Enhancing Rossmo’s criminal geographic targeting model through environmental and land-use spatial layers: a case study of the Atlanta homicides (1979–1981)

This study proposes an enhancement of traditional geographic profiling by expanding the range of spatial variables incorporated into Rossmo’s Criminal Geographic Targeting (CGT) formula. Rather than replacing the original CGT framework, the proposed approach refines its output by integrating environmental and contextual constraints to improve the operational prioritization of residential offender anchor point search areas. A demonstrative case study was conducted using a grid-based implementation of the CGT model applied to the Atlanta homicides (1979–1981), a historical series involving multiple victims and spatially distributed offences and body recovery locations. While offender attribution within the series remains contested, the cases were treated as an investigative aggregation reflecting how they were operationally considered at the time. In addition to standard crime location data, multiple geospatial layers were incorporated, including a Digital Elevation Model (DEM), urban green areas (parks), residential neighborhood zones, and cemetery locations. Each layer was operationalized through spatial weighting and combined with the baseline Rossmo probability surface to generate an enhanced predictive heatmap. The layer-enhanced model produced a more spatially concentrated probability distribution than the baseline CGT output, substantially reducing the prioritized search area while maintaining proximity to the known offender residence. The integration of terrain and land-use constraints resulted in clearer differentiation between high- and low-priority zones, improving the spatial focus for residential search prioritization. The findings indicate that incorporating environmental and land-use information into Rossmo’s CGT model can enhance the operational effectiveness of geographic profiling by narrowing search areas without altering the model’s core behavioral assumptions. The proposed approach supports investigative decision making through improved spatial prioritisation and offers a transparent and reproducible framework for integrating contextual geospatial data into CGT-based analyses.