Spatial Errors In Automated Geocoding Of Incident Locations In Australian Suicide Mortality Data

Background: There is increasing interest in the spatial analysis of suicide data to identify high-risk (often public) locations likely to benefit from access restriction measures. The identification of such locations, however, relies on accurately geocoded data. This study aims to examine the extent to which common completeness and positional spatial errors are present in suicide data due to the underlying geocoding process.Methods: Using Australian suicide mortality data from the National Coronial Information System for the period of 2008-2017, we compared the custodian automated geocoding process to an alternate multiphase process. Descriptive and kernel density cluster analyses were conducted to ascertain data completeness (address matching rates) and positional accuracy (distance revised) differences between the two datasets.Results: The alternate geocoding process initially improved address matching from 67.8% in the custodian dataset to 78.4%. Additional manual identification of nonaddress features (such as cliffs or bridges) improved overall match rates to 94.6%. Nearly half (49.2%) of nonresidential suicide locations were revised more than 1,000 m from data custodian coordinates. Spatial misattribution rates were greatest at the smallest levels of geography. Kernel density maps showed clear misidentification of hotspots relying solely on autogeocoded data.Conclusion: Suicide incidents that occur at nonresidential addresses are being erroneously geocoded to centralized fall-back locations in autogeocoding processes, which can lead to misidentification of suicide clusters. Our findings provide insights toward defining the nature of the problem and refining geocoding processes, so that suicide data can be used reliably for the detection of suicide hotspots. See video abstract at, http://links.lww.com/EDE/B862.