Abstract
This work presents research based on evidence with neural networks for the development of predictive crime models, finding the data sets used are focused on historical crime data, crime classification, types of theft at different scales of space and time, counting crime and conflict points in urban areas. Among some results, 81% precision is observed in the prediction of the Neural Network algorithm and ranges in the prediction of crime occurrence at a space-time point between 75% and 90% using LSTM (Long-ShortSpace-Time). It is also observed in this review, that in the field of justice, systems based on intelligent technologies have been incorporated, to carry out activities such as legal advice, prediction and decisionmaking, national and international cooperation in the fight against crime, police and intelligence services, control systems with facial recognition, search and processing of legal information, predictive surveillance, the definition of criminal models under the criteria of criminal records, history of incidents in different regions of the city, location of the police force, established businesses, etc., that is, they make predictions in the urban context of public security and justice. Finally, the ethical considerations and principles related to predictive developments based on artificial intelligence are presented, which seek to guarantee aspects such as privacy, privacy and the impartiality of the algorithms, as well as avoid the processing of data under biases or distinctions. Therefore, it is concluded that the scenario for the development, research, and operation of predictive crime solutions with neural networks and artificial intelligence in urban contexts, is viable and necessary in Mexico, representing an innovative and effective alternative that contributes to the attention of insecurity, since according to the indices of intentional homicides, the crime rates of organized crime and violence with firearms, according to statistics from INEGI, the Global Peace Index and the Government of Mexico, remain in increase.
Highlights
Crime in Mexico has reached alarming figures, intentional homicides, violence with firearms, drug dealing, among others, do not diminish, despite the strategies and efforts of the government to combat crime
This section describes the result of the tests with neural network models such as Feed-Forward Network, Recurrent Neural Network (RNN), Convolutional Neural Network (CNN) under the same training methods and conditions affecting their performance with data from the cities of Chicago and Portland
Through the review and analysis of the research cited in this document, the efficacy of artificial intelligence algorithms and neural networks with supervised or unsupervised learning has been shown for the prediction of crime through investigations under specific urban contexts
Summary
Crime in Mexico has reached alarming figures, intentional homicides, violence with firearms, drug dealing, among others, do not diminish, despite the strategies and efforts of the government to combat crime. Due to the aforementioned problems, that occur in other countries of the world,it is necessary to review models that, from technology, have contributed to the improvement of the operational intelligence of those responsible for guaranteeing the safety of cities, whether in police forces, in intelligence units or in urban space surveillance tasks. Derived from the review of the accuracy these technologies have, it is necessary to investigate and test them in several contexts, to generate local predictive models that contribute to crime prevention, either in institutional security and justice or from the perspective of citizen prevention who transits in public spaces, where the crime mostly affect
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