12 geohash

GeohashPandas 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 Geohash¶

In [2]:

import pandas as pd
from vgridpandas import geohashpandas          
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.geohash.latlon2geohash(resolution)
df.head()

import pandas as pd from vgridpandas import geohashpandas 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.geohash.latlon2geohash(resolution) df.head()

Out[2]:

	lon	lat	passenger_count	geohash	geohash_res
0	-73.993896	40.750111	1	dr	2
1	-73.976425	40.739811	1	dr	2
2	-73.968704	40.754246	5	dr	2
3	-73.863060	40.769581	5	dr	2
4	-73.945541	40.779423	1	dr	2

Geohash to geo boundary¶

In [3]:

df = df.geohash.geohash2geo()
df.head()

df = df.geohash.geohash2geo() df.head()

Out[3]:

	lon	lat	passenger_count	geohash	geohash_res	geometry
0	-73.993896	40.750111	1	dr	2	POLYGON ((-78.75 39.375, -67.5 39.375, -67.5 4...
1	-73.976425	40.739811	1	dr	2	POLYGON ((-78.75 39.375, -67.5 39.375, -67.5 4...
2	-73.968704	40.754246	5	dr	2	POLYGON ((-78.75 39.375, -67.5 39.375, -67.5 4...
3	-73.863060	40.769581	5	dr	2	POLYGON ((-78.75 39.375, -67.5 39.375, -67.5 4...
4	-73.945541	40.779423	1	dr	2	POLYGON ((-78.75 39.375, -67.5 39.375, -67.5 4...

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

In [6]:

import geopandas as gpd
from vgridpandas import geohashpandas

gdf = gpd.read_file('https://raw.githubusercontent.com/opengeoshub/vopendata/refs/heads/main/shape/polygon.geojson')
resolution = 8
gdf_polyfill = gdf.geohash.polyfill(resolution, predicate = "intersects", compact = True)
gdf_polyfill = gdf_polyfill.geohash.geohash2geo()
gdf_polyfill.plot(edgecolor = "white")

import geopandas as gpd from vgridpandas import geohashpandas gdf = gpd.read_file('https://raw.githubusercontent.com/opengeoshub/vopendata/refs/heads/main/shape/polygon.geojson') resolution = 8 gdf_polyfill = gdf.geohash.polyfill(resolution, predicate = "intersects", compact = True) gdf_polyfill = gdf_polyfill.geohash.geohash2geo() gdf_polyfill.plot(edgecolor = "white")

Out[6]:

<Axes: >

Geohash point binning¶

In [7]:

import geopandas as gpd
from vgridpandas import geohashpandas   
# 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 = 7
stats = "count"
df_bin = df.geohash.geohashbin(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 geohashpandas # 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 = 7 stats = "count" df_bin = df.geohash.geohashbin(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[7]:

<Axes: >