This paper discusses some of the difficulties encountered in attempting to introduce ideas derived from research on repeat victimi- zation to the police services of the United Kingdom. Repeat victimiza- tion is the phenomenon in which particular individuals or other targets are repeatedly attacked or subjected to other forms of victimization, including the loss of property. It is argued that repeat victimization is a good example of the kind of problem solving envisaged by Goldstein and discussed in his original conception of problem-oriented policing. The paper first briefly describes the U.K. research program on re- peat victimization and a chronology of the action taken to implant the ideas into the routine of policing. It then pinpoints some of the structural features of policing in England and Wales which facilitated the process described. These features are missing in many other ju- risdictions, which have a fundamentally different policing structure and call for a different approach in introducing research-based evi-

Repeat Victimisation: Lessons for Implementating Problem-Oriented Policing

The law of crime concentration states that half of the cumulative crime in a city will occur within approximately 4% of the city’s geography. The law is demonstrated by counting the number of incidents in each of N spatial areas (street segments or grid cells) and then computing a parameter based on the counts, such as a point estimate on the Lorenz curve or the Gini index. Here we show that estimators commonly used in the literature for these statistics are biased when the number of incidents is low (several thousand or less). Our objective is to significantly reduce bias in estimators for the law of crime concentration. By modeling crime counts as a negative binomial, we show how to compute an improved estimate of the law of crime concentration at low event counts that significantly reduces bias. In particular, we use the Poisson–Gamma representation of the negative binomial and compute the concentration statistic via integrals for the Lorenz curve and Gini index of the inferred continuous Gamma distribution. We illustrate the Poisson–Gamma method with synthetic data along with homicide data from Chicago. We show that our estimator significantly reduces bias and is able to recover the true law of crime concentration with only several hundred events. The Poisson–Gamma method has applications to measuring the concentration of rare events, comparisons of concentration across cities of different sizes, and improving time series estimates of crime concentration.

Reducing Bias in Estimates for the Law of Crime Concentration

In the last decades, the notion that cities are in a state of equilibrium with a centralised organisation has given place to the viewpoint of cities in disequilibrium and organised from bottom to up. In this perspective, cities are evolving systems that exhibit emergent phenomena built from local decisions. While urban evolution promotes the emergence of positive social phenomena such as the formation of innovation hubs and the increase in cultural diversity, it also yields negative phenomena such as increases in criminal activity. Yet, we are still far from understanding the driving mechanisms of these phenomena. In particular, approaches to analyse urban phenomena are limited in scope by neglecting both temporal non-stationarity and spatial heterogeneity. In the case of criminal activity, we know for more than one century that crime peaks during specific times of the year, but the literature still fails to characterise the mobility of crime. Here we develop an approach to describe the spatial, temporal, and periodic variations in urban quantities. With crime data from 12 cities, we characterise how the periodicity of crime varies spatially across the city over time. We confirm one-year criminal cycles and show that this periodicity occurs unevenly across the city. These ‘waves of crime’ keep travelling across the city: while cities have a stable number of regions with a circannual period, the regions exhibit non-stationary series. Our findings support the concept of cities in a constant change, influencing urban phenomena—in agreement with the notion of cities not in equilibrium.

/pdf/spatio-temporal-variations-in-the-urban-rhythm-the-oy2kl0q0hg.pdf

Spatio-temporal variations in the urban rhythm: the travelling waves of crime

Crime concentrations at micro places: A review of the evidence

Mobility between Colombian cities is predominantly repeat and return migration

Generally speaking, crime is, fortunately, a rare event. As far as modelling is concerned, this sparsity of data means that traditional measures to quantify concentration are not appropriate when applied to crime suffered by a population. Our objective is to develop a new technique to measure the concentration of crime which takes into account its low frequency of occurrence and its high degree of concentration in such a way that this measure is comparable over time and over different populations. This article derives an estimate of the distribution of crime suffered by a population based on a mixture model and then evaluates a new and standardised measurement of the concentration of the rates of suffering a crime based on that distribution. The new measure is successfully applied to the incidence of robbery of a person in Mexico and is able to correctly quantify the concentration crime in such a way that is comparable between different regions and can be tracked over different time periods. The risk of suffering a crime is not uniformly distributed across a population. There are certain groups which are statistically immune to suffering crime but there are also groups which suffer chronic victimisation. This measure improves our understanding of how patterns of crime can be quantified allowing us to determine if a prevention policy results in a crime reduction rather than target displacement. The method may have applications beyond crime science.

/pdf/measuring-the-distribution-of-crime-and-its-concentration-2b6e4kni29.pdf

Measuring the Distribution of Crime and Its Concentration

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Measuring the Distribution of Crime and Its Concentration