Comparing Measurements of Crime in Local Communities: A Case Study in Islington, London
Police-recorded crime data are prone to measurement error, affecting our understanding of the prevalence and distribution of crime. A growing body of research has responded to this problem using data from crime surveys and health emergency services. However, these data sources are not error-free. In this study we use violent crime data recorded by the police, ambulance services, two crime surveys and computer simulations in Islington, London. We illustrate how different data sources affect our understanding of the distribution and causes of crime. Different data sources show remarkably different results. However, estimates of crime from different sources become more similar when crime rates are calculated using workday population as the denominator and log-transformed. By comparing and combining multiple crime data sources we can provide a more accurate description of the prevalence, distribution and causes of crime. We propose using multiple data sources to study crime in local areas.
Estimating Crime in Place: Moving beyond Residence Location
We assess if asking victims about the places where crimes happen leads to estimates of ‘crime in place’ with better measurement properties. We analyse data from the Barcelona Victimization Survey (2015 to 2020) aggregated in 73 neighbourhoods using longitudinal quasi-simplex models and criterion validity to estimate the quality of four types of survey-based measures of crime. The distribution of survey-based offence location estimates, as opposed to victim residence estimates, is highly similar to police-recorded crime statistics, and there is little trade off in terms of the reliability and validity of offence location and victim residence measures. Estimates of crimes reported to the police show a better validity, but their reliability is lower and capture fewer crimes.
The Impact of Measurement Error in Regression Models Using Police Recorded Crime Rates
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.