From Criminal Spheres of Familiarity to Crime Networks

Oliveira, Marcos; Barbosa, Hugo; Yehle, T.; White, Sarah; Menezes, Ronaldo

doi:10.1007/978-3-319-16112-9_22

Cited by 7 publications

(6 citation statements)

References 25 publications

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“…For instance, its dynamics have been recently studied using the framework of multiplex networks [32,33] considering different social behaviors [34][35][36][37][38]. It allows to understand phenomena recorded in huge social network datasets [39][40][41]. Opinion dynamics allows to analyze and to model the spreading of ideas, opinions, and feelings by reducing the study of complex social scenarios to the analysis of few variables [42].…”

Section: Introductionmentioning

confidence: 99%

Modeling Radicalization Phenomena in Heterogeneous Populations

Galam

Javarone

2016

PLoS ONE

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The phenomenon of radicalization is investigated within a mixed population composed of core and sensitive subpopulations. The latest includes first to third generation immigrants. Respective ways of life may be partially incompatible. In case of a conflict core agents behave as inflexible about the issue. In contrast, sensitive agents can decide either to live peacefully adjusting their way of life to the core one, or to oppose it with eventually joining violent activities. The interplay dynamics between peaceful and opponent sensitive agents is driven by pairwise interactions. These interactions occur both within the sensitive population and by mixing with core agents. The update process is monitored using a Lotka-Volterra-like Ordinary Differential Equation. Given an initial tiny minority of opponents that coexist with both inflexible and peaceful agents, we investigate implications on the emergence of radicalization. Opponents try to turn peaceful agents to opponents driving radicalization. However, inflexible core agents may step in to bring back opponents to a peaceful choice thus weakening the phenomenon. The required minimum individual core involvement to actually curb radicalization is calculated. It is found to be a function of both the majority or minority status of the sensitive subpopulation with respect to the core subpopulation and the degree of activeness of opponents. The results highlight the instrumental role core agents can have to hinder radicalization within the sensitive subpopulation. Some hints are outlined to favor novel public policies towards social integration.

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Section: Introductionmentioning

confidence: 99%

Modeling Radicalization Phenomena in Heterogeneous Populations

Galam

Javarone

2016

PLoS ONE

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“…However, different types of crime are connected to social mechanisms in different ways (Hipp and Steenbeek, 2016) and exhibit unique temporal (Miethe et al, 2005;Oliveira et al, 2018) and spatial characteristics (Andresen and Linning, 2012;Oliveira et al, 2015Oliveira et al, , 2017White et al, 2014). It is plausible that the scaling laws of crime depend on crime type.…”

Section: More Crime In Cities?mentioning

confidence: 99%

More crime in cities? On the scaling laws of crime and the inadequacy of per capita rankings—a cross-country study

Oliveira

2021

Crime Sci

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Crime rates per capita are used virtually everywhere to rank and compare cities. However, their usage relies on a strong linear assumption that crime increases at the same pace as the number of people in a region. In this paper, we demonstrate that using per capita rates to rank cities can produce substantially different rankings from rankings adjusted for population size. We analyze the population–crime relationship in cities across 12 countries and assess the impact of per capita measurements on crime analyses, depending on the type of offense. In most countries, we find that theft increases superlinearly with population size, whereas burglary increases linearly. Our results reveal that per capita rankings can differ from population-adjusted rankings such that they disagree in approximately half of the top 10 most dangerous cities in the data analyzed here. Hence, we advise caution when using crime rates per capita to rank cities and recommend evaluating the linear plausibility before doing so.

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“…However, different types of crime are connected to social mechanisms differently (Hipp and Steenbeek 2016) and exhibit unique temporal (Miethe et al 2005;Oliveira et al 2018) and spatial characteristics (Andresen and Linning 2012;Oliveira et al 2015Oliveira et al , 2017White et al 2014). It is plausible that the scaling laws of crime depend on crime type.…”

Section: More Crime In Cities?mentioning

confidence: 99%

More crime in cities? On the scaling laws of crime and the inadequacy of per capita rankings -- a cross-country study

Oliveira

2020

Preprint

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OBJECTIVES To evaluate the relationship between population size and number of crimes in cities across twelve countries and assess the impact of per capita measurements on crime analyses, depending on offense type. METHODS We use data on burglaries and thefts at the city level and evaluate the relationship between crime numbers and population size using probabilistic scaling analysis. We estimate the growth exponent of each offense type and use Kendall rank correlation to assess the impact of a linear growth assumption (i.e., per-capita analysis) on cities rankings. RESULTS In nine out of eleven countries, theft increases superlinearly with population size; in two of them, it increases linearly. In eight out of ten countries, burglary increases linearly with population size; in two of them, it increases superlinearly. In nonlinear scenarios, using per capita rates to rank cities produces substantially different rankings from rankings adjusted for population size. CONCLUSIONS Comparing cities using per capita crime rates (e.g., crime per 100,000 people per year) assumes that crime increases linearly with population size. Our findings indicate, however, that this assumption is unfounded, implying that one should be cautious when using per capita rankings. When crime increases nonlinearly with population, per capita rates do not remove population effects. The contrasting crime growth of burglary and theft also suggests that different crime dynamics at the local level lead to different macro-level features in cities.

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From Criminal Spheres of Familiarity to Crime Networks

Cited by 7 publications

References 25 publications

Modeling Radicalization Phenomena in Heterogeneous Populations

Modeling Radicalization Phenomena in Heterogeneous Populations

More crime in cities? On the scaling laws of crime and the inadequacy of per capita rankings—a cross-country study

More crime in cities? On the scaling laws of crime and the inadequacy of per capita rankings -- a cross-country study

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