Skip to main content
100%

Demonstration of Uber's H3 DGGS which I did at the Palmerston North Regional GIS Forum (May 2022)

✓ Published0🌍 Public
Aalpha-beta-soup
Last edited May 8, 2022
Created on May 8, 2022

This example demonstrates Uber’s H3 hexagonal hierarchical geospatial indexing system (DGGS) in Python, using a Jupyter notebook to explore cell resolution and coordinate precision. It converts a point for “The Chalet” into an H3 cell ID via `h3.geo_to_h3`, then reverses it with `h3.h3_to_geo` to show information loss, and visualizes the cell boundary as a polygon using `h3.h3_to_geo_boundary`, `shapely.Polygon`, and `geopandas` with an interactive `gdf.explore()` map. It also traces a building footprint from OpenStreetMap to illustrate polygon-to-H3 conversion, emphasizing the explicit precision control that resolution offers.

AI-generated description

Similar vizzes

Loading thumbnail…

H3 Hexagons on a Globe

This example renders the 122 base cells of Uber’s H3 hexagon grid as polygons on an orthographic globe using d3-geoPath and h3-js. The five pentagonal cells are highlighted in translucent green, while the world’s countries, drawn from the visionscarto-world-atlas TopoJSON, appear in gray beneath a graticule. Canvas rendering supports drag-to-rotate and zoom-to-scale interactions, with state managed through a VizHub pattern.

CCurran Kelleher
66% match
Loading thumbnail…

Generate some H3 polygons

This example generates H3 hexagons at multiple resolutions to visualize hierarchical spatial coverage. It shows progressively finer hexagonal grids covering Europe, France, Pays de la Loire, and Nantes, from level 0 to 12. The code uses the `geojson2h3` library to convert GeoJSON polygons into H3 cell sets via `featureToH3Set`, then transforms them into GeoJSON FeatureCollections with `h3SetToFeatureCollection`. Each output is written to a separate GeoJSON file, demonstrating how the same bounding region subdivides into smaller hexagons as the resolution increases.

TThomasG77
65% match
Loading thumbnail…

Take a GeoJSON as an input, take all H3 hexagons within and output GeoJSON hexagons with their id

This example converts a GeoJSON polygon into a set of H3 hexagons that cover it, then outputs those hexagons as a new GeoJSON FeatureCollection. It shows how the H3 library’s `polyfill` function fills an arbitrary geographic area with hexagonal cells at resolution 8, while each output hexagon retains its unique H3 identifier in its properties. The code uses `h3.h3_to_geo_boundary` with `geo_json=True` to generate each hexagon’s polygon coordinates, then writes the result to a file. The input polygon is a hardcoded region in the San Francisco Bay Area.

TThomasG77
63% match
Loading thumbnail…

Geo spatial points with H3

A map of India displays a geo-spatial point distribution, with the region of Jammu & Kashmir highlighted by a grid of hexagonal cells generated using the H3 library. The visualization uses D3.js v5 to render the map and points as SVG elements, with a Mercator projection applied via `d3.geoPath`. User-supplied CSV data is aggregated by rounding coordinates to find duplicate points and increment their count, with zoom interactions enabled through `d3.zoom`.

Aarun-subba
60% match
Loading thumbnail…

Gist 34bad0b124f4effdf99d5555cb3222d8

This example shows how spatial relationships between geographic features can be explored through an interactive map. It uses D3.js with `d3.geoPath` to render country outlines from a GeoJSON source, while a force-directed layout via `d3.forceSimulation` positions non-overlapping markers for data points. The visualization computes marker placement on a Canvas, reading pixel colors with `getImageData` to detect land versus water and ensure points remain on landmasses. Hover interactions trigger a tooltip and highlight neighboring regions, with smooth transitions handled by `transition.attrTween`. The data originates from a public GitHub gist and is embedded in a Power BI report, where the canvas is updated on each zoom or pan event.

HHugoberry
60% match
Loading thumbnail…

Gist 63540e65c30a453a7e2e9b0345aa42ce

This example renders a 3D cityscape from geospatial building footprints, showing extruded building volumes colored in a uniform teal tone. It queries buildings within a given distance of a latitude/longitude point using Django’s GeoQuerySet (`outline__dwithin`) and converts each polygon outline with Shapely, then extrudes it with Trimesh’s `creation.extrude_polygon`. The scene is centered via a translation matrix, scaled, and exported as a GLB file using Trimesh’s `exchange.gltf.export_glb`. The visualization highlights building heights relative to their footprint areas.

Aandybak
60% match
Loading thumbnail…

Quick bit of Python code to calculate a 5km travel buffer given as a talk at PyConlineAU 2020!

This example shows a Jupyter notebook presentation comparing a straight-line 5km radius from a Melbourne home with the actual travel distance along road networks. The code geocodes a location using geopy’s Nominatim API, then builds a drivable network with OSMnx and calculates the reachable area using NetworkX and Shapely. An alpha shape polygon represents the true travel buffer, rendered on folium and matplotlib maps. The notebook highlights the discrepancy between Euclidean distance and road-based travel during COVID-19 lockdown restrictions.

IIsaKiko
59% match
Loading thumbnail…

Gist dd10fa64484c334e066db46d1b74959b

This example shows a geographic grid of vertical and horizontal line segments covering a small area near Shwatka, Yukon, generated from a what3words API request. The visualization uses a GeoJSON MultiLineString containing coordinates for 14 horizontal and 14 vertical lines, which together form a dense mesh of cell boundaries. The code likely renders these lines with a mapping library such as D3.js, using `d3.geoPath` to project and draw the geographic coordinates onto a canvas or SVG. The API response metadata confirms successful retrieval from what3words, with the grid representing a localized portion of their three-meter word-based location system.

GGeoWill
58% match