With the acceleration of the urbanization process, the problems caused by extreme
weather such as heavy rainstorm events have become more and more serious. During such events,
the road and its auxiliary facilities may be damaged in the process of the rainstorm and
waterlogging, resulting in the decline of its traffic capacity. Rainfall is a continuous process in a
space–time dimension, and as rainfall data are obtained through discrete monitoring stations, the
acquired rainfall data have discrete characteristics of time interval and space. In order to facilitate
users in understanding the impact of urban waterlogging on traffic, the visualization of
waterlogging information needs to be displayed under different spatial and temporal granularity.
Therefore, the appropriateness of the visualization granularity directly affects the user’s cognition
of the road waterlogging map. To solve this problem, this paper established a spatial granularity
and temporal granularity computing quantitative model for spatio-temporal visualization of road
waterlogging and the evaluation method of the model was based on the cognition experiment. The
minimum visualization unit of the road section is 50 m and we proposed a 5-level depth grading
method and two color schemes for road waterlogging visualization based on the user’s cognition.
To verify the feasibility of the method, we developed a prototype system and implemented a
dynamic spatio-temporal visualization of the waterlogging process in the main urban area of
Nanjing, China. The user cognition experiment showed that most participants thought that the
segmentation of road was helpful to the local visual expression of waterlogging, and the color
schemes of waterlogging depth were also helpful to display the road waterlogging information
more effectively.



中文翻译：

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基于动态评分的城市涝灾时空可视化方法

随着城市化进程的加快，由极端
天气引起的问题，例如暴雨事件变得越来越严重。在此类事件中，
道路及其辅助设施可能在暴雨和
涝灾过程中受损，从而导致其通行能力下降。降雨是一个
时空连续过程，随着通过离散监测站
获得降雨数据，所获得的降雨数据具有时间间隔和空间的离散特征。为了帮助
用户了解城市涝灾对交通的影响，
需要以不同的时空粒度显示涝灾信息的可视化。
因此，可视化粒度的适当性直接影响用户
对道路涝灾地图的认知。为解决这一问题，本文建立了
道路
涝灾时空可视化的空间粒度和时间粒度计算定量模型，并基于认知实验对该模型进行了评价。该
路段的最小可视化单位为50 m，我们
基于用户的认知，提出了5级深度分级方法和两种颜色方案，用于道路涝灾可视化。
为了验证该方法的可行性，我们开发了原型系统并实施了

南京主城区内涝过程的动态时空可视化 用户认知实验表明，大多数参与者认为
道路分割有助于提高涝渍的局部视觉表达，而且
淹渍深度的配色方案也有助于
更有效地显示道路涝渍信息。