Mapping the World's Stadiums with Wikipedia Data

4,500+ Stadiums on a Single Map

This map contains over 4,500 stadiums from around the world—specifically the ones from our total import of over 9,000 that have an opening year on record. From 100,000-seat mega-venues to small local grounds, the data was sourced from Wikipedia and imported into Mapsemble using the API. The interesting part isn't just the data itself, but what you can do with it once it's in Mapsemble.

Importing Large Datasets with the Mapsemble API

When you're working with thousands of features, manual entry or file uploads aren't practical. The Mapsemble API is designed for exactly this scenario.

We collected stadium data from Wikipedia's public query endpoints — names, coordinates, capacities, opening years, sports, and teams — and formatted it as GeoJSON. From there, we posted the entire dataset to Mapsemble in batches using the API:

POST https://app.mapsemble.com/api/v1/map/{map}/features

Each request can contain up to 1,000 features. The API uses unique IDs per feature, so you can re-run the import at any time — existing features get updated, new ones get added, and nothing gets duplicated. This makes it straightforward to keep your map in sync with a changing data source.

Once the data lands in Mapsemble, spatial indexing kicks in. The map loads only the stadiums visible in your current viewport, so even with 4,500+ features, panning and zooming stays fast.

Range Filters: Narrowing Down by Capacity

With 4,500 stadiums on the map, you need a way to find what you're looking for. That's where filters come in — and for numeric data like stadium capacity, the Range filter is particularly effective.

The Range filter gives you a slider with two handles: set a minimum and maximum, and the map instantly updates to show only the stadiums that fall within your range. On this map, you can drag the capacity slider to isolate stadiums between, say, 40,000 and 80,000 seats — and watch the markers and cards update in real time.

The filter is fully configurable: you can set step sizes, add unit labels, define custom bounds, or let Mapsemble derive the min and max automatically from your data.

The Histogram: See Your Data Distribution at a Glance

What makes the Range filter stand out is the histogram built into it. Before you even touch the slider, you can see how your data is distributed.

The histogram divides the full range of values into 50 buckets and shows a bar chart of how many features fall into each bucket. For stadiums, this immediately reveals that the vast majority of venues have capacities under 30,000 — while only a handful exceed 80,000.

As you drag the range handles, the bars update visually: bars inside your selected range are highlighted in blue, while those outside turn gray. This gives you instant feedback on how many results your filter will return before you even apply it.

The histogram is interactive and works with D3.js under the hood. Handles snap to step values, text inputs let you type exact numbers, and the whole thing responds to other active filters on the map. If you've already filtered by country, the histogram recalculates to show the distribution for that subset only.

What to Explore

Try a few things on the map above:

• Filter by capacity — use the Range slider to find stadiums between 50,000 and 100,000 seats. Notice how the histogram shows you exactly where the data clusters
• Zoom into Europe — England, Spain, and Germany are packed with stadiums. The clustering breaks apart as you zoom in, revealing individual venues
• Check the cards — click any marker or card to see stadium details, including opening year, sports, teams, and a link to the Wikipedia article

Build Something Like This

This map demonstrates a common pattern: take a large public dataset, import it through the API, and use Mapsemble's built-in filters to make it explorable.

The same approach works for any location-based dataset with numeric fields — property listings with prices, sensors with readings, hotels with ratings. Set up a Range filter, enable the histogram, and your users can navigate the data visually without writing a single query.

Want to try it? Get started for free and import your own dataset via CSV, Excel, or the API.

Have an idea for a map? Get in touch — we'd like to hear about it.