To overcome the low accuracy of visual perception caused by the small sample size and spatial heterogeneity of urban flood control data resulting from the use of rainfall gauge sensors, an urban flood visual framework based on a spatial grid was proposed. The framework is an aggregation framework composed of multiple submodels and algorithms. A three-level urban flood control grid based on the territorial management business model was designed for a local administrative bureau. A grid-constrained point data spatial clustering algorithm based on this grid division was proposed to solve the statistical bias problem. An algorithm that increases the number of samples was developed to support the adaptive covering heat map generation. The new algorithm can provide dense sensing information with only a small number of sensors. This framework was tested by an urban flood control business. The results demonstrate that the visual models and algorithms included in this framework eliminate the effects of spatial heterogeneity, solve the statistical bias problem, and improve the visual perception accuracy. The visualization framework is expected to be very helpful for the emergency response and decision making in urban flood control, and can also be applied to other fields such as water conservation and urban management.