FLAP: a framework for linking free-text addresses to the Ordnance Survey Unique Property Reference Number database

Huayu Zhang; Arlene Casey; Imane Guellil; Víctor Suárez-Paniagua; Clare MacRae; Charis Marwick; Honghan Wu; Bruce Guthrie; Beatrice Alex

doi:10.3389/fdgth.2023.1186208

FLAP: a framework for linking free-text addresses to the Ordnance Survey Unique Property Reference Number database

Front Digit Health. 2023 Nov 28:5:1186208. doi: 10.3389/fdgth.2023.1186208. eCollection 2023.

Authors

Huayu Zhang¹, Arlene Casey¹, Imane Guellil¹, Víctor Suárez-Paniagua¹, Clare MacRae², Charis Marwick³, Honghan Wu^{1

4}, Bruce Guthrie¹, Beatrice Alex^{1

5

6}

Affiliations

¹ Advanced Care Research Centre, The Usher Institute, College of Medicine and Veterinary Medicine, University of Edinburgh, Edinburgh, United Kingdom.
² Deanery of Molecular, Genetic and Population Health Sciences, Usher Institute, College of Medicine and Veterinary Medicine, University of Edinburgh, Edinburgh, United Kingdom.
³ Population Health and Genomics, School of Medicine, University of Dundee, Dundee, United Kingdom.
⁴ Institute of Health Informatics, University College London, London, United Kingdom.
⁵ School of Literatures, Languages and Cultures, University of Edinburgh, Edinburgh, United Kingdom.
⁶ Edinburgh Futures Institute, University of Edinburgh, Edinburgh, United Kingdom.

Abstract

Introduction: Linking free-text addresses to unique identifiers in a structural address database [the Ordnance Survey unique property reference number (UPRN) in the United Kingdom (UK)] is a necessary step for downstream geospatial analysis in many digital health systems, e.g., for identification of care home residents, understanding housing transitions in later life, and informing decision making on geographical health and social care resource distribution. However, there is a lack of open-source tools for this task with performance validated in a test data set.

Methods: In this article, we propose a generalisable solution (A Framework for Linking free-text Addresses to Ordnance Survey UPRN database, FLAP) based on a machine learning-based matching classifier coupled with a fuzzy aligning algorithm for feature generation with better performance than existing tools. The framework is implemented in Python as an Open Source tool (available at Link). We tested the framework in a real-world scenario of linking individual's ( $n = 771,588$ ) addresses recorded as free text in the Community Health Index (CHI) of National Health Service (NHS) Tayside and NHS Fife to the Unique Property Reference Number database (UPRN DB).

Results: We achieved an adjusted matching accuracy of 0.992 in a test data set randomly sampled ( $n = 3, 876$ ) from NHS Tayside and NHS Fife CHI addresses. FLAP showed robustness against input variations including typographical errors, alternative formats, and partially incorrect information. It has also improved usability compared to existing solutions allowing the use of a customised threshold of matching confidence and selection of top $n$ candidate records. The use of machine learning also provides better adaptability of the tool to new data and enables continuous improvement.

Discussion: In conclusion, we have developed a framework, FLAP, for linking free-text UK addresses to the UPRN DB with good performance and usability in a real-world task.

Keywords: UPRN; Unique Property Reference Number; free-text address; machine learning; record linkage.

Grants and funding

This research was funded by the Legal & General Group (research grant to establish the independent Advanced Care Research Centre at University of Edinburgh). The funder was not involved in the study design, analysis, interpretation of data, the writing of this article or the decision to submit it for publication.