Improving the Spatial Identification of Early Years Providers in England: A Comparison of Practical UPRN Linkage Approaches
This study evaluates five practical address-matching approaches for assigning Unique Property Reference Numbers (UPRNs) to Early Years Provider records in England, finding that while linkage coverage varies significantly, routine inclusion of UPRNs in official registers is essential for accurate spatial identification and effective service planning.
Original paper licensed under CC BY 4.0 (https://creativecommons.org/licenses/by/4.0/). This is an AI-generated explanation of the paper below. It is not written or endorsed by the authors. For technical accuracy, refer to the original paper. Read full disclaimer
In the complex landscape of public services, knowing exactly where a facility is located is often the difference between effective planning and blind guessing. For children's services, this means understanding not just how many childcare providers exist in a region, but precisely where they stand on the map. This spatial clarity is essential for spotting gaps in coverage, ensuring families in deprived areas can access care, and linking service data with environmental factors like air quality or local green spaces. However, a significant hurdle exists in the way this data is currently stored. While government records list the names and addresses of childcare providers, they frequently lack a specific, standardized code that uniquely identifies every building and plot of land in the country. Without this code, researchers and planners struggle to connect childcare data with other vital information, leaving the true distribution of services somewhat hidden.
A team of researchers at University College London set out to solve this problem by testing how best to assign these unique location codes to thousands of childcare records in England. They focused on the official register of early years providers, which contains over 80,000 entries but often lists addresses in a messy, inconsistent format. Some providers operate out of dedicated buildings, while others run from shared spaces like church halls, community centers, or school sites, making it difficult to pinpoint their exact location using standard computer matching tools. The researchers took a sample of nearly 6,500 of these records and tried five different methods to attach the correct unique location code to each one. They compared open-source software, proprietary cloud services, and a custom-built system designed specifically for this task, checking their results against a manually verified "gold standard" list of correct locations.
The study revealed a crucial lesson: finding a match is not the same as finding the right match. Some of the methods tested were incredibly good at returning a location code for almost every record, achieving near-perfect coverage. However, this high success rate often came at a cost; these methods frequently assigned the wrong code to the wrong building, mistaking a provider in a community hall for a different building nearby. Other methods were more cautious, returning fewer results overall but ensuring that when they did provide a code, it was highly likely to be correct. The researchers found that the most balanced approach—one that offered a good mix of finding many providers while keeping errors low—was a custom-built system that used specific rules to filter out unlikely matches, alongside a standard open-source tool that performed well when the address data was relatively clear.
One of the most interesting findings concerned the nature of the addresses themselves. The childcare providers in the study often did not fit the neat, structured format that many computer programs expect. Because many providers operate out of shared venues, the address might describe a room within a larger building rather than a standalone house. The study showed that tools designed primarily for residential addresses often struggled with this complexity, either failing to find a match or guessing incorrectly. The custom-built method, which allowed the researchers to apply specific logic about what kinds of buildings could plausibly house a childcare provider, proved more adaptable to these real-world quirks. This suggests that for institutional data, a flexible, rule-based approach often works better than a rigid, one-size-fits-all algorithm.
The researchers also highlighted the limitations of relying on automated tools alone. Even the best-performing methods did not get every single record right, and in cases where the address was vague or the venue was shared, the computer could not always determine the single most defensible location without human judgment. The study concluded that while automated tools are powerful, they are not a magic bullet. The most effective strategy involves understanding the specific weaknesses of each tool and recognizing that a high number of matches does not guarantee accuracy.
Ultimately, the paper argues that the solution to this problem lies not just in better software, but in better data collection from the start. If the government agency that registers these providers included the unique location code directly in their records at the time of registration, the need for this difficult, retrospective matching would disappear. This simple change would eliminate a major source of uncertainty, allowing planners and researchers to link childcare data with other public records with confidence. Until that happens, the study provides a clear guide on how to navigate the current limitations, showing that a careful combination of different methods can help reveal the true map of childcare provision across England.
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