Create a map

Preparing your data

Before you can create a map chart, you need a dataset that includes geographic information. This can come from a GeoJSON file with shapes (points, lines or polygons) or a JSON file with locations (points). You can then join other datasets with numeric or descriptive values to visualize.

Upload a dataset with geometry

To upload your base map:

• Go to your solution and follow the steps in the Create dataset article.

• Upload a .geojson file (for shapes) or .json file with GeoJSON-style points.

• After upload, the Polyteia Platform will automatically detect valid geometry and create a column called geometry or geom.

You can check the result by opening the dataset in Explore data—the geometry column will appear in column "geom" as structured data (e.g. { "type": "Polygon", ... }) or binary representation of the data (e.g. BQQAAROVNB+jFSQ...).

Join another dataset

In most cases, you'll want to combine the shape data with performance data, like counts, rates, or labels. You can do this directly in the Query tab when building your insight:

1. Click + Join dataset.

2. Choose the dataset to join (e.g. audit results, statistics).

3. Select the matching column (e.g. Gemeinde_name, Schluessel_gesamt, or district ID).

4. Use a left join to keep all regions even if no data match is found.

Selecting columns

After preparing your data and performing any necessary joins, the next step is to define which columns will be used in your Maps chart. This is done in the Query tab of the Insight Editor.

You'll typically need:

• a geometry column (created from GeoJSON or a converted point field);

• a label or region name column (for context);

• one or more numeric metrics to visualize.

Required column types

Example setup

In the Berlin hotel audit example (see screenshot), the following columns were selected:

• geom: contains the polygon for each district.

• Gemeinde_name: district name to show in tooltips or labels.

• Anzahl_Kontrollen_gesamt: total inspections.

• Anzahl_Kontrollen_bestanden: inspections passed.

• proznt_bestanden: the percentage metric to color the map.

You can add or remove fields using the Add column dropdown.

Choosing the map layer type

In the Chart tab, click + Add map to choose how your data should be visualized. You'll see three options:

Name the layer

At the top of the layer panel, you can enter a layer name (e.g. Audit success rate). This name is automatically used as a legend title on the chart, helping viewers understand what the color gradient represents.

Areas (polygons)

Use this when your dataset contains shapes like city districts, regions, or zones from a GeoJSON file.

Once selected, configure:

• geometry (GeoJSON): choose the geom column that contains the polygon data;

• label: select a name column, e.g. Gemeinde_name (appears in tooltip and optionally inside the shape);

• value: select a numeric metric to color each shape (e.g. proznt_bestanden);

• tooltip fields: add optional metrics like total audits or pass counts for more context.

Scatter (points)

Use the scatter layer to display individual points - like hotels, schools, or facilities - on the map. Each row in your dataset represents one location, and the coordinates must be formatted as a Point geometry.

This layer works especially well with simple JSON files where each entry looks like this:

{
  "name": "Bright Nest Hotel",
  "Koordinaten": {
    "type": "Point",
    "coordinates": [13.4078, 52.5114]
  }
}

The Polyteia Platform automatically detects fields like Koordinaten or location and creates a geom column you can use in the chart.

How to configure

In the Chart tab, click + Add map → select Scatter

Layer configuration options

Automatic clustering

The Cluster points toggle is enabled by default. This feature:

• groups nearby points into bubbles at low zoom;

• displays the number of items in each cluster;

• expands automatically as you zoom in.

You can disable it if you prefer to show each point individually at all zoom levels.

Heatmap

Use the heatmap layer when you want to visualize data density across a map—such as complaints, visits, events, or any high-volume, location-based records. Instead of showing individual dots, it draws a smooth color gradient that highlights where data points are concentrated.

When to use heatmaps

• Your data includes many points in close proximity.

• You want to show hotspots, not individual items.

• Labels and tooltips are not needed.

Example use case

You have a dataset of hotel locations and you're interested in where most hotels are - not in individual records.

How to configure

1. In the Chart tab, click + Add map and choose Heatmap.

2. Name the layer (e.g. Noise complaints) - this title appears in the legend.

3. Set:

   • geometry (GeoJSON): Select the column with your point data (e.g. Koordinaten or geom);

   • Intensity: Optional numeric value used to weight how strongly each point contributes to the heatmap. Default is 5.

Limitations

• Heatmaps do not support labels, tooltips, or individual dot styling.

• They are best used for overview visualizations, not detailed inspection.