{"version":"1.0","type":"rich","title":"Demonstration of Uber's H3 DGGS which I did at the Palmerston North Regional GIS Forum (May 2022)","description":"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.","provider_name":"VizHub AI Studio","provider_url":"https://next.vizhub.com","html":"<iframe src=\"https://next.vizhub.com/viz/gist-6a4b0295534c501e7d008645706fbe12?mode=embed\" width=\"960\" height=\"500\" scrolling=\"no\" frameborder=\"1\"></iframe>","width":960,"height":500,"cache_age":3600}