Spatial Heterogeneity in Positional Errors: A Comparison of Two Residential Geocoding Efforts in the Agricultural Health Study

Abstract

Geocoding is a powerful tool for environmental exposure assessments that rely on spatial databases. Geocoding processes, locators, and reference datasets have improved over time; however, improvements have not been well-characterized. Enrollment addresses for the Agricultural Health Study, a cohort of pesticide applicators and their spouses in Iowa (IA) and North Carolina (NC), were geocoded in 2012–2016 and then again in 2019. We calculated distances between geocodes in the two periods. For a subset, we computed positional errors using “gold standard” rooftop coordinates (IA; N = 3566) or Global Positioning Systems (GPS) (IA and NC; N = 1258) and compared errors between periods. We used linear regression to model the change in positional error between time periods (improvement) by rural status and population density, and we used spatial relative risk functions to identify areas with significant improvement. Median improvement between time periods in IA was 41 m (interquartile range, IQR: −2 to 168) and 9 m (IQR: −80 to 133) based on rooftop coordinates and GPS, respectively. Median improvement in NC was 42 m (IQR: −1 to 109 m) based on GPS. Positional error was greater in rural and low-density areas compared to in towns and more densely populated areas. Areas of significant improvement in accuracy were identified and mapped across both states. Our findings underscore the importance of evaluating determinants and spatial distributions of errors in geocodes used in environmental epidemiology studies.

Ensemble Models for Tick Vectors: Standard Surveys Compared with Convenience Samples

“…The issue of positional error [9] has been explored in medical and spatial geography for data sets most frequently when street addresses were used as the locations of health outcomes [11][12][13]. There are various reasons positional errors arise with these data sources [9].…”

“…Because street segments are often longer in rural areas these errors tend to be larger [9]. Address positional errors have received special attention in exposure studies for rural populations [11][12][13]. In these studies, house addresses had reported positional errors, with 44-72% with errors of at least 100 m and the average positional errors ranged from 200 to >800 meters (with extreme values of 35.6-48.5 km).…”

“…These spatial errors are related to converting a place description to geographic coordinates (geocoding) [9,11,12] but the consequences for modeling SDMs appeared have been ignored in the applications literature. This issue has been most frequently evaluated when a street address is the key field in geocoding efforts [ (12,13].…”

Drinking water sources and water quality in a prospective agricultural cohort

“…Additionally, to assess environmental exposures, we collected and geocoded AHS participants’ addresses at each study phase. 18,19 Version 1 geocodes were used for assigning drinking water source and modeling nitrate concentrations in private wells (described below).…”

Ensemble Models for Tick Vectors: Standard Surveys Compared with Convenience Samples

“…These spatial errors are related to the algorithms that convert a place description to geographic coordinates (geocoding) [ 9 , 11 , 12 ] but the consequences for modeling SDMs appeared have been ignored in the applications literature. This issue has been most frequently evaluated when a street address is the key field in geocoding efforts [ 12 , 13 ].…”