Research On Spatial-Temporal Crime Prediction Technology Based On Adaptive Graph Learning

Crime prediction is a crucial area of computational sociology and is frequently employed in law enforcement as an applied security measure.Crime prediction involves utilizing spatio-temporal data mining techniques to analyze and model past crime events,revealing underlying crime patterns and rules,and making informed predictions about the future spatio-temporal distribution of crimes.Accurate crime prediction has significant implications for the development of security prevention and control systems,distribution of police resources.organization of local police work,and security operations for significant events.Although there have been many achievements in current domestic and foreign research efforts,several challenges still persist:(1)Spatial modeling relies heavily on pre-defined graphs constructed from external data.(2)Temporal modeling based on recurrent neural networks often suffers from poor accuracy and low training efficiency;(3)There is a lack of a multi-view spatial fusion strategy that can effectively fuse external data with an adaptive graph for modeling;(4)There is also a lack of a multi-view temporal fusion strategy that can model the cumulative impact of crime events with different historical timespans on the occurrence of future crime events.This thesis proposes two solutions to address the aforementioned challenges.Solution 1:This paper proposes the Spatial-Temporal Attention Network for Crime Prediction with Adaptive Graph Learning(AGL-STAN)to address challenges(1)and(2).The adaptive graph learning module is utilized to tackle challenge(1),while the time-aware self-attention module is designed to solve challenge(2).Firstly,an adaptive learning process is implemented by constructing a spatial relationship graph using a randomly initialized learnable community representation matrix.Secondly,the input crime history data is spatially modeled with fixed time windows based on this graph to learn the deep hidden level region representation matrix.Then,a self-attentive mechanism is introduced to parallelize the deep level region representation matrix at each time point,and the resulting processed data is fed into a prediction layer for dimensionality transformation.Finally,the optimized objective function is combined with a downstream task to supervise the model and learn a more realistic crime spatial correlation graph,thereby achieving efficiency optimization and improving prediction accuracy.Experiments are conducted on real datasets to validate the performance of AGLSTAN.The results show a 4.97%and 5.14%improvement in Macro-F1 and Micro-F1 metrics,respectively,compared to existing models,and also demonstrate a significant improvement in training efficiency.Solution 2:For challenges(3)and(4),a Multi-View Spatial-Temporal Attention Network for Crime Prediction with Adaptive Graph Learning(MAGL-STAN)is proposed.The model employs a multi-view strategy to optimize the two modules in Solution 1,namely the multi-view adaptive graph learning module and the multi-view time-aware self-attention module.These modules are designed to address challenges(3)and(4)respectively.Firstly,the pre-defined and learned external graphs are fused with the adaptive graphs in a multi-view strategy to learn more realistic spatial relationships of crimes.Secondly,multi-view feature extraction is performed for crime occurrence sequences to better understand the cumulative impact of crime data in the evolution process.Then,metadata that may be strongly correlated with crime is introduced for fusion modeling.Finally,a joint optimization objective function with constraints is used to stabilize the training,resulting in an impressive improvement in prediction accuracy.The results show that MAGL-STAN has a relative improvement of 4.47%and 3.86%over the AGL-STAN model in the Macro-F1 and Micro-F1 metric.