Crime Type Prediction based on Various Occurrence using Parallel LSTM

Abstract

Crime is a widespread societal issue that has a negative impact on people's standard of living and the nation's prosperity. It's a major consideration for potential residents and tourists alike when deciding whether or not to settle in a given area. As crime rates rise, police departments have a growing need for cutting-edge GIS and data mining tools to enhance crime analytics and strengthen public safety. The suggested method includes preprocessing, feature selection, and evaluating the model's performance. We begin by cleaning up the raw crime statistics. For more predictable signals, this comprises both spatial and temporal regularization. Feature selection is performed using a rough spanning tree. To measure the effectiveness of the model, we employ parallel LSTM. When compared to two established approaches, the new strategy fares quite well.