Download Kuwait map in H3 Index format
A map built from OpenStreetMap data, ready to download
Customize the map
Open this place's area in the constructor and adjust everything to your needs
Open in constructor →Map passport
What this map contains by default
This is how the map is built as-is — every setting can be changed in the constructor.
About H3 Index
Map of Kuwait in H3 Index — an export of real OpenStreetMap data for the selected area.
H3 is Uber's hierarchical hexagonal spatial index. Each map feature is aggregated into an H3 cell at the requested resolution; the export returns JSON with real H3 indices once the worker image is rebuilt with h3>=4.0. Until then the exporter fails honestly — we no longer emit fallback_7_7690_5336 synthetic strings.
Which layers are in the file
This file for the selected area contains:
- Roads
- Water
- Greenery
Coordinate systems
WGS 84 (EPSG:4326), selectable in the wizard
Units
Meters (projected in the chosen coordinate system)
| Category | GIS |
| File extension | .json |
| Tier | Open Beta |
How to use H3 Index for Kuwait
- Python — read the JSON, then use the h3-py library: h3.cell_to_boundary gives the cell outline, h3.grid_disk gives the neighbors, and pandas assembles the summary tables.
- deck.gl — the H3HexagonLayer takes indexes directly, so a density map is built without creating polygons first.
- Kepler.gl — load a table with a column of indexes and pick the H3 layer type: an interactive heat map comes together in a couple of clicks.
Programs that open H3 Index
FAQ: H3 Index for Kuwait
Which library is used?
h3>=4.0 (Uber h3-py). Without it the exporter fails instead of emitting synthetic indices.
What is inside the exported file?
JSON with the H3IndexCollection structure: the resolution, the extent of the area and an array of cells. Every cell carries an H3 index, the total number of features that fell into it and a breakdown by layer; on request the cell boundary is added as a GeoJSON polygon.
Which resolution should I choose?
Seven by default — a cell with an edge of about 1.2 km, which is district level. The larger the number, the finer the hexagon: eight and nine suit city blocks, ten and above suit individual streets. At fine resolutions the number of cells grows enormously and the file grows with it.
How does a feature end up in a cell?
By centroid: the feature's geometric center is taken, and the whole feature is assigned to a single cell. A long street is not cut between hexagons — it lands in the cell of its center, which is worth keeping in mind when reading density.
Can I get CSV or Parquet?
The export delivers JSON. A table is one line away: pandas.json_normalize over the array of cells, then save to CSV or Parquet.
