented visual analytic system and obtain nine clusters via SOM clustering. The spatial-semantic features of the nine clusters are analyzed and interpreted
and several possible causes of traffic accidents are found and validated by the traffic police. For example
the largest cluster's "financial" feature is prominent
which means the traffic accidents contained in this cluster are related to banks or other financial institutions. The policemen interpret that many people park their car temporarily when visiting financial institutions
and such parking tends to cause collision accidents.
Conclusion
2
POI has spatial-semantic information
and this study utilizes POI to enhance the spatial semantics of traffic accident data. A spatial semantic-enhanced method is presented
and the corresponding visual analytic system is designed and implemented. Analysis of 2018 Hefei traffic accident data reveals several interesting results that are confirmed by traffic policemen. The presented method is useful for discovering the spatial pattern of traffic accidents and beneficial for traffic accident prevention and management. In the future
additional attributes
such as time and density
could be considered
and more sophisticated visual encoding and interaction methods should be studied and applied.
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