Estimating Crime in Place: Moving Beyond Residence Location

Cernat, Alexandru; Buil‐Gil, David; Brunton‐Smith, Ian; Pina-Sánchez, José; Murrià-Sangenís, Marta

doi:10.1177/00111287211064779

Cited by 5 publications

(5 citation statements)

References 40 publications

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“…Second, regarding the construction of our second dataset, we made the strong assumption that the victimization survey measures crimes and under-reporting where crimes actually happen as opposed to the places where people reside. As pointed out by [ 15 ], survey-based offense location of crimes is a better approximation to police records. Unfortunately, we do not have a survey that asks for the offense location, but rather, we assume that the offense took place at least in the same jurisdiction where the person resides.…”

Section: Discussionmentioning

confidence: 99%

“…Inconsistencies in police practices may add random measurement errors to official crime statistics [ 15 ], but heterogeneous systematic negative counts in the form of underreporting affect the conclusions that can be drawn from the data. Such underreporting can be caused by a variety of factors, as documented by [ 16 ].…”

Section: Introductionmentioning

confidence: 99%

“…In addition, in citizen security planning, which requires time-varying and trustable reports of crime incidences for resource allocation [2,11,12], heterogeneous dark crime figures in different geographic areas may also result in the misallocation of police resources, hampering short-time planning as demonstrated by [9,13]. These geographic variations result from complex victim underreporting dynamics and inconsistencies in recording practices across different jurisdictions [14,15].…”

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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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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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“…For reasons of convenience, when similar comparisons are undertaken in the literature, they are often based on the assumption that the benchmark measures against which police recorded crime rates are compared can be considered a 'gold standard', free of measurement error. However, we know that both victimisation surveys and vital statistics are affected by different limitations, such as sampling error or the fact that both measures reflect the location of the victim's residence rather than where the incident took place (Cernat et al, 2021). As a result, we can deduce that the extent of measurement error attributed to police data in the literature has likely been exaggerated, but how much so is currently unclear.…”

Section: Caveats and Future Avenues Of Researchmentioning

confidence: 99%

The Impact of Measurement Error in Regression Models Using Police Recorded Crime Rates

et al. 2022

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Objectives: Assess the extent to which measurement error in police recorded crime rates impact the estimates of regression models exploring the causes and consequences of crime. Methods:We focus on linear models where crime rates are included either as the response or as an explanatory variable, in their original scale or log-transformed. Two measurement error mechanisms are considered, systematic errors in the form of under-recorded crime, and random errors in the form of recording inconsistencies across areas. The extent to which such measurement error mechanisms impact model parameters is demonstrated algebraically using formal notation, and graphically using simulations. Results:The impact of measurement error is highly variable across different settings. Depending on the crime type, the spatial resolution, but also where and how police recorded crime rates are introduced in the model, the measurement error induced biases could range from negligible to severe, affecting even estimates from explanatory variables free of measurement error. We also demonstrate how in models where crime rates are introduced as the response variable, the impact of measurement error could be eliminated using log-transformations. Conclusions:The validity of a large share of the evidence base exploring the effects and consequences of crime is put into question. In interpreting findings from the literature relying on regression models and police recorded crime rates, we urge researchers to consider the biasing effects shown here. Future studies should also anticipate the impact in their findings and employ sensitivity analysis if the expected measurement error induced bias is non-negligible.

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“…Furthermore, if deriving estimates of the prevalence of random errors in police recorded crime rates from victimisation surveys, it will need to be considered that such estimates are likely to be overestimated. This is because victimisation surveys are also affected by measurement error (Cernat et al, 2022;Levine, 1976;Skogan, 1981) arising from different problems such as acquiescence bias, memory failures, and sampling error (Lohr, 2019;Schneider, 1981).…”

Section: Rcme: Recounting Crime With Measurement Errormentioning

confidence: 99%

Exploring the Impact of Measurement Error in Police Recorded Crime Rates through Sensitivity Analysis

Pina-Sánchez¹,

Brunton‐Smith²,

Buil‐Gil³

et al. 2022

Preprint

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It has been long known that police recorded crime data is susceptible to substantial measurement error. However, despite its limitations, police data is widely used in regression models exploring the causes and effects of crime. Furthermore, because of the complex error mechanisms affecting police data, attempts to adjust for their impact are rare and tailored to specific settings (crime types, measurement models, outcome models, and precursors or consequences of crime). Here we introduce rcme: Recounting Crime with Measurement error, a new R package to enable sensitivity assessments of the impact of measurement error in analyses using police recorded crime rates across a wide range of settings. Using two real world examples – i) the link from violent crime to disorder, and ii) the role of collective efficacy in mitigating criminal damage – we demonstrate how rcme can be used to summarise the impacts of measurement error in empirical models used in research and practice.

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Estimating Crime in Place: Moving Beyond Residence Location

Cited by 5 publications

References 40 publications

Modelling underreported spatio-temporal crime events

Modelling underreported spatio-temporal crime events

The Impact of Measurement Error in Regression Models Using Police Recorded Crime Rates

Exploring the Impact of Measurement Error in Police Recorded Crime Rates through Sensitivity Analysis

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