18 gars

In [ ]:

GARSPandas 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 GARS¶

In [2]:

import pandas as pd
from vgridpandas import garspandas             
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 = 3
df = df.gars.latlon2gars(resolution)
df.head()

import pandas as pd from vgridpandas import garspandas 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 = 3 df = df.gars.latlon2gars(resolution) df.head()

Out[2]:

	lon	lat	passenger_count	gars	gars_res
0	-73.993896	40.750111	1	213LX17	3
1	-73.976425	40.739811	1	213LX31	3
2	-73.968704	40.754246	5	213LX17	3
3	-73.863060	40.769581	5	213LX18	3
4	-73.945541	40.779423	1	213LX17	3

Maidenhead to geo boundary¶

In [3]:

df = df.gars.gars2geo()
df.head()

df = df.gars.gars2geo() df.head()

Out[3]:

	lon	lat	passenger_count	gars	gars_res	geometry
0	-73.993896	40.750111	1	213LX17	3	POLYGON ((-73.91667 40.75, -73.91667 40.83333,...
1	-73.976425	40.739811	1	213LX31	3	POLYGON ((-73.91667 40.66667, -73.91667 40.75,...
2	-73.968704	40.754246	5	213LX17	3	POLYGON ((-73.91667 40.75, -73.91667 40.83333,...
3	-73.863060	40.769581	5	213LX18	3	POLYGON ((-73.83333 40.75, -73.83333 40.83333,...
4	-73.945541	40.779423	1	213LX17	3	POLYGON ((-73.91667 40.75, -73.91667 40.83333,...

GARS point binning¶

In [4]:

from vgridpandas import garspandas
import geopandas as gpd

# 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")
resolution = 4
stats = "count"
df_bin = df.gars.garsbin(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)
)

from vgridpandas import garspandas import geopandas as gpd # 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") resolution = 4 stats = "count" df_bin = df.gars.garsbin(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[4]:

<Axes: >