Before diving into the specifics of the UK Places data launch, it’s important to understand why US vs UK POI differences matter in practice. Differences in language, postal systems, density, and spatial geometry directly affect how points of interest data are interpreted in location intelligence, global modelling, and cross-market analysis. Treating POI data as structurally uniform across geographies can lead to misclassification, skewed density metrics, and integration challenges when scaling analytics beyond a single country.
This month we launched our long-awaited, highly anticipated UK Places data. While we’ve been curating points of interest (POI) data in the US for the past few years, our customers have been asking for the same in the UK. Our UK data offering provides our Places and Geometry datasets for England, Scotland, and Wales, covering over 1.3M places and more than 500 brands.
We have big aspirations at SafeGraph. We aim to one day provide places data for the entire world. Our expansion into Great Britain taught us a lot about how places, and as a result places data, differ by geography. We encountered five main technical differences between US and UK POIs.
American jargon does not always translate to British jargon very well, and our machine learning models required some lessons in the Queen’s English to make use of the metadata attached to UK data sources. For example, humans may know that pubs and inns in Great Britain are the same as bars in the US, or high streets are the same as shopping strips, but computers need to learn these things.
This difference becomes especially important when performing POI data comparison between the US and UK, as category-level inconsistencies can propagate errors through classification models, filters, and downstream analytics.
Best practice: Normalize category labels and naming conventions across regions before training models or running comparative analyses.
Postal codes across the pond are much more granular than their counterparts in the US, so much so that some high-rises comprise several postal codes. Our US POIs represent 37000 distinct postal codes, while our UK POIs represent a whopping 609 thousand.
These points of interest data differences have a direct impact on spatial joins, aggregation logic, and geographic rollups, particularly for users accustomed to ZIP code–level analysis in the US.
Best practice: Treat UK postal codes as high-resolution spatial identifiers and adjust aggregation logic accordingly rather than mapping them one-to-one with US ZIP codes.
Great Britain is crowded. POIs are more densely co-located in the UK than in the US. Great Britain has 14.9 POIs per square mile, while the US only has 1.7 POIs per square mile. Of course, the US has a much larger area than Great Britain, but the resulting increase in POI density was a new challenge for us as we built out the UK Places data.
This geospatial data difference affects proximity analysis, clustering, and trade area modelling, especially when applying US-calibrated assumptions to UK environments.
Best practice: Recalibrate distance thresholds and clustering parameters to account for higher POI density UK vs US conditions.
With these crowded, co-located POIs come smaller spaces. The average polygon size for branded, “OWNED_POLYGON” POIs in Great Britain is 934 sq. meters. It’s roughly double that in the US, at 1,917 sq. meters.
Smaller polygon sizes can influence visit attribution, overlap calculations, and spatial weighting, particularly when comparing activity across markets.
Best practice: Normalize polygon-based metrics when comparing performance or behaviour across US and UK datasets.
Great Britain’s architecture is much older than America’s (some buildings date back to 3000 BCE), making distinct buildings much harder to delineate. We still strive for world-class polygons that define even the most obscure demarcations between adjacent buildings, but it’s a much taller order, and there will inevitably be more “SHARED_POLYGONS” in the UK data as a result. Users can expect our “% OWNED” polygon metric to increase over time as we continue to learn more about this.
Building places data in the UK has been a fascinating lesson in geography, history, and culture, and we are excited to see what other regional differences we encounter as we continue to expand.
These structural constraints introduce additional complexity for footprint-level attribution and ownership-based analysis.
Best practice: Use ownership metrics and supporting attributes alongside geometry when analysing shared-building environments.
US vs UK POI differences extend beyond simple formatting or terminology changes. They shape how geospatial data differences influence real analytical use cases, from location intelligence and market analysis to global modelling and data integration. Accounting for these points of interest data differences allows teams to build workflows that scale across regions while maintaining analytical accuracy and consistency.
1. Why are US vs UK POI differences important for analytics?
They affect how data is categorized, aggregated, and interpreted across regions, which can impact modelling accuracy.
2. How does POI density UK vs US affect analysis?
Higher density in the UK requires different clustering and proximity assumptions to avoid distorted insights.
3. Are UK postal codes comparable to US ZIP codes?
No. UK postal codes are far more granular and should be handled differently in spatial workflows.
4. Do polygon sizes affect location intelligence outcomes?
Yes. Smaller polygons can influence visit attribution, overlap analysis, and spatial metrics.
5. What causes more shared polygons in the UK?
Older architecture and tightly packed buildings make footprint delineation more complex.
6. How should teams adapt global POI workflows?
By normalizing language, recalibrating spatial thresholds, and accounting for regional geometry differences.
7. Is POI data comparison between US and UK suitable for benchmarking?
Yes, but only when workflows are adjusted to reflect structural differences in the data.
