Usage

Vgridpandas key features¶

Vgridpandas supports popular DGGSs such as H3, S2, A5, rHEALPix, DGGAL, DGGRID, Open-EAGGR ISEA4T, ISEA3H, EASE-DGGS, QTM, OLC, Geohash, GEOREF, MGRS, Tilecode, Quadkey, Maidenhead, and GARS.

You can try out vgridpandas by using the cloud-computing platforms below without having to install anything on your computer:

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.

Install vgridpandas¶

Uncomment the following line to install vgridpandas.

In [ ]:

# %pip install vgridpandas

# %pip install vgridpandas

Latlon to DGGS¶

Pandas¶

In [1]:

import pandas as pd
from vgridpandas import h3pandas

df = pd.read_csv('https://raw.githubusercontent.com/opengeoshub/vopendata/main/csv/housing.csv')
resolution = 4
df = df.h3.latlon2h3(resolution, lat_col='lat', lon_col='lon')
df

import pandas as pd from vgridpandas import h3pandas df = pd.read_csv('https://raw.githubusercontent.com/opengeoshub/vopendata/main/csv/housing.csv') resolution = 4 df = df.h3.latlon2h3(resolution, lat_col='lat', lon_col='lon') df

Out[1]:

	lon	lat	housing_median_age	total_rooms	total_bedrooms	population	households	median_income	median_house_value	ocean_proximity	h3	h3_res
0	-122.23	37.88	41	880	129.0	322	126	8.3252	452600	NEAR BAY	8428309ffffffff	4
1	-122.22	37.86	21	7099	1106.0	2401	1138	8.3014	358500	NEAR BAY	8428309ffffffff	4
2	-122.24	37.85	52	1467	190.0	496	177	7.2574	352100	NEAR BAY	8428309ffffffff	4
3	-122.25	37.85	52	1274	235.0	558	219	5.6431	341300	NEAR BAY	8428309ffffffff	4
4	-122.25	37.85	52	1627	280.0	565	259	3.8462	342200	NEAR BAY	8428309ffffffff	4
...	...	...	...	...	...	...	...	...	...	...	...	...
20635	-121.09	39.48	25	1665	374.0	845	330	1.5603	78100	INLAND	8428149ffffffff	4
20636	-121.21	39.49	18	697	150.0	356	114	2.5568	77100	INLAND	8428149ffffffff	4
20637	-121.22	39.43	17	2254	485.0	1007	433	1.7000	92300	INLAND	8428149ffffffff	4
20638	-121.32	39.43	18	1860	409.0	741	349	1.8672	84700	INLAND	8428149ffffffff	4
20639	-121.24	39.37	16	2785	616.0	1387	530	2.3886	89400	INLAND	8428149ffffffff	4

20640 rows × 12 columns

GeoPandas¶

In [ ]:

import geopandas as gpd
from vgridpandas import h3pandas

df = gpd.read_file('https://raw.githubusercontent.com/opengeoshub/vopendata/main/shape/housing.geojson')
resolution = 4
df = df.h3.latlon2h3(resolution)
df

import geopandas as gpd from vgridpandas import h3pandas df = gpd.read_file('https://raw.githubusercontent.com/opengeoshub/vopendata/main/shape/housing.geojson') resolution = 4 df = df.h3.latlon2h3(resolution) df

Out[ ]:

	longitude	latitude	housing_median_age	total_rooms	total_bedrooms	population	households	median_income	median_house_value	ocean_proximity	geometry	h3	h3_res
0	-122.23	37.88	41.0	880.0	129.0	322.0	126.0	8.3252	452600.0	NEAR BAY	POINT (-122.23 37.88)	8428309ffffffff	4
1	-122.22	37.86	21.0	7099.0	1106.0	2401.0	1138.0	8.3014	358500.0	NEAR BAY	POINT (-122.22 37.86)	8428309ffffffff	4
2	-122.24	37.85	52.0	1467.0	190.0	496.0	177.0	7.2574	352100.0	NEAR BAY	POINT (-122.24 37.85)	8428309ffffffff	4
3	-122.25	37.85	52.0	1274.0	235.0	558.0	219.0	5.6431	341300.0	NEAR BAY	POINT (-122.25 37.85)	8428309ffffffff	4
4	-122.25	37.85	52.0	1627.0	280.0	565.0	259.0	3.8462	342200.0	NEAR BAY	POINT (-122.25 37.85)	8428309ffffffff	4
...	...	...	...	...	...	...	...	...	...	...	...	...	...
20635	-121.09	39.48	25.0	1665.0	374.0	845.0	330.0	1.5603	78100.0	INLAND	POINT (-121.09 39.48)	8428149ffffffff	4
20636	-121.21	39.49	18.0	697.0	150.0	356.0	114.0	2.5568	77100.0	INLAND	POINT (-121.21 39.49)	8428149ffffffff	4
20637	-121.22	39.43	17.0	2254.0	485.0	1007.0	433.0	1.7000	92300.0	INLAND	POINT (-121.22 39.43)	8428149ffffffff	4
20638	-121.32	39.43	18.0	1860.0	409.0	741.0	349.0	1.8672	84700.0	INLAND	POINT (-121.32 39.43)	8428149ffffffff	4
20639	-121.24	39.37	16.0	2785.0	616.0	1387.0	530.0	2.3886	89400.0	INLAND	POINT (-121.24 39.37)	8428149ffffffff	4

20640 rows × 13 columns

DGGS to geo¶

In [4]:

df = df.h3.h32geo(h3_col='h3', fix_antimeridian=False)
df.plot(edgecolor='white')

df = df.h3.h32geo(h3_col='h3', fix_antimeridian=False) df.plot(edgecolor='white')

Out[4]:

<Axes: >

(Multi)Polygon to DGGS¶

In [7]:

import geopandas as gpd
from vgridpandas import s2pandas

gdf = gpd.read_file('https://raw.githubusercontent.com/opengeoshub/vopendata/main/shape/polygon.geojson')
resolution = 17
gdf_polyfill = gdf.s2.polyfill(resolution, compact=True, predicate='intersect', explode=False)
gdf_polyfill = gdf_polyfill.s2.s22geo(s2_col='s2', fix_antimeridian=False)
gdf_polyfill.plot(edgecolor='white')

import geopandas as gpd from vgridpandas import s2pandas gdf = gpd.read_file('https://raw.githubusercontent.com/opengeoshub/vopendata/main/shape/polygon.geojson') resolution = 17 gdf_polyfill = gdf.s2.polyfill(resolution, compact=True, predicate='intersect', explode=False) gdf_polyfill = gdf_polyfill.s2.s22geo(s2_col='s2', fix_antimeridian=False) gdf_polyfill.plot(edgecolor='white')

Out[7]:

<Axes: >

DGGS point binning¶

Pandas¶

In [12]:

import pandas as pd
from vgridpandas import a5pandas

resolution = 7
df = pd.read_csv('https://raw.githubusercontent.com/opengeoshub/vopendata/main/csv/housing.csv')
stats = 'max'
numeric_col = 'median_house_value'
df_bin = df.a5.a5bin(resolution=resolution,
                    lat_col='lat',
                    lon_col='lon',
                    stats=stats,
                    numeric_col=numeric_col,
                    # category_col='ocean_proximity',
                    )
df_bin.plot(
    column=f'{numeric_col}_{stats}',
    cmap='Spectral_r',
    legend=True,
    linewidth=0.2,
)

import pandas as pd from vgridpandas import a5pandas resolution = 7 df = pd.read_csv('https://raw.githubusercontent.com/opengeoshub/vopendata/main/csv/housing.csv') stats = 'max' numeric_col = 'median_house_value' df_bin = df.a5.a5bin(resolution=resolution, lat_col='lat', lon_col='lon', stats=stats, numeric_col=numeric_col, # category_col='ocean_proximity', ) df_bin.plot( column=f'{numeric_col}_{stats}', cmap='Spectral_r', legend=True, linewidth=0.2, )

Out[12]:

<Axes: >

GeoPandas¶

In [14]:

import geopandas as gpd
from vgridpandas import a5pandas

resolution = 7
df = gpd.read_file('https://raw.githubusercontent.com/opengeoshub/vopendata/main/shape/housing.geojson')
stats = 'max'
numeric_col = 'median_house_value'
df_bin = df.a5.a5bin(resolution=resolution,
                    stats=stats,
                    numeric_col=numeric_col,
                    # category_col='ocean_proximity',
                    )
df_bin.plot(
    column=f'{numeric_col}_{stats}',
    cmap='Spectral_r',
    legend=True,
    linewidth=0.2,
)

import geopandas as gpd from vgridpandas import a5pandas resolution = 7 df = gpd.read_file('https://raw.githubusercontent.com/opengeoshub/vopendata/main/shape/housing.geojson') stats = 'max' numeric_col = 'median_house_value' df_bin = df.a5.a5bin(resolution=resolution, stats=stats, numeric_col=numeric_col, # category_col='ocean_proximity', ) df_bin.plot( column=f'{numeric_col}_{stats}', cmap='Spectral_r', legend=True, linewidth=0.2, )

Out[14]:

<Axes: >