Dot density across Chicago
Small translucent dots show the source observations themselves. Dense clusters become darker through overlap while isolated observations stay visible, offering a useful alternative to continuous smoothing or cell aggregation. Each dot is one synthetic record rather than a population conversion.
Start an editable copy with this data and your current map styling.
What to look at
- Small dots preserve individual observations without turning them into large bubbles.
- Partial opacity makes concentration visible through overlapping marks.
- The same points can be switched to a continuous heatmap in the studio.
Source columns
These fields describe the example data. CSV and Excel use first-row headers; GIS formats store geometry and feature properties. Required geometry depends on the map type.
| Column | Type | Example |
|---|---|---|
| id | text | chicago-dots-10 |
Rebuild it with your own data
- 1
Get your CSV file ready
The example fields are id. For CSV or Excel, put headers in the first row. GIS files carry geometry and feature properties. Prepare any grid, catchment or route geometry before import.
- 2
Drop it into the studio
MapCrafter detects the geometry, counts the features, and shows the layers it found.
- 3
Style the imported layer
Pick which column drives colour, and which drives size or width where relevant.
- 4
Pick a basemap theme
Ivory is used here. 15 other basemaps are available, including official OpenFreeMap styles. Switch using Layers & basemap.
- 5
Share or export
Publish a link, embed the map, or export a PNG at print resolution.
Questions about this example
- What file format does this dot density use?
- This example illustrates CSV data. The live preview uses GeoJSON geometry. MapCrafter reads CSV directly, along with Excel, CSV, KML, KMZ, GPX, GeoJSON, TopoJSON and zipped Shapefile. No conversion step and no desktop software.
- What columns does the source data need?
- The file used here has 1 columns: id. For tabular imports, supply coordinate columns, WKT or GeoJSON geometry, or address columns for an explicit geocoding step. Polygon and line files carry their geometry separately from these attributes.
- Can I use my own data with this map type?
- Yes. Open the studio, drop in your file, and MapCrafter detects the geometry and applies a starting style. Then style the layer and choose which property drives colour, size or width. The studio can render a point heatmap, but grids, routes and catchment polygons must be prepared before import.
- Can I export or share the finished map?
- Each map can be published to a short share link, embedded in another page with an iframe, or exported as a print-resolution PNG. The cleaned-up geometry can also be exported as GeoJSON. All of that is available on the free tier.
- Where does the data in this example come from?
- Seeded synthetic observations around four Chicago coordinate anchors. One dot is one simulated record; positions and concentrations are illustrative.
More heat maps examples
Gallery (314)- CSVBinned density over LondonTen thousand observations would overplot into a solid blob. Binning them into hexagons aggregates first and colours second, which is the only way a reader can see where a dense point dataset actually concentrates.
- CSVTokyo binned densityA dark-theme hexbin over the Kantō plain, with the highest-value cells reaching a pale yellow. On a dark basemap the ramp has to run towards light, because a dark-to-dark ramp has nowhere left to go.
- GeoJSONAgricultural survey binsA wide, sparse region binned at a coarse cell size. Cell size is the single most consequential choice in a binned map: too small and the pattern dissolves into noise, too large and the structure vanishes into a single blob.
Last updated 2026-10-22