GeoJSON boundary data for business intelligence
Turn sales, service, population or performance data into clear regional reports. Choose a boundary level, check its matching fields and load the map into a Power BI visual.
Country, county, council, constituency or postcode.
Join your report data by a shared name or code.
Use your measure to compare each boundary area.
Start with the Visual
Power BI supports GeoJSON in more than one way. The best boundary detail depends on how you want the report to work.
Upload a custom GeoJSON or TopoJSON map, add a matching Location field and use a measure to colour each region.
Best starting choices: postcode areas, local authorities, US states, world countries, constituencies and NHS ICBs.
Use a GeoJSON reference layer when the report needs roads, places, zoom levels or more detailed location context.
Strong candidates: postcode districts, postcode sectors, US counties and local service boundaries.
Compatibility guidance was checked against the current Microsoft Shape Map documentation and Azure Maps reference-layer documentation.
Recommended from Our Catalogue
These recommendations combine historic interest in equivalent catalogue products with the location types used in Power BI reports. They are demand signals, not a guarantee of future sales.
A practical level for sales territories, customer distribution, delivery planning and regional performance.
Detailed local coverage for territory planning, direct marketing, service demand and customer analysis.
A useful reporting level for public services, property, funding, operations and local performance.
A clear first choice for national sales, branch, population and performance dashboards.
County names and FIPS fields support more detailed public, commercial and operational analysis.
Useful for cross-border delivery, customer, service and territory reports across Ireland.
Compare revenue, customers, operations or performance across a consistent worldwide layer.
Map election results, demographics, casework, campaign data and constituency performance.
Compare healthcare activity, resources and outcomes across current ICB boundaries.
A Simple Workflow
The map supplies the shapes. Your table supplies the values. One shared field joins the two parts.
Check the coverage, boundary count and sample fields. Select the least detailed map that still answers the report question.
Make one field in your data match a map property. For example, match a council code, FIPS code, country code or exact boundary name.
Add the GeoJSON to Shape Map or Azure Maps. Check unmatched areas, then use your measure to set colours and tooltips.
Clear join example
A report row with Postcode_District = YO18 must match a boundary property with the same value. A stable code is usually safer than a name because names can have different spellings.
Built for a Confident First Test
Each catalogue product shows its geographic coverage, boundary count and representative attributes. This helps you confirm that the file contains a field that can match your Power BI table.
Frequently Asked Questions
Start with the map visual, boundary level and matching field. These three choices prevent most problems.
Ask About Your ReportYes. Power BI Shape Map supports custom GeoJSON and TopoJSON maps. The Azure Maps visual can also use GeoJSON as a reference layer.
Use a field that exists in both datasets. Stable codes are usually best. Names can also work when spelling and formatting are the same.
Use Shape Map for a focused region-colouring visual. Use Azure Maps when you need a geographic base map, more location context or a detailed reference layer.
Filter the map to the area used in the report, simplify the geometry or convert it to TopoJSON. Microsoft states that Shape Map plots a maximum of 1,500 data points.
Not for a normal Power BI upload. If you need a different format or a smaller file, use our browser-based conversion tool or contact us.
Not sure which boundary matches your Power BI table?
Send us a few sample location values and tell us which map visual you plan to use. We can help you identify the closest dataset before you buy.