13 georef

GEOREFPandas 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 GEOREF¶

In [2]:

import pandas as pd
from vgridpandas import georefpandas          
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 = 2
df = df.georef.latlon2georef(resolution)
df.head()

import pandas as pd from vgridpandas import georefpandas 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 = 2 df = df.georef.latlon2georef(resolution) df.head()

Out[2]:

	lon	lat	passenger_count	georef	georef_res
0	-73.993896	40.750111	1	HJBL0045	2
1	-73.976425	40.739811	1	HJBL0144	2
2	-73.968704	40.754246	5	HJBL0145	2
3	-73.863060	40.769581	5	HJBL0846	2
4	-73.945541	40.779423	1	HJBL0346	2

GEOREF to geo boundary¶

In [3]:

df = df.georef.georef2geo()
df.head()

df = df.georef.georef2geo() df.head()

Out[3]:

	lon	lat	passenger_count	georef	georef_res	geometry
0	-73.993896	40.750111	1	HJBL0045	2	POLYGON ((-74 40.75, -73.98333 40.75, -73.9833...
1	-73.976425	40.739811	1	HJBL0144	2	POLYGON ((-73.98333 40.73333, -73.96667 40.733...
2	-73.968704	40.754246	5	HJBL0145	2	POLYGON ((-73.98333 40.75, -73.96667 40.75, -7...
3	-73.863060	40.769581	5	HJBL0846	2	POLYGON ((-73.86667 40.76667, -73.85 40.76667,...
4	-73.945541	40.779423	1	HJBL0346	2	POLYGON ((-73.95 40.76667, -73.93333 40.76667,...

GEOREF point binning¶

In [4]:

import geopandas as gpd
from vgridpandas import georefpandas
# 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 = 3
stats = "count"
df_bin = df.georef.georefbin(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 geopandas as gpd from vgridpandas import georefpandas # 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 = 3 stats = "count" df_bin = df.georef.georefbin(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: >