Supported Geometry Types
GEOS BI supports a variety of geometry types
GEOS BI offers robust support for multiple geometry types, such as circles, lines, and polygons.
Lines (Defined by two coordinates)
To demonstrate the use of line geometries, we’ll use the following dataset:
| id | name | width | start_point_latitude | start_point_longitude | end_point_latitude | end_point_longitude |
|---|---|---|---|---|---|---|
| 1 | Rhine River Segment | 500 | 51.652249 | 6.6003785 | 49.512211 | 8.4361953 |
| 2 | Elbe River Segment | 700 | 52.104592 | 11.662477 | 50.916128 | 14.152070 |
| 3 | Danube River Segment | 1000 | 48.577033 | 13.460849 | 49.021126 | 12.123448 |
You can download the dataset here: lines_01.csv
To visualize lines on the map, simply put the four location columns (start and end coordinates) into the first four data fields of the visual. The result will look like this:
Multi-LineStrings, Polygons and other complex geometries
For more complex geometries, such as Multi-LineStrings or Polygons, GEOS BI supports industry-standard formats like GeoJSON and WKT (Well-Known Text).
To use these formats, place the GeoJSON or WKT data into the designated WKT/GeoJSON data field.
GeoJSON
Here’s a sample dataset using GeoJSON:
| id | name | area_km2 | population | geometry |
|---|---|---|---|---|
| 1 | Berlin Central District | 39.47 | 366408 | {"type": "Feature", "properties": {}, "geometry": {"type": "Polygon", "coordinates": [[[13.366,52.537],[13.366,52.500],[13.445,52.500],[13.445,52.537],[13.366,52.537]]]}} |
| 2 | Munich Old Town | 310.43 | 1488206 | {"type": "Feature", "properties": {}, "geometry": {"type": "Polygon", "coordinates": [[[11.565,48.146],[11.565,48.129],[11.589,48.129],[11.589,48.146],[11.565,48.146]]]}} |
| 3 | Hamburg Harbor | 755.22 | 1841179 | {"type": "Feature", "properties": {}, "geometry": {"type": "Polygon", "coordinates": [[[9.936,53.548],[9.936,53.518],[10.030,53.518],[10.030,53.548],[9.936,53.548]]]}} |
Download the dataset here: geojson_01.csv
When visualized in Power BI, the data appears as follows:
WKT
For WKT-formatted data, here’s an example dataset:
| id | city | stations | geometry |
|---|---|---|---|
| 1 | Cologne | Cologne Central, Cologne Messe/Deutz | MULTIPOINT ((6.958307 50.941357), (6.982230 50.940326)) |
| 2 | Dortmund | Dortmund Central, Dortmund Stadthaus | MULTIPOINT ((7.458076 51.517781), (7.466944 51.514167)) |
| 3 | Essen | Essen Central, Essen Süd | MULTIPOINT ((7.013750 51.451389), (7.008611 51.441944)) |
You can download the dataset here: wkt_01.csv
In Power BI, the WKT data is visualized as shown below:
