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Marginalized individuals are less likely to participate or have their interests represented in political processes than historically privileged individuals. Interest groups are considered the best means to address this gap, but there is little research on the role of interest groups in mobilizing people to participate in political processes and none on marginalized communities in particular. This paper is the first to test hypotheses about organizational strategies used to mobilize vulnerable communities for political participation around unconventional oil and gas policies in California and Colorado. Based on a survey of interest groups in both states, the results show that interest groups working in vulnerable communities do more outside advocacy (i.e., connecting residents to representatives) and use more personal communication methods (i.e., door-to-door canvassing) than interest groups working in historically privileged communities. However, organizational strategies in general are not well predicted by the target community's composition, suggesting that decisions around mobilization strategies are driven by other factors. [ABSTRACT FROM AUTHOR]

Point sources in spatially referenced data can impact outcomes in surrounding locations (e.g., a factory that emits air pollution). Previous statistical methods have sought to describe the non-stationary correlation induced by the presence of a point source, with fewer attempting to quantify its overall impact. We introduce directionally varying change points (DVCP), a model that aims to estimate the magnitude of the impact of a point source as well as its range of influence across the spatial domain. DVCP allows for a unique change point parameter, describing the range of influence of a point source, at every angle extending from the source and uses a Gaussian process with directionally defined correlation function to facilitate estimation of the parameters. The Gaussian predictive process approximation is used for fitting the model to large datasets. Through simulation, we show that DVCP can easily accommodate a wide range of shapes defining the range of influence. We apply DVCP to better understand spatial patterns of ambient PM 2.5 concentrations and issues related to environmental inequity in California and Colorado. The method is available in the R package DVCP. Supplementary materials accompanying this paper appear online. [ABSTRACT FROM AUTHOR]