vgridpandas 1.0.7

Integration of Vgrid DGGS into Pandas and GeoPandas

VgridPandas

VgridPandas - Integrates Vgrid DGGS with GeoPandas and Pandas, inspired by H3-Pandas

VgridPandas supports a wide range of popular geodesic DGGS including H3, S2, A5, rHEALPix, Open-EAGGR ISEA4T, EASE-DGGS, DGGAL, DGGRID, QTM, as well as graticule-based DGGS such as OLC, Geohash, MGRS, GEOREF, TileCode, Quadkey, Maidenhead, and GARS.

Full VgridPandas DGGS documentation is available at vgridpandas document.

To work with Vgrid in Python or CLI, use vgrid package. Full Vgrid DGGS documentation is available at vgrid document.

To work with Vgrid DGGS in QGIS, install the Vgrid Plugin.

To visualize DGGS in Maplibre GL JS, try the vgrid-maplibre library.

For an interactive demo, visit the Vgrid Homepage.

Installation

pip

pip install vgridpandas --upgrade

Key Features

• Latlong to DGGS: Convert latitude and longitude coordinates into DGGS cell IDs.
• DGGS to geo boundary: Convert DGGS cell IDs into their corresponding geographic boundaries.
• (Multi)Linestring/ (Multi)Polygon to DGGS: Convert (Multi)Linestring/ (Multi)Polygon to DGGS, supporting compact option.
• DGGS binning: Aggregate points into DGGS cells, supporting common statistics (count, min, max, etc.) and category-based groups.

Usage examples

Latlong to DGGS

import pandas as pd
from vgridpandas import h3pandas
df = pd.DataFrame({'lat': [10, 11], 'lon': [106, 107]})
resolution = 10
df = df.h3.latlon2h3(resolution)
df

| h3              |   lat |   lon |
|-----------------|-------|-------|
| 8a65a212199ffff |    10 |   106 |
| 8a65b0b68237fff |    11 |   107 |

DGGS to geo boundary

df = df.h3.h32geo()
df

| h3              |   lat |   lon | geometry        |
|-----------------|-------|-------|-----------------|
| 8a65a212199ffff |    10 |   106 | POLYGON ((...)) |
| 8a65b0b68237fff |    11 |   107 | POLYGON ((...)) |

(Multi)Linestring/ (Multi)Polygon to DGGS

import geopandas as gpd
from vgridpandas import s2pandas

gdf = gpd.read_file('https://raw.githubusercontent.com/opengeoshub/vopendata/refs/heads/main/shape/polygon.geojson')
resolution = 18
gdf_polyfill = gdf.s2.polyfill(resolution, compact = True, predicate = "largest_overlap", explode = True)
gdf_polyfill.head()
gdf_polyfill = gdf_polyfill.s2.s22geo("s2")
gdf_polyfill.plot(edgecolor = "white")

DGGS Binning

import pandas as pd
import geopandas as gpd
from vgridpandas import a5pandas
resolution = 15
df = pd.read_csv("https://raw.githubusercontent.com/opengeoshub/vopendata/refs/heads/main/csv/dist1_pois.csv")
# df = gpd.read_file("https://raw.githubusercontent.com/opengeoshub/vopendata/refs/heads/main/shape/dist1_pois.geojson")
stats = "count"
df_bin = df.a5.a5bin(resolution=resolution, stats = stats, 
                    # numeric_column="confidence",
                    # category_column="category",
                    return_geometry=True)
df_bin.plot(
    column=stats,        # numeric column to base the colors on
    cmap='Spectral_r',   # color scheme (matplotlib colormap)
    legend=True,  
    linewidth=0.2         # boundary width (optional)
)

Further examples

For more examples, see the example notebooks.