Local geographic context is vital in understanding how, where, and why opioid overdoses occur. Official overdose data often lack the context required for evidence-based intervention, especially in towns where analytical resources are often more limited due to a smaller population size. In this study we use spatial mixed methods to capture local expert knowledge in the form of a Spatial Video Geonarrative (SVG) and Wordmapper. From 2015 to 2020 different overdose related “experts” in five small towns were interviewed resulting in the identification of numerous micro-geographies of risk such as around gas stations, fast food restaurants, and motels, as well as the connected proximate spaces, such as side streets or parking lots. The local geographic connective tissue was also revealed whether physical in terms of across urban-suburban political boundaries, or through true social networks initially established by going to the same school. While there were similarities between study towns, enough local nuance existed so that universal rules could not be developed; not all gas stations pose the same risk for example. Indeed, the main purpose of this paper is to show, as the opioid epidemic shows no signs of abating, intervention strategies must be, at least in part, based on these types of repeatable techniques designed to continually update our local knowledge. This chapter illustrates the potential of SVG and Wordmapper as a sustainable local method that can be easily applied by any locality.