We examined whether neighborhood-level characteristics influence spatial variations in the risk of intimate partner violence (IPV). Geocoded data on IPV cases with associated protection orders (n = 1,623) in the city of Valencia, Spain (2011–2013), were used for the analyses. Neighborhood units were 552 census block groups. Drawing from social disorganization theory, we explored 3 types of contextual influences: concentrated disadvantage, concentration of immigrants, and residential instability. A Bayesian spatial random-effects modeling approach was used to analyze influences of neighborhood-level characteristics on small-area variations in IPV risk. Disease mapping methods were also used to visualize areas of excess IPV risk. Results indicated that IPV risk was higher in physically disordered and decaying neighborhoods and in neighborhoods with low educational and economic status levels, high levels of public disorder and crime, and high concentrations of immigrants. Results also revealed spatially structured remaining variability in IPV risk that was not explained by the covariates. In this study, neighborhood concentrated disadvantage and immigrant concentration emerged as significant ecological risk factors explaining IPV. Addressing neighborhood-level risk factors should be considered for better targeting of IPV prevention.

In this study, we analyze first whether there is a common spatial distribution of child maltreatment (CM) and intimate partner violence (IPV), and second, whether the risks of CM and IPV are influenced by the same neighborhood characteristics, and if these risks spatially overlap. To this end we used geocoded data of CM referrals (N = 588) and IPV incidents (N = 1450) in the city of Valencia (Spain). As neighborhood proxies, we used 552 census block groups. Neighborhood characteristics analyzed at the aggregated level (census block groups) were: Neighborhood concentrated disadvantage (neighborhood economic status, neighborhood education level, and policing activity), immigrant concentration, and residential instability. A Bayesian joint modeling approach was used to examine the spatial distribution of CM and IPV, and a Bayesian random-effects modeling approach was used to analyze the influence of neighborhood-level characteristics on small-area variations of CM and IPV risks. For CM, 98% of the total between-area variation in risk was captured by a shared spatial component, while for IPV the shared component was 77%. The risks of CM and IPV were higher in neighborhoods characterized by lower levels of economic status and education, and higher levels of policing activity, immigrant concentration, and residential instability. The correlation between the log relative risk of CM and IPV was .85. Most census block groups had either low or high risks in both outcomes (with only 10.5% of the areas with mismatched risks). These results show that certain neighborhood characteristics are associated with an increase in the risk of family violence, regardless of whether this violence is against children or against intimate partners. Identifying these high-risk areas can inform a more integrated community-level response to both types of family violence. Future research should consider a community-level approach to address both types of family violence, as opposed to individual-level intervention addressing each type of violence separately.

Willingness to intervene when one becomes aware of a case of intimate partner violence against women (IPVAW) reflects the level of tolerance and acceptance of this type of violence in society. Increasing the likelihood of intervention to help victims of IPVAW is also a target for prevention strategies aiming to increase informal social control of IPVAW. In this study, we present the development and validation of the Willingness to Intervene in Cases of Intimate Partner Violence (WI-IPVAW) scale. We report data for both the long and short versions of the scale. We analyzed the latent structure, the reliability and validity of the WI-IPVAW across four samples (N = 1648). Factor analyses supported a bifactor model with a general non-specific factor expressing willingness to intervene in cases of IPVAW, and three specific factors reflecting different intervention preferences: a preference for setting the law enforcement process in motion (“calling the cops” factor), a preference for personal intervention (“personal involvement” factor), and a preference for non-intervention (“not my business” factor). Configural, metric, and partial scalar invariance across genders were supported. Two short versions of the scale, with nine and six items, respectively, were constructed on the base of quantitative and qualitative criteria. The long and short versions of the WI-IPVAW demonstrated both high reliability and construct validity, as they were strongly related to the acceptability of IPVAW, victim-blaming attitudes, perceived severity of IPVAW, and hostile sexism. These results confirm that both the long and short versions of the WI-IPVAW scale are psychometrically sound instruments to analyze willingness to intervene in cases of IPVAW in different settings and with different research needs (e.g., long versions for clinical and research settings, and short versions for large population surveys). The WI-IPVAW is also useful for assessing prevention policies and public education campaigns design to promote a more responsive social environment in cases of IPVAW, thus contributing to deter and reduce this major social and public health problem.

This paper uses spatial data of cases of intimate partner violence against women (IPVAW) to examine neighborhood-level influences on small-area variations in IPVAW risk in a police district of the city of Valencia (Spain). To analyze area variations in IPVAW risk and its association with neighborhood-level explanatory variables we use a Bayesian spatial random-effects modeling approach, as well as disease mapping methods to represent risk probabilities in each area. Analyses show that IPVAW cases are more likely in areas of high immigrant concentration, high public disorder and crime, and high physical disorder. Results also show a spatial component indicating remaining variability attributable to spatially structured random effects. Bayesian spatial modeling offers a new perspective to identify IPVAW high and low risk areas, and provides a new avenue for the design of better-informed prevention and intervention strategies.

The aim of this study was to develop and validate a scale measuring acceptability of IPVAW (A-IPVAW).