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Modelling underreported spatio-temporal crime events.

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This study introduces a new model to estimate true crime rates and underreporting, addressing the "dark figure of crime". The method uses daily data to improve public security strategies.

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Area of Science:

  • Criminology
  • Data Science
  • Public Policy

Background:

  • Crime statistics are crucial for public security strategies but are affected by underreporting, creating a 'dark figure of crime'.
  • Accurate crime incidence and underreporting rates are essential for effective policy design.

Purpose of the Study:

  • To develop and validate a novel methodology for estimating true crime incidence and underreporting rates over time.
  • To address the challenge of the 'dark figure of crime' using sequentially available daily data.

Main Methods:

  • A novel spatiotemporal event underreporting model based on the combinatorial multi-armed bandit framework was proposed.
  • The model's parameters, including true incidence and underreporting rates, were validated through extensive simulations.
  • The methodology was applied to real-world crime data from Bogotá, Colombia.

Main Results:

  • The proposed methodology successfully identified fundamental model parameters in simulations.
  • Estimated true crime and underreporting rates were derived from Bogotá's crime data.
  • The study demonstrated the potential for rapid estimation of underreporting rates for spatiotemporal events.

Conclusions:

  • The developed methodology offers a promising approach to accurately estimate crime underreporting.
  • This technique can significantly aid public policy design by providing more reliable crime data.
  • Addressing the 'dark figure of crime' is critical for enhancing citizen security.