06 dggrid

DGGRIDPandas key features¶

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

DGGRID setup¶

In [2]:

from dggrid4py import tool, DGGRIDv8

dggrid_exec = tool.get_portable_executable('.')

dggrid_instance = DGGRIDv8(
    executable=dggrid_exec,
    working_dir='.',
    capture_logs=False,
    silent=True,
    has_gdal=False,
    tmp_geo_out_legacy=True,
    debug=False,
)

from dggrid4py import tool, DGGRIDv8 dggrid_exec = tool.get_portable_executable('.') dggrid_instance = DGGRIDv8( executable=dggrid_exec, working_dir='.', capture_logs=False, silent=True, has_gdal=False, tmp_geo_out_legacy=True, debug=False, )

Import dggridpandas¶

In [3]:

from vgridpandas import dggridpandas
dggs_type = "ISEA3H"
address_type = "SEQNUM"

from vgridpandas import dggridpandas dggs_type = "ISEA3H" address_type = "SEQNUM"

Latlon to DGGRID¶

Pandas¶

In [20]:

import pandas as pd

df = pd.read_csv('https://raw.githubusercontent.com/opengeoshub/vopendata/main/csv/housing.csv')
resolution = 10
df = df.dggrid.latlon2dggrid(dggrid_instance, dggs_type, resolution, lat_col='lat', lon_col='lon', address_type=address_type)
df

import pandas as pd df = pd.read_csv('https://raw.githubusercontent.com/opengeoshub/vopendata/main/csv/housing.csv') resolution = 10 df = df.dggrid.latlon2dggrid(dggrid_instance, dggs_type, resolution, lat_col='lat', lon_col='lon', address_type=address_type) df

Out[20]:

	lon	lat	housing_median_age	total_rooms	total_bedrooms	population	households	median_income	median_house_value	ocean_proximity	dggrid_isea3h	dggrid_isea3h_res
0	-122.23	37.88	41	880	129.0	322	126	8.3252	452600	NEAR BAY	38240	10
1	-122.22	37.86	21	7099	1106.0	2401	1138	8.3014	358500	NEAR BAY	38240	10
2	-122.24	37.85	52	1467	190.0	496	177	7.2574	352100	NEAR BAY	38240	10
3	-122.25	37.85	52	1274	235.0	558	219	5.6431	341300	NEAR BAY	38240	10
4	-122.25	37.85	52	1627	280.0	565	259	3.8462	342200	NEAR BAY	38240	10
...	...	...	...	...	...	...	...	...	...	...	...	...
20635	-121.09	39.48	25	1665	374.0	845	330	1.5603	78100	INLAND	37515	10
20636	-121.21	39.49	18	697	150.0	356	114	2.5568	77100	INLAND	37515	10
20637	-121.22	39.43	17	2254	485.0	1007	433	1.7000	92300	INLAND	37515	10
20638	-121.32	39.43	18	1860	409.0	741	349	1.8672	84700	INLAND	37515	10
20639	-121.24	39.37	16	2785	616.0	1387	530	2.3886	89400	INLAND	37515	10

20640 rows × 12 columns

GeoPandas¶

In [21]:

import geopandas as gpd

df = gpd.read_file('https://raw.githubusercontent.com/opengeoshub/vopendata/main/shape/housing.geojson')
resolution = 10
df = df.dggrid.latlon2dggrid(dggrid_instance, dggs_type, resolution, address_type=address_type)
df

import geopandas as gpd df = gpd.read_file('https://raw.githubusercontent.com/opengeoshub/vopendata/main/shape/housing.geojson') resolution = 10 df = df.dggrid.latlon2dggrid(dggrid_instance, dggs_type, resolution, address_type=address_type) df

Out[21]:

	longitude	latitude	housing_median_age	total_rooms	total_bedrooms	population	households	median_income	median_house_value	ocean_proximity	geometry	dggrid_isea3h	dggrid_isea3h_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)	38240	10
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)	38240	10
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)	38240	10
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)	38240	10
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)	38240	10
...	...	...	...	...	...	...	...	...	...	...	...	...	...
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)	37515	10
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)	37515	10
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)	37515	10
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)	37515	10
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)	37515	10

20640 rows × 13 columns

DGGRID to geo¶

In [ ]:

df = df.dggrid.dggrid2geo(dggrid_instance, dggs_type, resolution, dggrid_col=f"dggrid_{dggs_type.lower()}")
df.plot(edgecolor='white')

df = df.dggrid.dggrid2geo(dggrid_instance, dggs_type, resolution, dggrid_col=f"dggrid_{dggs_type.lower()}") df.plot(edgecolor='white')

Out[ ]:

<Axes: >

DGGRID binning¶

Pandas¶

In [23]:

import pandas as pd

resolution = 10
df = pd.read_csv('https://raw.githubusercontent.com/opengeoshub/vopendata/main/csv/housing.csv')
stats = 'max'
numeric_col = 'median_house_value'
df_bin = df.dggrid.dggridbin(dggrid_instance, dggs_type, resolution,
                    lat_col='lat', lon_col='lon',
                    stats=stats, numeric_col=numeric_col, address_type=address_type)
df_bin.plot(column=f'{numeric_col}_{stats}', cmap='Spectral_r', legend=True, linewidth=0.2)

import pandas as pd resolution = 10 df = pd.read_csv('https://raw.githubusercontent.com/opengeoshub/vopendata/main/csv/housing.csv') stats = 'max' numeric_col = 'median_house_value' df_bin = df.dggrid.dggridbin(dggrid_instance, dggs_type, resolution, lat_col='lat', lon_col='lon', stats=stats, numeric_col=numeric_col, address_type=address_type) df_bin.plot(column=f'{numeric_col}_{stats}', cmap='Spectral_r', legend=True, linewidth=0.2)

Out[23]:

<Axes: >

GeoPandas¶

In [6]:

import geopandas as gpd

resolution = 10
df = gpd.read_file('https://raw.githubusercontent.com/opengeoshub/vopendata/main/shape/housing.geojson')
stats = 'max'
numeric_col = 'median_house_value'
df_bin = df.dggrid.dggridbin(dggrid_instance, dggs_type, resolution,
                    stats=stats, numeric_col=numeric_col, address_type=address_type)
df_bin.plot(column=f'{numeric_col}_{stats}', cmap='Spectral_r', legend=True, linewidth=0.2)

import geopandas as gpd resolution = 10 df = gpd.read_file('https://raw.githubusercontent.com/opengeoshub/vopendata/main/shape/housing.geojson') stats = 'max' numeric_col = 'median_house_value' df_bin = df.dggrid.dggridbin(dggrid_instance, dggs_type, resolution, stats=stats, numeric_col=numeric_col, address_type=address_type) df_bin.plot(column=f'{numeric_col}_{stats}', cmap='Spectral_r', legend=True, linewidth=0.2)

Out[6]:

<Axes: >