Comparing Esri ArcGIS and SAS Geocoding Approaches: Test case with 3,238 Wisconsin addresses

This report describes a comparison of two geocoding methods used by the Cohorts for Environmental Exposures and Cancer Risks (CEECR) consortium: ArcGIS Geocoding by Esri and the SAS GEOCODE Procedure. The goal of this report is to determine the comparability of data sets that employ different approaches for linking survey data with spatial surrogates of exposure to environmental and socioeconomic factors. ArcGIS and SAS GEOCODE were selected as two platforms for this comparison because both programs are being used by one or more CEECR cohort study teams and they can be used locally offline. The latter minimizes confidentiality issues related to online data linkages. Residential addresses from 3,238 Wisconsin residents in the Cancer & COVID Study and the Wisconsin in situ Cohort (WISC) were geocoded and linked to eight different publicly available datasets of environmental and socioeconomic factors at various geographic scales using both geocoding platforms. Since the two analytic platforms vary in geocoding approaches, the validity and accuracy of both platforms were compared to examine differences when assigning surrogate measurements of exposure based on spatial locations. ArcGIS offered a higher specificity for matched addresses with slightly more latitude/longitude point and street matches (97.7%) than SAS (95.9%), with the remainder matching at the zip code level. The two geocoding platforms showed high concordance in assignment at the county (99.6%), census tract (96.5%), and census block group (94.7%). As a result, the correlations based on census tracts and block groups were very strong for linked exposure measures of socioeconomic status, environmental justice, urban/rural residence, air pollution, proximity to industrial sites, and cancer risk (all intraclass correlation coefficients [≥]98%). Slightly lower concordance was observed for point source linkages (intraclass correlation coefficients 96-97%). Approximately ~4% of addresses were mis-matched largely in rural areas where census areas are larger and accurate geocoding base-layers are less widely available than in urban areas. For researchers that are already utilizing SAS, the GEOCODE procedure can be a logical choice as it is included in base SAS software and does not require an additional cost. However, SAS and ArcGIS provide similar options for the vast majority of study address locations.