06 isea3h

ISEA3HPandas 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 OpenEAGGR ISEA3H¶

In [2]:

import pandas as pd
from vgridpandas import isea3hpandas
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 = 16
df = df.isea3h.latlon2isea3h(resolution)
df.head()

import pandas as pd from vgridpandas import isea3hpandas 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 = 16 df = df.isea3h.latlon2isea3h(resolution) df.head()

Out[2]:

	lon	lat	passenger_count	isea3h	isea3h_res
0	-73.993896	40.750111	1	0116-1266,-151	16
1	-73.976425	40.739811	1	0116-1267,-150	16
2	-73.968704	40.754246	5	0116-1265,-150	16
3	-73.863060	40.769581	5	0116-1264,-141	16
4	-73.945541	40.779423	1	0116-1263,-148	16

OpenEAGGR ISEA3H to geo boundary¶

In [3]:

df = df.isea3h.isea3h2geo()
df.head()

df = df.isea3h.isea3h2geo() df.head()

Out[3]:

	lon	lat	passenger_count	isea3h	isea3h_res	geometry
0	-73.993896	40.750111	1	0116-1266,-151	16	POLYGON ((-73.98575 40.74841, -73.99244 40.751...
1	-73.976425	40.739811	1	0116-1267,-150	16	POLYGON ((-73.96567 40.73905, -73.97236 40.742...
2	-73.968704	40.754246	5	0116-1265,-150	16	POLYGON ((-73.96627 40.7582, -73.97296 40.7613...
3	-73.863060	40.769581	5	0116-1264,-141	16	POLYGON ((-73.85444 40.76895, -73.86113 40.772...
4	-73.945541	40.779423	1	0116-1263,-148	16	POLYGON ((-73.94048 40.77762, -73.94717 40.780...

(Multi)Linestring/ (Multi)Polygon to ISEA3H¶

In [4]:

from vgridpandas import isea3hpandas
import geopandas as gpd

gdf = gpd.read_file('https://raw.githubusercontent.com/opengeoshub/vopendata/refs/heads/main/shape/polygon.geojson')
resolution = 17
gdf_polyfill = gdf.isea3h.polyfill(resolution, compact = True, predicate = "intersects", explode = False)
gdf_polyfill = gdf_polyfill.isea3h.isea3h2geo(isea3h_column = "isea3h")
gdf_polyfill.plot(edgecolor = "white")

from vgridpandas import isea3hpandas import geopandas as gpd gdf = gpd.read_file('https://raw.githubusercontent.com/opengeoshub/vopendata/refs/heads/main/shape/polygon.geojson') resolution = 17 gdf_polyfill = gdf.isea3h.polyfill(resolution, compact = True, predicate = "intersects", explode = False) gdf_polyfill = gdf_polyfill.isea3h.isea3h2geo(isea3h_column = "isea3h") gdf_polyfill.plot(edgecolor = "white")

Out[4]:

<Axes: >

ISEA3H point binning¶

In [5]:

import pandas as pd
import geopandas as gpd
from vgridpandas import isea3hpandas
resolution = 15
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.isea3h.isea3hbin(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 import geopandas as gpd from vgridpandas import isea3hpandas resolution = 15 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.isea3h.isea3hbin(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: >