Development of an Algorithm to Evaluate the Quality of Geolocated Addresses in Urban Areas

Abstract

The spatial and semantic data of geographic addresses are extremely important for citizens, governments, and companies. The addresses can georeference environmental, economic, security, health, and demographic parameters in urban areas. Additionally, address components can be used by users to locate any point of interest (POI) with location-based systems (LBSs). For this reason, errors in address data can affect the geographic location of events, map representations, and spatial analyses. Thus, this paper presents the development of an algorithm for evaluating the quality of semantic and geographic information in any geospatial address dataset. The reference datasets are accessible using open data platforms or spatial data infrastructure (SDI) and volunteered geographic information (VGI), and both have been compared with commercial datasets using geocoding web services. Address quality analysis was developed using several open-source data science code libraries combined with spatial databases and geographic information systems. In addition, the quality of geographic addresses was evaluated by carrying out normalized tests in accordance with International Geospatial Standards (ISO 19157). Finally, this methodology assesses the quality of authorized and VGI address datasets that can be used for geocoding any relevant information in specific urban areas.

Challenges in Geocoding: An Analysis of R Packages and Web Scraping Approaches

“…Given the diversity of the geocoding services available, it is crucial to systematically evaluate their output, so as to ensure that decision-makers are relying on the most accurate and reliable data. While some previous work has focused on assessing the accuracy of these services [28,29], there is a need for more in-depth evaluations considering a broader range of current service providers and their evolving technologies. This could lead to a better understanding of their respective strengths and limitations, ultimately enhancing the precision of geocoding applications across different fields.…”