Comparative Analysis of The Combined Model (Spatial and Temporal) and Regression Models for Predicting Murder Crime

This research dealt with the analysis of murder crime data in Iraq in its temporal and spatial dimensions, then it focused on building a new model with an algorithm that combines the characteristics associated with time and spatial series so that this model can predict more accurately than other models by comparing them with this model, which we called the Combined Regression model (CR), which consists of merging two models, the time series regression model with the spatial regression model, and making them one model that can analyze data in its temporal and spatial dimensions. Several models were used for comparison with the integrated model, namely Multiple Linear Regression (MLR), Decision Tree Regression (DTR), Random Forest Regression (RFR) and Neural Network Regression (NNR). The data used is about the monthly numbers of murder crimes for the police directorates in Baghdad and the governorates during the period from January 2015 to June 2023. The data was analyzed and then divided into two sets, a training and testing set, to perform these models in prediction. The accuracy of each modsl’s performance was evaluated using two statistical measures: RMSE and in order to determine the best and most accurate performing model among the selected models. An important result was obtained in the comparison between these models, as the combined model obtained the most accurate performance than the other models, based on the values ​​of the performance accuracy metrics for each model in relation to the data used in the murder crimes.