05 isea4t

ISEA4TPandas 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 ISEA4T¶

In [2]:

import pandas as pd
from vgridpandas import isea4tpandas
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.isea4t.latlon2isea4t(resolution)
df.head()

import pandas as pd from vgridpandas import isea4tpandas 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.isea4t.latlon2isea4t(resolution) df.head()

Out[2]:

	lon	lat	passenger_count	isea4t	isea4t_res
0	-73.993896	40.750111	1	010102302222012122	16
1	-73.976425	40.739811	1	010102302222031012	16
2	-73.968704	40.754246	5	010102302222301201	16
3	-73.863060	40.769581	5	010102312201111201	16
4	-73.945541	40.779423	1	010102312220100100	16

OpenEAGGR ISEA4T to geo boundary¶

In [3]:

df = df.isea4t.isea4t2geo()
df.head()

df = df.isea4t.isea4t2geo() df.head()

Out[3]:

	lon	lat	passenger_count	isea4t	isea4t_res	geometry
0	-73.993896	40.750111	1	010102302222012122	16	POLYGON ((-73.99399 40.75029, -73.99462 40.749...
1	-73.976425	40.739811	1	010102302222031012	16	POLYGON ((-73.97647 40.73898, -73.97716 40.739...
2	-73.968704	40.754246	5	010102302222301201	16	POLYGON ((-73.96834 40.75345, -73.96903 40.754...
3	-73.863060	40.769581	5	010102312201111201	16	POLYGON ((-73.86317 40.7699, -73.8638 40.76893...
4	-73.945541	40.779423	1	010102312220100100	16	POLYGON ((-73.94534 40.77862, -73.94603 40.779...

(Multi)Linestring/ (Multi)Polygon to ISEA4T¶

In [4]:

from vgridpandas import isea4tpandas
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.isea4t.polyfill(resolution, compact = True, predicate = "intersects", explode = False)
gdf_polyfill = gdf_polyfill.isea4t.isea4t2geo(isea4t_column = "isea4t")
gdf_polyfill.plot(edgecolor = "white")

from vgridpandas import isea4tpandas 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.isea4t.polyfill(resolution, compact = True, predicate = "intersects", explode = False) gdf_polyfill = gdf_polyfill.isea4t.isea4t2geo(isea4t_column = "isea4t") gdf_polyfill.plot(edgecolor = "white")

Out[4]:

<Axes: >

ISEA4T point binning¶

In [5]:

import pandas as pd
import geopandas as gpd
from vgridpandas import isea4tpandas
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.isea4t.isea4tbin(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 isea4tpandas 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.isea4t.isea4tbin(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: >