06 ease

EASE-DGGS key features¶

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

Install vgridpandas¶

In [1]:

# %pip install vgridpandas

# %pip install vgridpandas

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

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

In [4]:

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(ease_column = "ease")
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(ease_column = "ease") gdf_polyfill.plot(edgecolor = "white")

Out[4]:

<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: >