Measuring the Distribution of Crime and Its Concentration

Curiel, Rafael Prieto; Delmar, Sofía Collignon; Bishop, Steven R.

doi:10.1007/s10940-017-9354-9

Cited by 32 publications

(13 citation statements)

References 49 publications

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“…The objective of this work is to present the Rare Event Concentration Coefficient ( RECC ), a metric specifically designed for the analysis of low-frequency and highly concentrated data [ 23 ] which is comparable between different cities, types of accidents, time periods and times of a day. This tool has been successfully applied in the case of crime concentration [ 24 ] and now provides a starting point for the analysis of the concentration of road accidents.…”

Section: Introductionmentioning

confidence: 99%

A novel rare event approach to measure the randomness and concentration of road accidents

2018

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BackgroundRoad accidents are one of the main causes of death around the world and yet, from a time-space perspective, they are a rare event. To help us prevent accidents, a metric to determine the level of concentration of road accidents in a city could aid us to determine whether most of the accidents are constrained in a small number of places (hence, the environment plays a leading role) or whether accidents are dispersed over a city as a whole (hence, the driver has the biggest influence).MethodsHere, we apply a new metric, the Rare Event Concentration Coefficient (RECC), to measure the concentration of road accidents based on a mixture model applied to the counts of road accidents over a discretised space. A test application of a tessellation of the space and mixture model is shown using two types of road accident data: an urban environment recorded in London between 2005 and 2014 and a motorway environment recorded in Mexico between 2015 and 2016.FindingsIn terms of their concentration, about 5% of the road junctions are the site of 50% of the accidents while around 80% of the road junctions expect close to zero accidents. Accidents which occur in regions with a high accident rate can be considered to have a strong component related to the environment and therefore changes, such as a road intervention or a change in the speed limit, might be introduced and their impact measured by changes to the RECC metric. This new procedure helps us identify regions with a high accident rate and determine whether the observed number of road accidents at a road junction has decreased over time and hence track structural changes in the road accident settings.

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Section: Introductionmentioning

confidence: 99%

A novel rare event approach to measure the randomness and concentration of road accidents

2018

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“…We described regularities in both city-level and local-level dynamics of crime, providing statistical characteristics for crime modelling. Though the proposal of a generative mechanism is beyond the scope of this paper, our work brings a new piece to the puzzle, alongside with other regularities in crime such as scaling 9 and concentration 19,72 . Further investigations to understand the emergence of the temporal regularities might examine the specific cases of Santa Monica and Seattle (i.e., cities that fail to exhibit annual regularity) and also examine different spatial units of aggregation such as dynamic ones.…”

Section: Discussionmentioning

confidence: 99%

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

Oliveira¹,

Ribeiro²,

Bastos-Filho³

et al. 2018

EPJ Data Sci.

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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. 4/11

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“…Street segments and intersections, their spatial units of analysis (18,445), exceeded the number of crime incidents, with the exception of theft, theft from vehicle, and burglary within beginning years of data. In another study using Vancouver data from years 1991, 1996, and 2001, Andresen and Malleson [3] estimate the spatial concentration of assault, burglary, robbery, sexual assault, theft, theft from vehicle, and motor vehicle theft across census tracts (110), dissemination areas (1,011), and street segments (11,730). Their reported crime counts indicate that assaults (in 1996 and 2001), theft (in 2001), and for all years robbery, sexual assault, and theft of vehicle all had lower frequencies than the total number of street segments.…”

Section: Need For Improved Concentration Estimation Methods For Low Ementioning

confidence: 99%

“…Recent attempts have been made to address this problem. Curiel et al [11] propose a method for addressing the small sample size problem by modeling the rate of crime as a function of population in each spatial unit. However, population estimates are often not available at the street segment and grid cell level (where the law of crime concentration is typically measured).…”

Section: Commonly Used Estimators For the Law Of Crime Concentrationmentioning

confidence: 99%

Reducing Bias in Estimates for the Law of Crime Concentration

et al. 2019

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Objectives. The law of crime concentration states that half of the cumulative crime in a city will occur within approximately 4 percent 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. Methods. 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. Results. 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. Conclusions. 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.

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Measuring the Distribution of Crime and Its Concentration

Cited by 32 publications

References 49 publications

A novel rare event approach to measure the randomness and concentration of road accidents

A novel rare event approach to measure the randomness and concentration of road accidents

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

Reducing Bias in Estimates for the Law of Crime Concentration

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