07 ease

EASEPandas 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 EASE-DGGS¶

In [2]:

import pandas as pd
from vgridpandas import easepandas
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
df = df.ease.latlon2ease(resolution)
df.head()

import pandas as pd from vgridpandas import easepandas 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 df = df.ease.latlon2ease(resolution) df.head()

Out[2]:

	lon	lat	passenger_count	ease	ease_res
0	-73.993896	40.750111	1	L6.070283.13.01.20.89.78.88	6
1	-73.976425	40.739811	1	L6.070283.13.11.02.86.77.82	6
2	-73.968704	40.754246	5	L6.070283.13.02.20.44.71.66	6
3	-73.863060	40.769581	5	L6.070284.10.02.01.96.80.71	6
4	-73.945541	40.779423	1	L6.070283.13.02.02.06.34.29	6

EASE-DGGS to geo boundary¶

In [3]:

df = df.ease.ease2geo()
df.head()

df = df.ease.ease2geo() df.head()

Out[3]:

	lon	lat	passenger_count	ease	ease_res	geometry
0	-73.993896	40.750111	1	L6.070283.13.01.20.89.78.88	6	POLYGON ((-73.9939 40.7501, -73.99389 40.7501,...
1	-73.976425	40.739811	1	L6.070283.13.11.02.86.77.82	6	POLYGON ((-73.97643 40.7398, -73.97642 40.7398...
2	-73.968704	40.754246	5	L6.070283.13.02.20.44.71.66	6	POLYGON ((-73.96871 40.75424, -73.9687 40.7542...
3	-73.863060	40.769581	5	L6.070284.10.02.01.96.80.71	6	POLYGON ((-73.86306 40.76958, -73.86305 40.769...
4	-73.945541	40.779423	1	L6.070283.13.02.02.06.34.29	6	POLYGON ((-73.94555 40.77941, -73.94554 40.779...

(Multi)Linestring/ (Multi)Polygon to EASE-DGGS¶

In [6]:

from vgridpandas import easepandas
import geopandas as gpd

gdf = gpd.read_file('https://raw.githubusercontent.com/opengeoshub/vopendata/refs/heads/main/shape/polygon.geojson')
resolution = 4
gdf_polyfill = gdf.ease.polyfill(resolution, compact = False, predicate = "intersects", explode = False)
gdf_polyfill = gdf_polyfill.ease.ease2geo()
gdf_polyfill.plot(edgecolor = "white")

from vgridpandas import easepandas import geopandas as gpd gdf = gpd.read_file('https://raw.githubusercontent.com/opengeoshub/vopendata/refs/heads/main/shape/polygon.geojson') resolution = 4 gdf_polyfill = gdf.ease.polyfill(resolution, compact = False, predicate = "intersects", explode = False) gdf_polyfill = gdf_polyfill.ease.ease2geo() gdf_polyfill.plot(edgecolor = "white")

Out[6]:

<Axes: >

EASE-DGGS point binning¶

In [5]:

import pandas as pd
from vgridpandas import easepandas
resolution = 4
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.ease.easebin(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 easepandas resolution = 4 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.ease.easebin(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: >