This study posed questions that advance the science of geonarratives through a case study of criminal ex-offenders.
The importance of including a contextual underpinning to the spatial analysis of social data is gaining traction in the spatial science community. The challenge, however, is how to capture these data in a rigorous manner that is translational. One method that has shown promise in achieving this aim is the spatial video geonarrative (SVG). In the current study, 11 ex-offenders provided sketch maps and SVGs identifying high-crime areas of their community. Wordmapper software was used to map and classify the SVG content; its spatial filter extension was used for hot spot mapping with statistical significance tested using Monte Carlo simulations. Then, each subject's sketch map and SVG were compared. Results indicate that SVGs consistently produce finer spatial-scale data and more locations of relevance than the sketch maps. SVGs also provide explanation of spatial-temporal processes and causal mechanisms linked to specific places, which are not evident in the sketch maps. SVG can be a rigorous translational method for collecting data on the geographic context of many phenomena; therefore, this paper makes an important advance in understanding how environmentally immersive methods contribute to the understanding of geographic context. (publisher abstract modified)