Create a map

Create and configure map visualizations on the Polyteia Platform.

Maps let you show your data directly on a map - either as colored regions, individual points, or heatmaps.

Each layer type helps visualize different information:

• Area maps are ideal for comparing values by region

• Scatter maps highlight individual locations

• Heatmaps show concentration or intensity

After creation, you can label shapes, color by value, and customize tooltips. Tooltips are small information windows that open when you hover over an area or point.

1

Prepare data

Before you can create a map visualization, you need a dataset with 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 link other datasets with numerical or descriptive values to visualize.

Upload a dataset with geographic information

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

• Upload a .geojson file (for shapes) or .json file with GeoJSON-style points. Learn more about GeoJSON and JSON here

• The Polyteia Platform automatically recognizes valid geographic information and creates a column titled geometry or geom

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

Example: Berlin district geometries with polygon data in binary representation.

Join dataset

In most cases, you'll want to combine the shape data with other data, like counts or labels. You can do this directly in the Query area when creating 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 right join to keep all regions even if no data match is found

Example: Join Berlin district geometries (geom, Gemeinde_name) with hotel audit results (proznt_bestanden, Anzahl_Kontrollen_gesamt) via Gemeinde_name.

2

Define columns

After preparing your data and performing joins, the next step is to define which columns will be used in your map visualization. This is done in the Query area of the insight editor.

You need:

• a column with geographic information (created from GeoJSON or a converted point field)

• a label or region name column (for context)

• one or more numeric metrics to visualize

Column type	Purpose	Example
Geometry (geographic information)	Draws shapes or plots points	geom
Region label	Optional, used as label or tooltip	Gemeinde_name
Metric	Main value used for coloring (or weighting)	proznt_bestanden
Optional breakdown	Additional information in tooltips	Anzahl_Kontrollen_gesamt, Anzahl_Kontrollen_bestanden

Required column types

In the Berlin hotel audit example, the following columns were selected:

• geom: contains the polygon for each district

• Gemeinde_name: district name shown in tooltips or labels

• Anzahl_Kontrollen_gesamt: total audits

• Anzahl_Kontrollen_bestanden: passed audits

• proznt_bestanden: the percentage metric to color the map

You can add or remove fields via the Add column dropdown menu.

Berlin hotel audits

A map visualization only uses the geometry column and a numeric column for coloring - additional columns are displayed as tooltips when users hover over shapes or points.

3

Select map type

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

Layer type	Description
Areas	Color-coded regions based on polygon shapes (GeoJSON).
Scatter	Individual data points shown as circles on the map.
Heatmap	Density of points visualized using color intensity.

You can mix these if needed. Most map visualizations, however, use one layer at a time.

Name the layer

In the layer panel, you can enter a layer name (e.g. Audit success rate). This name is automatically used as a legend title in the chart and helps viewers understand what the color gradient represents.

Areas (polygons)

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

After selection, configure:

• Geometry (GeoJSON): Choose the column containing the polygon data

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

• Value: Choose a numeric metric to color each shape (e.g. proznt_bestanden)

• Tooltip fields: Add optional metrics like total audits for more context

Optionally, you can enable Show value label to display the value inside the region and Use translucent colors for better background contrast.

Scatter (points)

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

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

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

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

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

Layer configuration options

Field	Function
Geometry (GeoJSON)	Automatically filled with the geom column containing point data.
Name	Label shown in tooltip (e.g. name, Gemeinde_name).
Point size	Optional numeric field to size points (e.g. number of visits).
Category	Optional field to color points by category (e.g. type of facility).
Cluster points	Automatically groups nearby points at low zoom (enabled by default).
Show point size values	Toggle to show the size metric above each point (if point size is set).
Use translucent colors	Makes overlapping points visually more recognizable.
Tooltip fields	Additional fields shown on hover (e.g. address, rating, type).

Automatic clustering

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

• groups nearby points into bubbles at low zoom

• shows the number of items in each cluster

• expands automatically when zooming in

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

Heatmap

Use the heatmap when you want to visualize data density on a map - like complaints, visits, events, or any location-based datasets with high volume. Instead of showing individual points, the heatmap draws a smooth color gradient that highlights where data points are concentrated.

When to use heatmaps:

• Your data contains many points in close proximity

• You want to show hotspots, not individual items

• Labels and tooltips are not needed

Example

You have a dataset of hotel locations and are interested in where most hotels are located - not individual entries.

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

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

3. Set:

   • Geometry (GeoJSON): Choose the column with your point data (e.g. Koordinaten or geom)

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

If you don't set an intensity column, all points count equally. If you use an intensity column (e.g. number of violations), denser value sets will have more visual impact.

Limitations

• Heatmaps do not support labels, tooltips, or individual point formatting

• They are best suited for overview visualizations, not detailed analysis