Starbucks Food & Beverage Locations Dataset – UK

Quick links: Dataset Summary Methodology Download Data Quality Regional Distribution Brand Bundle Related Datasets Use Cases FAQ Analyze with AI Starbucks is a multinational chain of coffeehouses and roastery reserves headquartered in Seattle, Washington, USA. As the world's largest coffeehouse chain, it has a massive presence across the UK, offering espresso-based beverages, teas, and food items.There are 1,440 Starbucks Food & Beverages as of 27 August 2026 in the UK. This dataset is compiled and maintained by Geolocet and provides a complete, geocoded list of all Starbucks locations, including full address details, administrative divisions, and precise WGS84 latitude/longitude coordinates - structured for GIS, retail analytics, mapping, and AI/RAG workflows. Dataset Summary Dataset Coverage: 1,440 Starbucks food & beverages in the UK Contents: Coordinates, addresses, postal codes, administrative divisions, and contact details File Format: Fully geocoded CSV dataset (UTF-8) Free Sample: Instantly accessible dataset to verify structure and data quality Use Cases: Suitable for GIS, retail analytics, site selection, and AI/RAG workflows Last Updated: 27 August 2026 Dataset Methodology: This dataset is compiled from publicly available business listings, official company sources, and geospatial validation workflows. Automated quality checks and manual analyst reviews are applied to improve coordinate precision, address standardisation, duplicate detection, and overall analytical consistency.It is periodically reviewed and updated to reflect known network changes, closures, relocations, and newly identified locations. Dataset fields included in the CSV: GUID Title Latitude Longitude Street No Street Town Local authority District Postal Code Address Wheelchair Phone Website Opening hours Data Quality Scorecard Geospatial Accuracy: 98%+ (Verified WGS84 Coordinates) Contact Details (Phone)51% Web Address55% Opening Hours61% Data Preview: Sample geospatial records from the Starbucks dataset in UK ID Location Title Latitude Longitude Postal Code Full Address a6e6789... Starbucks Coffee 52.351446 -0.180743 PE29 7EG PE29 7EG, Cambridgeshire, United Kingdom 82cb70b... Starbucks Coffee 52.204027 0.121221 CB2 3BJ Saint Andrew's Street, CB2 3BJ, Cambr... 6ff8b2f... Starbucks 51.426433 0.102060 DA14 6DW 77a High Street, DA14 6DW, Greater Lo... 8567feb... Starbucks Coffee Company 52.404587 0.720780 IP24 3QL London Road, IP24 3QL, Norfolk, Unite... 8a2dc7e... Starbucks 50.933146 -1.508165 SO40 3XF Eddystone Road, Totton, SO40 3XF, Ham... Note: Only a subset of the full dataset fields are displayed here. Download the free sample (option above) to view all fields and verify the data structure. Why download from Geolocet? Instant download - full dataset available immediately after purchase, no waiting, no manual fulfilment Free sample first - verify structure, fields, and coordinate precision before you commit Analysis-ready CSV - clean, standardised, and compatible with Excel, Python, QGIS, Power BI, and PostgreSQL out of the box Regularly updated - last updated 27 August 2026 ✅ Data looks right? Add to cart ↑ - or download the free sample first.Regional Distribution BreakdownLooking at the geographic distribution, the highest concentration of Starbucks locations in the UK is found in England (1216 sites). This is followed by Scotland (120 sites) and Wales (58 sites).Learn more about the brand network in our report: View ReportAlso available for the UK Brand bundle Top 23 Fast Food and Restaurants Brands in the UK - €560 All major chains in one standardised dataset. Best for competitive benchmarking, network analysis, and market sizing across the leading brands. View Top Brands dataset → Full market coverage All Fast Food and Restaurants Locations in the UK - complete POI dataset Includes everything in the brand bundle plus independent operators, smaller chains, and local businesses not covered by the top brands. Best for full market mapping, territory planning, and white-space analysis. View full POI dataset → Related geospatial datasets Demographics dataset for the UK: Overlay demographic indicators to deeply understand the population structures and household types surrounding these Starbucks locations. Explore demographics data insights for the UK: Methodology, use cases, and definitions explaining how demographic metrics support your location intelligence workflows. Income indicators dataset for the UK (small-area income analysis): Download an analysis-ready dataset on income distribution across small geographic units - ideal for segmentation, customer analytics, location planning, and socio-economic scoring. Income distribution and socio-economic patterns in the UK - expert analysis: A detailed blog post explaining income geography, disparities, and how income-based indicators can support location intelligence and market analytics. Explore a rich library of the UK-specific datasets on our dedicated country page: detailed demographics, wealth indicators, multi-level boundaries, and a broad spectrum of retail POIs. View demographics, retail POI, and administrative boundary datasets for the UK Need the data in another format?We can deliver this dataset in alternative formats upon request (GeoJSON, Shapefile, Excel, PostgreSQL import files, etc.). Contact us at contact@geolocet.com.Who uses this data? Smart City Research: Academic researchers analyzing commercial density, urban growth patterns, and spatial economics. Economic Development: Agencies identifying underserved neighborhoods or "retail deserts" for targeted commercial investment. Last-Mile Delivery Routing: E-commerce and food-delivery planners optimizing localized courier routes and dispatch proximity. Geofencing & Targeted Advertising: Media buyers executing hyper-local, location-based mobile ad campaigns around specific brand locations. Trade Area Marketing: Agencies planning direct-mail or localized out-of-home (OOH) billboard campaigns near high-density retail clusters. Franchise Expansion: Network development teams assessing market saturation and mapping open territories for new franchisees. Commercial Brokerage: Real estate brokers validating commercial property valuations based on proximity to major retail anchors. Vendor Distribution: FMCG and wholesale suppliers identifying specific retail locations for direct-store-delivery (DSD) pitching. CRM Data Enrichment: RevOps teams appending accurate, standardized contact details and coordinates to existing Salesforce/HubSpot records. Frequently Asked Questions Q: Is this dataset suitable for market analysis? A: Yes. The dataset is designed for retail analysis, competitor benchmarking, site selection, market coverage studies, and geospatial intelligence workflows. Q: Can I use this dataset for proximity analysis? A: Yes. The geocoded coordinates are suitable for drive-time analysis, catchment modeling, nearest-neighbor analysis, and accessibility studies. Q: Can I integrate this dataset into a PostgreSQL/PostGIS database? A: Yes. The dataset structure is compatible with PostgreSQL/PostGIS and other relational spatial databases. Q: How accurate are the coordinates? A: Coordinates undergo automated validation and manual quality review processes to improve positional accuracy and analytical reliability. Q: Is the dataset standardized for analytics workflows? A: Yes. Address formatting, administrative areas, and geospatial fields are standardized to improve consistency across analytical environments. Q: Can I preview the dataset before purchasing? A: Yes. A free sample is available so you can evaluate the structure, fields, and geospatial quality before purchase. Q: Are the datasets suitable for machine learning workflows? A: Yes. The structured tabular format and standardized coordinates make the datasets suitable for machine learning and predictive analytics applications. Analyze this data with AIUse these prompts with ChatGPT, Claude, or Gemini to extract strategic insights from this dataset: "Analyze this Starbucks dataset to identify underserved regions in the UK for potential market expansion." "Compare the spatial distribution of Starbucks locations against major competitor clusters to identify overlap and whitespace opportunities in the UK." "Generate regional density heatmaps showing where Starbucks has the strongest and weakest retail presence in the UK." Disclaimer: All brand logos and trademarks displayed are the property of their respective owners and are used strictly for identification purposes. This product consists of geospatial location data only; no images, logos, or trademark rights are included in the downloadable files.