Improving geocoding matching rates of structured addresses in Rio de Janeiro, Brazil

Abstract

Strategies for improving geocoded data often rely on interactive manual processes that can be time-consuming and impractical for large-scale projects. In this study, we evaluated different automated strategies for improving address quality and geocoding matching rates using a large dataset of addresses from death records in Rio de Janeiro, Brazil. Mortality data included 132,863 records with address information in a structured format. We performed regular expressions and dictionary-based methods for address standardization and enrichment. All records were linked by their postal code or street name to the Brazilian National Address Directory (DNE) obtained from Brazil’s Postal Service. Residential addresses were geocoded using Google Maps. Records with address data validated down to the street level and location type returned as rooftop, range interpolated, or geometric center were considered a geocoding match. The overall performance was assessed by manually reviewing a sample of addresses. Out of the original 132,863 records, 85.7% (n = 113,876) were geocoded and validated, out of which 83.8% were matched as rooftop (high accuracy). Overall sensitivity and specificity were 87% (95%CI: 86-88) and 98% (95%CI: 96-99), respectively. Our results indicate that address quality and geocoding completeness can be reliably improved with an automated geocoding process. R scripts and instructions to reproduce all the analyses are available at https://github.com/reprotc/geocoding.

Automatic Identification of Addresses: A Systematic Literature Review

“…However, the successful automated geocoding of residential addresses depends on a number of factors, namely population densities (with positional error increasing as population density decreases) [27,67], the completeness of an address (existence or not of a number and street name), and changes in street names, among others [27]. These limitations can be tackled by the previous standardization and enrichment of addresses [68] and the choice of the most adequate geocoding method, including the use of property data [67] or the use of hybrid geocoding approaches [28].…”

Spatial Analysis of Dengue Clusters at Department, Municipality and Local Scales in the Southwest of Colombia, 2014–2019

“…The geocoding process, which consists of assigning geographic coordinates to the addresses, was carried out in cases where the detailed description of the location was available, through Google Maps Geocoding API [ 20 ]. Georeferencing, which also involves the positioning of the element in a coordinate system, was applied for general descriptions referring to polygon-type elements.…”

Short-term association between ambient air pollution and cardio-respiratory mortality in Rio de Janeiro, Brazil

“…We selected all deaths from cardiovascular (International Classification of Diseases, 10th Revision–ICD-10, codes I00-I99) and respiratory diseases (ICD-10, codes J00-J99) that occurred in residents of the municipality of Rio de Janeiro between 2012 and 2017. Residential addresses were geocoded and validated using the method described in a previous study [ 15 ]. In brief, we used different automated methods for address matching and validation, and the performance of the geocoding process was assessed by manually reviewing a sample of addresses.…”