Multimodal prediction of aggressive behavior occurrence using a decision-level approach

Reyes, Ana María Muñoz; Rudas, Jorge; Pulido, Cristian; Chaparro, Luisa Fernanda; Victorino, Jorge; Narvaez, Luz Angela; Martínez, Darwin; Gómez, Francisco

doi:10.1049/icp.2021.1463

Cited by 1 publication

(3 citation statements)

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“…Underreporting is present not only in the spatial dimension but also in the temporal dimension [42]. Previous works on crime underreporting focused on constructing average crime rate estimations for long-term windows [6,9,17,18,27]. Most of these works aim to quantify the spatial underreporting of official crime registers, assuming the citizen's surveys on victimization as the crime ground truth.…”

Section: Discussionmentioning

confidence: 99%

“…Although these observations still suffer from the dark figure of crime [8], together they may potentially provide valuable information for complementing official records. Recently, different works explored data integration/fusion approaches to provide more information about crime from multiple crime observation sources [27], particularly by spatially combining estimations of crime from different data sources, such as official crime reports, calls to the emergency line related to crime, and citizen's contraventions [27]. Nevertheless, these approaches are limited in uncovering crime underreporting over time (online) because there are no mechanisms for integrating partial observations arriving online into previous crime observation data.…”

Section: Introductionmentioning

confidence: 99%

“…The main objective of this work was to study the identification over time of the "true" unknown crime incidence rates based on official reports of crime incidents and the "true" underreporting rates based on complementary information, particularly crime-related data acquired gradually over time. In contrast with previous works aimed to describe underreporting in long-time scales by exploiting victimization surveys [6,9,19], official crime data [17,18,22], or combining multiple crime data sources [27], this work aims to integrate additional incremental evidence about crime once is available, allowing to gain knowledge about the crime phenomena gradually, instead of forcing to wait for final integration. The proposed online estimation relies on a new crime underreporting combinatorial multi-armed bandit model [28] aimed to elicit the "true" average incidence rates and estimate the underreporting rates for different spatial units over time.…”

Section: Introductionmentioning

confidence: 99%

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Modelling underreported spatio-temporal crime events

Riascos

Ñungo

Tobón³

et al. 2023

PLoS ONE

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Crime observations are one of the principal inputs used by governments for designing citizens’ security strategies. However, crime measurements are obscured by underreporting biases, resulting in the so-called “dark figure of crime”. This work studies the possibility of recovering “true” crime and underreported incident rates over time using sequentially available daily data. For this, a novel underreporting model of spatiotemporal events based on the combinatorial multi-armed bandit framework was proposed. Through extensive simulations, the proposed methodology was validated for identifying the fundamental parameters of the proposed model: the “true” rates of incidence and underreporting of events. Once the proposed model was validated, crime data from a large city, Bogotá (Colombia), was used to estimate the “true” crime and underreporting rates. Our results suggest that this methodology could be used to rapidly estimate the underreporting rates of spatiotemporal events, which is a critical problem in public policy design.

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