The pressing need to improve traffic safety has become a societal concern in many cities around the world. Many traffic accidents are not occurring as stand-alone events but as consequences of other road incidents and hazards. To capture the traffic safety indications from a holistic aspect, this paper presents a suite of visualization techniques to explore large traffic safety datasets collected from different sources using adaptive study areas which include the whole region (Hamilton County, Ohio, U.S.) as well as smaller sub-areas. In the present study, these data source include (1) Hamilton County’s 911 emergency response data, which includes traffic incidents as well as other types of incidents throughout the county, and (2) Tennessee crash data, which contains only vehicle crashes with more detail on the circumstances of each crash. Both abstract and spatial visualization techniques are used to derive a better understanding of traffic safety patterns for different traffic participants in various urban environments. In addition to the entire region of Hamilton County, safety is examined on the highways, in the downtown area, and in a shopping district east of the city center. It is possible to characterize incidents in the different areas, gain a better understanding of common incident patterns, and identify outliers in the data. Finally, a textured tile calendar is presented to compare spatiotemporal patterns.

改善交通安全的紧迫需求已成为世界许多城市的社会关注点。许多交通事故不是作为独立的事件发生，而是作为其他道路事故和危害的后果而发生的。为了从整体角度捕获交通安全指示，本文提出了一套可视化技术，以利用自适应研究区域（包括美国整个地区（汉密尔顿县，美国俄亥俄州）以及较小区域）探索从不同来源收集的大型交通安全数据集子区域。在本研究中，这些数据源包括（1）汉密尔顿县的911紧急响应数据，其中包括交通事故以及全县范围内的其他类型的事故；以及（2）田纳西州撞车数据，其中仅包含更详细的撞车事件在每次崩溃的情况下。抽象和空间可视化技术都可以用来更好地理解各种城市环境中不同交通参与者的交通安全模式。除汉密尔顿县的整个地区外，还对高速公路，市中心和市中心以东的购物区进行安全检查。可以表征不同区域中的事件，更好地了解常见事件模式，并识别数据中的异常值。最后，提出了一个纹理瓷砖日历，以比较时空模式。在市中心和市中心以东的购物区中。可以表征不同区域中的事件，更好地了解常见事件模式，并识别数据中的异常值。最后，提出了一个纹理瓷砖日历，以比较时空模式。在市中心和市中心以东的购物区中。可以表征不同区域中的事件，更好地了解常见事件模式，并识别数据中的异常值。最后，提出了一个纹理瓷砖日历，以比较时空模式。