{"id":3379,"date":"2023-08-31T10:59:22","date_gmt":"2023-08-31T10:59:22","guid":{"rendered":"https:\/\/neuraldesigner.com\/blog\/crime-prevention\/"},"modified":"2025-09-29T16:30:58","modified_gmt":"2025-09-29T14:30:58","slug":"crime-prevention","status":"publish","type":"blog","link":"https:\/\/www.neuraldesigner.com\/blog\/crime-prevention\/","title":{"rendered":"Prevent urban crime using machine learning"},"content":{"rendered":"

The volume, variety, and velocity of information stored in security and crime prevention institutions have increased significantly.<\/p>\n

The intelligent analysis of all that data can substantially help those security forces adopt these techniques.<\/p>\n

This study applies predictive analytics in law enforcement and crime.<\/p>\n

Specifically, we develop a neural network to identify patterns of criminal behavior based on location and time variables.<\/p>\n

This predictive model will allow security forces to optimize their resources and prevent crimes before they happen.<\/p>\n

The developed algorithm can predict crime categories at a given date, time, and district.<\/p>\n

Moreover, we can plot the predictions as heat maps, which allows the development of new innovative city systems that could help in the fight against crime.<\/p>\n

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Contents:<\/h3>\n
    \n
  1. Introduction<\/a>.<\/li>\n
  2. Application type<\/a>.<\/li>\n
  3. Data set<\/a>.<\/li>\n
  4. Neural network<\/a>.<\/li>\n
  5. Training strategy<\/a>.<\/li>\n
  6. Testing analysis<\/a>.<\/li>\n
  7. Model deployment<\/a>.<\/li>\n<\/ol>\n<\/section>\n
    \n

    1. Introduction<\/h2>\n

    According to classical theories of criminality, recent literature has focused on the impact of socioeconomic and demographic variables on various types of crime.<\/p>\n

    To date, the development of efficiency indicators has helped governments improve their efficiency.<\/p>\n

    Additionally, it has facilitated police work in crime prevention, criminal investigation, apprehension, maintaining order, and providing citizen services.<\/p>\n

    This work incorporates new methodologies based on machine learning and neural networks into the aforementioned traditional concepts.<\/p>\n

    These new methods will enhance the allocation of resources to police departments worldwide in their efforts to combat crime.<\/p>\n<\/section>\n

    \n

    2. Application type<\/h2>\n

    The variable to be predicted is the number of crimes (a count variable).\u00a0Therefore, this is an approximation<\/a> project.<\/p>\n

    This work aims to demonstrate the ability of neural networks to predict criminal activity in the City and County of San Francisco, based on spatial and temporal variables.<\/p>\n

    We will explore how a predictive model can enhance the efficiency of a city’s security forces in resource allocation. We also include crime predictions for a given day at different times.<\/p>\n<\/section>\n

    \n

    3. Data set<\/h2>\n

    The original\u00a0dataset<\/a> contains incidents derived from the San Francisco Police Department’s (SFPD<\/span>) Crime Incident Reporting System.<\/p>\n

    The data ranges from 01\/01\/2003 to 05\/13\/2015. In particular, it includes 878,049 incident reports with the following variables: day, category, description, weekday, police district, resolution, address, X-coordinate, and Y-coordinate.<\/p>\n

    The first step is to prepare the data set, which is the source of information for the approximation problem. The original dataset is unsuitable for building a criminality model, so it underwent detailed preprocessing.<\/p>\n

    We have taken into account two types of variables:<\/p>\n