Evaluation of the quality of an online geocoding resource in the context of a large Brazilian city

Abstract

Geocoding urban addresses usually requires the use of an underlying address database. Under the influence of the format defined for TIGER files decades ago, most address databases and street geocoding algorithms are organized around street centerlines, associating numbering ranges to thoroughfare segments between two street crossings. While this method has been successfully employed in the USA for a long time, its transposition to other countries may lead to increased errors. This article presents an evaluation of the centerline‐geocoding resources provided by Google Maps, as compared to the point‐geocoding method used in the city of Belo Horizonte, Brazil, which we took as a baseline. We generated a textual address for each point object found in the city's point‐based address database, and submitted it to the Google Maps geocoding API. We then compared the resulting coordinates with the ones recorded in Belo Horizonte's GIS. We demonstrate that the centerline segment interpolation method, employed by the online resources following the American practice, has problems that can considerably influence the quality of the geocoding outcome. Completeness and accuracy have been found to be irregular, especially within lower income areas. Such errors in online services can have a significant impact on geocoding efforts related to social applications, such as public health and education, since the online service can be faulty and error‐prone in the most socially demanding areas of the city. In the conclusion, we point out that a volunteered geographic information (VGI) approach can help with the enrichment and enhancement of current geocoding resources, and can possibly lead to their transformation into more reliable point‐based geocoding services.

A cross-sectional ecological analysis of international and sub-national health inequalities in commercial geospatial resource availability

“…However, elsewhere an apparent inverse relationship between geocoding outcomes and area deprivation has been observed at local level in other LMICs. A study in the Brazilian city of Belo Horizonte, for example, found that geocoding precision via the Google geocoding API was lower in slum areas than in formal urban neighbourhoods [ 51 ]. In response to this issue, the What3Words georeferencing system, which uses an algorithm to assign three words as a unique, human-friendly georeference to each of 57 trillion grid squares globally, has been used to locate addresses in Brazilian favelas lacking conventional addressing systems [ 52 ].…”

Impact of travel mode shift and trip distance on active and non-active transportation in the São Paulo Metropolitan Area in Brazil

“…We used a much less sophisticated modeling approach, which was unable to consider synergies among changes. Concerning distance calculation, another limitation is related to some inaccuracy in Google Maps in Brazil, especially in lower income areas ( Davis Junior and de Alencar, 2011 ).…”

Assessing the Accuracy of Volunteered Geographic Information arising from Multiple Contributors to an Internet Based Collaborative Project

“…The power of the crowd is well known and it can sometimes be used to derive information that was impossible or at least impractical to obtain by other means. The potential of crowdsourcing has been noted in geography and associated geospatial sciences with numerous examples of its use (Basiouka and Potsiou , Davis and de Alencar , Girres and Touya , Goodchild , Heipke , Neis et al. ).…”