Volume 11, Issue 2

We investigated the additional predictive value of an individual’s neighbourhood (quality and location), and of changes therein on his/her healthcare costs. To this end, we combined several Dutch nationwide data sources from 2003 to 2014, and selected inhabitants who moved in 2010. We used random forest models to predict the area under the curve of the regular healthcare costs of individuals in the years 2011–2014. In our analyses, the quality of the neighbourhood before the move appeared to be quite important in predicting healthcare costs (i.e. importance rank 11 out of 126 socio-demographic and neighbourhood variables; rank 73 out of 261 in the full model with prior expenditure and medication). The predictive performance of the models was evaluated in terms of R 2 (or proportion of explained variance) and MAE (mean absolute (prediction) error). The model containing only socio-demographic information improved marginally when neighbourhood was added ( R 2 +0.8%, MAE −€5). The full model remained the same for the study population ( R 2  = 48.8%, MAE of €1556) and for subpopulations. These results indicate that only in prediction models in which prior expenditure and utilization cannot or ought not to be used neighbourhood might be an interesting source of information to improve predictive performance.

Recent scholarship on militant Islamist radicalization in Southeast Asia highlights the significance of local context in understanding support and recruitment into militancy. While research on terrorism in Southeast Asia engenders a dynamism of epistemic inquiry in diverse areas of research, an empirical mapping of radicalization is generally absent. Research on militant Islamic radicalization in Southeast Asia needs more robust consideration of geospatial relations and data to fill this lacuna. A geospatial analysis is, above all else, synthesis. It bridges spatial statistical analytics and qualitative socio-spatial investigation. We argue that a geospatial analytical approach to understanding radicalization offers a way in which to begin empirically mapping radicalization in the region. Correspondingly, our work considers militant Islamic radicalization in Malaysia by employing geospatial analysis to build a more nuanced layering and comprehensive understanding of the spatial arrangements of radicalization. Viewed from this perspective, radicalization can be understood through spatial logics and practices aimed at facilitating better understandings of socio-political relations of political violence and terrorism more broadly.

Despite the growing criminal activities in South Africa, many victims still do not report the crimes, therefore there was a need to understand the determinants of the likelihood of reporting a crime in the country. Binary logistic regression is a supervised machine learning algorithm that can assist in predicting the likelihood of reporting a crime but the selection of relevant variables to add in the model varies from one author to the other. Selection of theoretically sound and statistically relevant independent variables is key to achieving parsimonious multivariate models. This study sought to test the efficiency of some commonly used variable selection methods for logistic regression models in order to identify the most relevant determinants of the likelihood of reporting a crime of housebreaking. The study used 17 candidate variables such as the victims’ demographic variables and their perceptions on the police. The multivariate model fitted using stepwise selection was found to be a best fit for the data based on the lowest AIC, the highest classification accuracy rate and the highest Area under the Receiver Operating Characteristic curve. The model fitted using the Hosmer-Lemeshow (H-L) algorithm was the worst fit for the data. The study revealed a limitation of the stepwise selection method which is that this method may select different independent variables for each unique set of randomly selected observations of the same dataset. The study established a multivariate logistic regression model to predict the likelihood of a victim reporting a crime of housebreaking and the determinants thereof.

This article examines the role of authoritarianism in shaping individual policy support for creating a waiting period before legal immigrants can access welfare programs. I use logistic regression models to analyse the opinions of non-Latino whites from the 1992 American National Election Study (ANES). I find that authoritarianism is related to a waiting period before immigrants can access welfare benefits, despite a significant number of non-authoritarians also preferring a waiting period. This effect is robust with the inclusion of several traditional predictors of welfare attitudes.