Exploring Neighborhood Influences on Small-Area Variations in Intimate Partner Violence Risk: A Bayesian Random-Effects Modeling Approach

Gracia, Enrique; López‐Quílez, Antonio; Marco, Miriam; Lladosa, Silvia; Lila, Marisol

doi:10.3390/ijerph110100866

Cited by 52 publications

(64 citation statements)

References 78 publications

(134 reference statements)

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…exp(−0.3302) − 1 = −0.2812 (15) exp(−0.5331) − 1 = −0.4132 (16) Using the same calculation method, we estimated that the relative risk of burglary and non-motor vehicle theft will increase by 67.30% and 46.96%, with an increase of one standard deviation in bar density, respectively (Equations (17) and (18)). Table 4 shows the posterior means of the covariance/correlation matrix of the random effects.…”

Section: Analysis Of Risk Factorsmentioning

confidence: 93%

“…Policing rooms are set up by local police stations and staffed by several police officers to guarantee neighborhood's security. The relationship between policing and crime has been investigated in previous studies [15,27].…”

Section: Datamentioning

confidence: 99%

“…This is changing, however, as more researchers have become aware of the advantages of Bayesian spatial models. A small but growing body of research has adopted Bayesian spatial modeling approaches in crime analysis [10][11][12][13][14][15][16][17]. For example, researchers using Bayesian spatial models have investigated contextual influences on domestic violence [12,15,18], juvenile offenders [14] and property crime [13].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Joint Modeling of Multiple Crimes: A Bayesian Spatial Approach

Liu

Zhu

2017

IJGI

View full text Add to dashboard Cite

A multivariate Bayesian spatial modeling approach was used to jointly model the counts of two types of crime, i.e., burglary and non-motor vehicle theft, and explore the geographic pattern of crime risks and relevant risk factors. In contrast to the univariate model, which assumes independence across outcomes, the multivariate approach takes into account potential correlations between crimes. Six independent variables are included in the model as potential risk factors. In order to fully present this method, both the multivariate model and its univariate counterpart are examined. We fitted the two models to the data and assessed them using the deviance information criterion. A comparison of the results from the two models indicates that the multivariate model was superior to the univariate model. Our results show that population density and bar density are clearly associated with both burglary and non-motor vehicle theft risks and indicate a close relationship between these two types of crime. The posterior means and 2.5% percentile of type-specific crime risks estimated by the multivariate model were mapped to uncover the geographic patterns. The implications, limitations and future work of the study are discussed in the concluding section.

show abstract

Section: Analysis Of Risk Factorsmentioning

confidence: 93%

Section: Datamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Joint Modeling of Multiple Crimes: A Bayesian Spatial Approach

Liu

Zhu

2017

IJGI

View full text Add to dashboard Cite

show abstract

“…Research has found that concentrated disadvantage is the factor most strongly associated with crime, even after controlling for individual characteristics [1,8]. Immigration has also been related to crime rates, and previous research has shown that higher levels of ethnic heterogeneity are related to higher levels of crime [1,9,10]. Residential instability is also a relevant factor in social disorganization theory.…”

Section: Introductionmentioning

confidence: 99%

“…Most research has focused on residential burglary [13][14][15], juvenile delinquency [16,17], homicides [1,18,19], and robbery and assaults [18,19]. Interest is also growing in studying this relationship in other crimes that tends to occur 'behind closed doors' [20] such as intimate partner violence [9,10] or child maltreatment [21].…”

Section: Introductionmentioning

confidence: 99%

Linking Neighborhood Characteristics and Drug-Related Police Interventions: A Bayesian Spatial Analysis

Marco

Gracia

López‐Quílez

2017

IJGI

Self Cite

View full text Add to dashboard Cite

Abstract:This paper aimed to analyze the spatial distribution of drug-related police interventions and the neighborhood characteristics influencing these spatial patterns. To this end, police officers ranked each census block group in Valencia, Spain (N = 552), providing an index of drug-related police interventions. Data from the City Statistics Office and observational variables were used to analyze neighborhood characteristics. Distance to the police station was used as the control variable. A Bayesian ecological analysis was performed with a spatial beta regression model. Results indicated that high physical decay, low socioeconomic status, and high immigrant concentration were associated with high levels of drug-related police interventions after adjustment for distance to the police station. Results illustrate the importance of a spatial approach to understanding crime.

show abstract

Neighborhood violent crime and adolescent relationship aggression

Taylor

Mumford

Okeke

et al. 2019

Aggressive Behavior

View full text Add to dashboard Cite

Research has shown that neighborhoods play a role in the etiology of violence. However, few adolescent relationship aggression (ARA) studies have objective measures of violent neighborhoods. Drawing on a nationally representative sample of youth, this study examines the association between ARA and local levels of violent crime (measured using geocoded Uniform Crime Report data from each of the youths' residential neighborhoods). Study analyses are based on survey data from 723 youth (ages 10-18) in current or recent dating relationships (351 males and 372 females) in the Survey on Teen Relationships and Intimate Violence (STRiV), a national representative household panel survey exploring interpersonal violence and related aggression among adolescents. About 19% of the sample reported ARA victimization in their most recent dating relationship (ARA perpetration was 17%). Neighborhood violent crime in the study (males living in 86.9 and females 99.8) was slightly lower than the national average of 100. With a broad national sample, 40% non-Whites,hypotheses guided by theories of neighborhood influence were tested. The study did not find an association between neighborhood violent crime and ARA victimization and perpetration, controlling for key demographic factors. The results, for a broad range of high-and low-crime neighborhoods, suggest that neighborhood violence does not seem to affect individual rates of ARA. The results suggest the ARA victimization and perpetration are perhaps ubiquitous and found both in low and high violent crime neighborhoods, suggesting that addressing local violent crime rates alone does not seem to be a path to also reducing ARA.

show abstract

Exploring Neighborhood Influences on Small-Area Variations in Intimate Partner Violence Risk: A Bayesian Random-Effects Modeling Approach

Cited by 52 publications

References 78 publications

Joint Modeling of Multiple Crimes: A Bayesian Spatial Approach

Joint Modeling of Multiple Crimes: A Bayesian Spatial Approach

Linking Neighborhood Characteristics and Drug-Related Police Interventions: A Bayesian Spatial Analysis

Neighborhood violent crime and adolescent relationship aggression

Contact Info

Product

Resources

About