08 dggal

DGGALPandas 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 [1]:

# %pip install vgridpandas

# %pip install vgridpandas

Latlon to DGGAL¶

In [2]:

import pandas as pd
from vgridpandas import dggalpandas
df = pd.read_csv('https://github.com/uber-web/kepler.gl-data/raw/master/nyctrips/data.csv')
df = df.head(100)
df = df.rename({'pickup_longitude': 'lon', 'pickup_latitude': 'lat'}, axis=1)[['lon', 'lat', 'passenger_count']]
resolution = 6
dggs_type = "isea9r"  # choose one from ['gnosis','isea3h','isea9r','ivea3h',
# 'ivea9r','rtea3h','rtea9r','rhealpix']
df = df.dggal.latlon2dggal(dggs_type, resolution,set_index=False)
df.head()

import pandas as pd from vgridpandas import dggalpandas df = pd.read_csv('https://github.com/uber-web/kepler.gl-data/raw/master/nyctrips/data.csv') df = df.head(100) df = df.rename({'pickup_longitude': 'lon', 'pickup_latitude': 'lat'}, axis=1)[['lon', 'lat', 'passenger_count']] resolution = 6 dggs_type = "isea9r" # choose one from ['gnosis','isea3h','isea9r','ivea3h', # 'ivea9r','rtea3h','rtea9r','rhealpix'] df = df.dggal.latlon2dggal(dggs_type, resolution,set_index=False) df.head()

Out[2]:

	lon	lat	passenger_count	dggal_isea9r	dggal_isea9r_res
0	-73.993896	40.750111	1	G0-6BABB	6
1	-73.976425	40.739811	1	G0-6BABB	6
2	-73.968704	40.754246	5	G0-6BABB	6
3	-73.863060	40.769581	5	G0-6BABC	6
4	-73.945541	40.779423	1	G0-6BABB	6

DGGAL to geo boundary¶

In [3]:

df = df.dggal.dggal2geo(dggs_type)
df.head()

df = df.dggal.dggal2geo(dggs_type) df.head()

Out[3]:

	lon	lat	passenger_count	dggal_isea9r	dggal_isea9r_res	geometry
0	-73.993896	40.750111	1	G0-6BABB	6	POLYGON ((-74.01084 40.80323, -74.0132 40.8010...
1	-73.976425	40.739811	1	G0-6BABB	6	POLYGON ((-74.01084 40.80323, -74.0132 40.8010...
2	-73.968704	40.754246	5	G0-6BABB	6	POLYGON ((-74.01084 40.80323, -74.0132 40.8010...
3	-73.863060	40.769581	5	G0-6BABC	6	POLYGON ((-73.88184 40.77904, -73.88421 40.776...
4	-73.945541	40.779423	1	G0-6BABB	6	POLYGON ((-74.01084 40.80323, -74.0132 40.8010...

(Multi)Linestring/ (Multi)Polygon to DGGAL¶

In [4]:

from vgridpandas import dggalpandas
import geopandas as gpd

gdf = gpd.read_file('https://raw.githubusercontent.com/opengeoshub/vopendata/refs/heads/main/shape/polygon.geojson')
resolution = 10
gdf_polyfill = gdf.dggal.polyfill(dggs_type, resolution, compact = True, predicate = "intersects", explode = False)
gdf_polyfill = gdf_polyfill.dggal.dggal2geo(dggs_type)
gdf_polyfill.plot(edgecolor = "white")

from vgridpandas import dggalpandas import geopandas as gpd gdf = gpd.read_file('https://raw.githubusercontent.com/opengeoshub/vopendata/refs/heads/main/shape/polygon.geojson') resolution = 10 gdf_polyfill = gdf.dggal.polyfill(dggs_type, resolution, compact = True, predicate = "intersects", explode = False) gdf_polyfill = gdf_polyfill.dggal.dggal2geo(dggs_type) gdf_polyfill.plot(edgecolor = "white")

Out[4]:

<Axes: >

DGGAL point binning¶

In [5]:

import pandas as pd
from vgridpandas import dggalpandas
resolution = 10
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.dggal.dggalbin(dggs_type, 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)
)

import pandas as pd from vgridpandas import dggalpandas resolution = 10 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.dggal.dggalbin(dggs_type, 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) )

Out[5]:

<Axes: >