02 s2

S2Pandas 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 S2¶

In [2]:

import pandas as pd
from vgridpandas import s2pandas
df = pd.read_csv('https://github.com/uber-web/kepler.gl-data/raw/master/nyctrips/data.csv')
df = df.rename({'pickup_longitude': 'lon', 'pickup_latitude': 'lat'}, axis=1)[['lon', 'lat', 'passenger_count']]
df = df.head(100)
resolution = 8
df = df.s2.latlon2s2(resolution)
df.head()

import pandas as pd from vgridpandas import s2pandas df = pd.read_csv('https://github.com/uber-web/kepler.gl-data/raw/master/nyctrips/data.csv') df = df.rename({'pickup_longitude': 'lon', 'pickup_latitude': 'lat'}, axis=1)[['lon', 'lat', 'passenger_count']] df = df.head(100) resolution = 8 df = df.s2.latlon2s2(resolution) df.head()

Out[2]:

	lon	lat	passenger_count	s2	s2_res
0	-73.993896	40.750111	1	89c25	8
1	-73.976425	40.739811	1	89c25	8
2	-73.968704	40.754246	5	89c25	8
3	-73.863060	40.769581	5	89c25	8
4	-73.945541	40.779423	1	89c25	8

S2 to geo boundary¶

In [3]:

df = df.s2.s22geo() # if set_index = False in the previous step, call df = df.s2.s22geo(s2_col = 's2_00')
df.head()

df = df.s2.s22geo() # if set_index = False in the previous step, call df = df.s2.s22geo(s2_col = 's2_00') df.head()

Out[3]:

	lon	lat	passenger_count	s2	s2_res	geometry
0	-73.993896	40.750111	1	89c25	8	POLYGON ((-74.21793 40.82771, -74.21793 40.510...
1	-73.976425	40.739811	1	89c25	8	POLYGON ((-74.21793 40.82771, -74.21793 40.510...
2	-73.968704	40.754246	5	89c25	8	POLYGON ((-74.21793 40.82771, -74.21793 40.510...
3	-73.863060	40.769581	5	89c25	8	POLYGON ((-74.21793 40.82771, -74.21793 40.510...
4	-73.945541	40.779423	1	89c25	8	POLYGON ((-74.21793 40.82771, -74.21793 40.510...

(Multi)Linestring/ (Multi)Polygon to S2¶

In [6]:

import geopandas as gpd
from vgridpandas import s2pandas

gdf = gpd.read_file('https://raw.githubusercontent.com/opengeoshub/vopendata/refs/heads/main/shape/polygon.geojson')
resolution = 18
gdf_polyfill = gdf.s2.polyfill(resolution, compact = True, predicate = "largest_overlap", explode = True)
gdf_polyfill = gdf_polyfill.s2.s22geo()
gdf_polyfill.plot(edgecolor = "white")

import geopandas as gpd from vgridpandas import s2pandas gdf = gpd.read_file('https://raw.githubusercontent.com/opengeoshub/vopendata/refs/heads/main/shape/polygon.geojson') resolution = 18 gdf_polyfill = gdf.s2.polyfill(resolution, compact = True, predicate = "largest_overlap", explode = True) gdf_polyfill = gdf_polyfill.s2.s22geo() gdf_polyfill.plot(edgecolor = "white")

Out[6]:

<Axes: >

S2 point binning¶

In [5]:

import pandas as pd
import geopandas as gpd
from vgridpandas import s2pandas
resolution = 16
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")
df.head()
stats = "count"
df_bin = df.s2.s2bin(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 s2pandas resolution = 16 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") df.head() stats = "count" df_bin = df.s2.s2bin(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: >