04 rhealpix

rHEALPixPandas key features¶

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 rHEALPix¶

In [2]:

import pandas as pd
from vgridpandas import rhealpixpandas
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 = 11
df = df.rhealpix.latlon2rhealpix(resolution)
df.head()

import pandas as pd from vgridpandas import rhealpixpandas 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 = 11 df = df.rhealpix.latlon2rhealpix(resolution) df.head()

Out[2]:

	lon	lat	passenger_count	rhealpix_res
rhealpix
P01127645811	-73.993896	40.750111	1	11
P01127657310	-73.976425	40.739811	1	11
P01127654506	-73.968704	40.754246	5	11
P01127750407	-73.863060	40.769581	5	11
P01127706706	-73.945541	40.779423	1	11

rHEALPix to geo boundary¶

In [3]:

df = df.rhealpix.rhealpix2geo()
df.head()

df = df.rhealpix.rhealpix2geo() df.head()

Out[3]:

	lon	lat	passenger_count	rhealpix_res	geometry
rhealpix
P01127645811	-73.993896	40.750111	1	11	POLYGON ((-73.99431 40.75041, -73.9938 40.7504...
P01127657310	-73.976425	40.739811	1	11	POLYGON ((-73.97653 40.74018, -73.97602 40.740...
P01127654506	-73.968704	40.754246	5	11	POLYGON ((-73.96891 40.75439, -73.9684 40.7543...
P01127750407	-73.863060	40.769581	5	11	POLYGON ((-73.86323 40.76975, -73.86272 40.769...
P01127706706	-73.945541	40.779423	1	11	POLYGON ((-73.94604 40.77998, -73.94554 40.779...

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

In [4]:

from vgridpandas import rhealpixpandas
import geopandas as gpd

gdf = gpd.read_file('https://raw.githubusercontent.com/opengeoshub/vopendata/refs/heads/main/shape/polygon.geojson')
resolution = 11
gdf_polyfill = gdf.rhealpix.polyfill(resolution, compact = True, predicate = "intersects", explode = False)
gdf_polyfill.head()
gdf_polyfill = gdf_polyfill.rhealpix.rhealpix2geo("rhealpix")
gdf_polyfill.plot(edgecolor = "white")

from vgridpandas import rhealpixpandas import geopandas as gpd gdf = gpd.read_file('https://raw.githubusercontent.com/opengeoshub/vopendata/refs/heads/main/shape/polygon.geojson') resolution = 11 gdf_polyfill = gdf.rhealpix.polyfill(resolution, compact = True, predicate = "intersects", explode = False) gdf_polyfill.head() gdf_polyfill = gdf_polyfill.rhealpix.rhealpix2geo("rhealpix") gdf_polyfill.plot(edgecolor = "white")

Out[4]:

<Axes: >

rHEALPix point binning¶

In [5]:

import pandas as pd
import geopandas as gpd
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")
df.head()
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 import geopandas as gpd 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") df.head() 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[5]:

<Axes: >