Spatio-Temporal Visualization Method for Urban Waterlogging Warning Based on Dynamic Grading.

With the acceleration of the urbanization process, the problems caused by extremeweather 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 andwaterlogging, resulting in the decline of its traffic capacity. Rainfall is a continuous process in aspace–time dimension, and as rainfall data are obtained through discrete monitoring stations, theacquired rainfall data have discrete characteristics of time interval and space. In order to facilitateusers in understanding the impact of urban waterlogging on traffic, the visualization ofwaterlogging information needs to be displayed under different spatial and temporal granularity.Therefore, the appropriateness of the visualization granularity directly affects the user’s cognitionof the road waterlogging map. To solve this problem, this paper established a spatial granularityand temporal granularity computing quantitative model for spatio-temporal visualization of roadwaterlogging and the evaluation method of the model was based on the cognition experiment. Theminimum visualization unit of the road section is 50 m and we proposed a 5-level depth gradingmethod 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 adynamic spatio-temporal visualization of the waterlogging process in the main urban area ofNanjing, China. The user cognition experiment showed that most participants thought that thesegmentation of road was helpful to the local visual expression of waterlogging, and the colorschemes of waterlogging depth were also helpful to display the road waterlogging informationmore effectively.