Risk prediction with AI to optimise resource allocation

Program aims

• Predict high-risk crime areas using historical data and socio-economic, climatic and temporal variables.
• Optimising patrol resources through a data-driven approach.
• Analysing crime patterns to better understand the dynamics of crime in Montevideo.

Development

The project has involved the following phases:

• Collection of historical data and different climatic and socio-economic variables.
• Data cleaning and preparation, adjusting variables and dealing with missing values.
• Exploratory data analysis, identifying different crime patterns, analysing correlations and detecting outliers.
• Feature engineering, analysing the importance of different variables, encoding categorical variables and creating new variables.
• Predictive modelling, training the different supervised models, optimising the hyperparameters and carrying out an evaluation with performance metrics.
• Evaluation of the model, studying the ROC curve and overfitting, optimising the classification threshold and evaluating the metrics.

Conclusions

The rise of artificial intelligence has focused on its generative characteristics, but it can also be used to improve society. AI- and ML-supported crime analysis in areas critical to public safety facilitates the optimisation of police resources, allocating them where they are most needed, as well as supporting urban planning and crime prevention of all kinds.