Objectives  With the acceleration of urbanization, problems caused by extreme weather such as heavy rain have become increasingly obvious. As one of the most common meteorological disasters, urban floods have become one of the urban diseases in today's big cities. Therefore, it is necessary to explore the methods and technologies of multi-source spatiotemporal data integration to achieve dynamic road waterlogging visualization.

  Methods  Firstly, we designed a spatiotemporal data model of road waterlogging and stored the data in the form of database tables to describe the evolution process of road waterlogging in both time and space. Secondly, a matching method for waterlogging data and road data was put forward to obtain road waterlogging depth. Thirdly, we designed a prototype system for dynamic road waterlogging warnings and achieved the extraction and release of road waterlogging depth. Finally, an experiment was carried out in downtown Nanjing City to verify the availability of the proposed method.

  Results  The results show that the proposed data integration method can meet the multi-scale needs of dynamic road waterlogging visualization. We integrate multi-source spatiotemporal data from the perspective of dynamic road waterlogging visualization to solve the problem of transferring multi-source data into road waterlogging depth.

  Conclusions  In further studies, real-time traffic will be considered to comprehensively express the road condition and waterlogging condition.