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

• Main Authors: Huayu Zhang (Author), Arlene Casey (Author), Imane Guellil (Author), Víctor Suárez-Paniagua (Author), Clare MacRae (Author), Charis Marwick (Author), Honghan Wu (Author), Bruce Guthrie (Author), Beatrice Alex (Author)
• Format: Book
• Published: Frontiers Media S.A., 2023-11-01T00:00:00Z.
• Subjects: article
• Online Access: Connect to this object online.
• Notes: 2673-253X; 10.3389/fdgth.2023.1186208
• Keywords: free-text address; Unique Property Reference Number; UPRN; record linkage; machine learning; Medicine; R; Public aspects of medicine; RA1-1270; Electronic computers. Computer science; QA75.5-76.95
• Source: Frontiers in Digital Health, Vol 5 (2023)

IntroductionLinking 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.MethodsIn 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).ResultsWe 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.DiscussionIn 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.