An Adaptive Spatial Resolution Method Based on the ST-ResNet Model for Hourly Property Crime Prediction

Effective predictive policing can guide police patrols and deter crime. Hourly crime prediction is expected to save police time. The selection of spatial resolution is important due to its strong relationship with the accuracy of crime prediction. In this paper, we propose an adaptive spatial resolution method to select the best spatial resolution for hourly crime prediction. The ST-ResNet model is applied to predict crime risk, with historical crime data and weather data as predictive variables. A predictive accuracy index (PAI) is used to evaluate the accuracy of the results. Data on property crimes committed in Suzhou, a big city in China, were selected as the research data. The experiment results indicate that a 2.4 km spatial resolution leads to the best performance for crime prediction. The adaptive spatial resolution method can be used to guide police deployment.

Visualization of Flooding Using Adaptive Spatial Resolution

Flood simulations are vital to gain insight into possible dangers and damages for effective emergency planning. With flexible and natural ways of visualizing water flow, more precise evaluation of the study area is achieved. In this study, we describe a method for flood visualization using both regular and adaptive grids for position-based fluids method to visualize the depth of water in the study area. The mapping engine utilizes adaptive cell sizes to represent the study area and utilizes Jenks natural breaks method to classify the data. Predefined single-hue and multi-hue color sets are used to generate a heat map of the study area. It is shown that the dynamic representation benefits the mapping engine through enhanced precision when the study area has non-disperse clusters. Moreover, it is shown that, through decreasing precision, and utilizing an adaptive grid approach, the simulation runs more efficiently when particle interaction is computationally expensive.