Usage

Vgridpandas key features¶

Vgridpandas supports popular geodesic DGGS such as H3, S2, A5, rHEALPix, Open-Eaggr ISEA4T, EASE-DGGS, QTM, and graticule DGGS such as OLC, Geohash, MGRS, GEOREF, Tilecode, Quadkey, Maidenhead, GARS

You can try out vgridpandas by using the cloud-computing platforms below without having to install anything on your computer:

Install vgridpandas¶

In [1]:

# %pip install vgridpandas

# %pip install vgridpandas

Latlong to DGGS¶

In [ ]:

import pandas as pd
from vgridpandas import h3pandas
df = pd.DataFrame({'lat': [14.6657293, -187.5088058], 'lon': [14, 15]})
resolution = 0
df = df.h3.latlon2h3(resolution)
df.head()

import pandas as pd from vgridpandas import h3pandas df = pd.DataFrame({'lat': [14.6657293, -187.5088058], 'lon': [14, 15]}) resolution = 0 df = df.h3.latlon2h3(resolution) df.head()

Out[ ]:

	lat	lon	h3_res
h3
8059fffffffffff	14.665729	14	0
8071fffffffffff	-187.508806	15	0

DGGS to geo boundary¶

In [3]:

df = df.h3.h32geo()
df

df = df.h3.h32geo() df

Out[3]:

	lat	lon	h3_res	geometry
h3
8059fffffffffff	14.665729	14	0	POLYGON ((-2.48387 22.19754, -4.014 11.5453, 3...
8071fffffffffff	-187.508806	15	0	POLYGON ((-175.45827 15.28573, -180.78284 5.88...

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

In [6]:

from shapely.geometry import box
from vgridpandas import s2pandas
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.s2.polyfill(resolution, predicate = "largest_overlap", explode = False)
gdf_polyfill = gdf_polyfill.s2.s22geo(s2_column = "s2")
gdf_polyfill.plot(edgecolor = "white")

from shapely.geometry import box from vgridpandas import s2pandas 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.s2.polyfill(resolution, predicate = "largest_overlap", explode = False) gdf_polyfill = gdf_polyfill.s2.s22geo(s2_column = "s2") gdf_polyfill.plot(edgecolor = "white")

Out[6]:

<Axes: >

DGGS point binning¶

In [17]:

import pandas as pd
from vgridpandas import rhealpixpandas
resolution = 10
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.rhealpix.rhealpixbin(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 from vgridpandas import rhealpixpandas resolution = 10 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.rhealpix.rhealpixbin(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[17]:

<Axes: >