08 olc

OLCPandas 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 OLC¶

In [2]:

import pandas as pd
from vgridpandas import olcpandas       
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 = 10
df = df.olc.latlon2olc(resolution)
df.head()

import pandas as pd from vgridpandas import olcpandas 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 = 10 df = df.olc.latlon2olc(resolution) df.head()

Out[2]:

	lon	lat	passenger_count	olc_res
olc
87G8Q224+2C	-73.993896	40.750111	1	10
87G8P2QF+WC	-73.976425	40.739811	1	10
87G8Q23J+MG	-73.968704	40.754246	5	10
87G8Q49P+RQ	-73.863060	40.769581	5	10
87G8Q3H3+QQ	-73.945541	40.779423	1	10

OLC to geo boundary¶

In [3]:

df = df.olc.olc2geo()
df.head()

df = df.olc.olc2geo() df.head()

Out[3]:

	lon	lat	passenger_count	olc_res	geometry
olc
87G8Q224+2C	-73.993896	40.750111	1	10	POLYGON ((-73.994 40.75, -73.99388 40.75, -73....
87G8P2QF+WC	-73.976425	40.739811	1	10	POLYGON ((-73.9765 40.73975, -73.97638 40.7397...
87G8Q23J+MG	-73.968704	40.754246	5	10	POLYGON ((-73.96875 40.75412, -73.96862 40.754...
87G8Q49P+RQ	-73.863060	40.769581	5	10	POLYGON ((-73.86312 40.7695, -73.863 40.7695, ...
87G8Q3H3+QQ	-73.945541	40.779423	1	10	POLYGON ((-73.94562 40.77938, -73.9455 40.7793...

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

In [4]:

import geopandas as gpd
from vgridpandas import olcpandas

gdf = gpd.read_file('https://raw.githubusercontent.com/opengeoshub/vopendata/refs/heads/main/shape/polygon.geojson')
resolution = 8
gdf_polyfill = gdf.olc.polyfill(resolution, predicate = "intersects", compact = False)
gdf_polyfill.head()
gdf_polyfill = gdf_polyfill.olc.olc2geo(olc_column = "olc")
gdf_polyfill.plot(edgecolor = "white")

import geopandas as gpd from vgridpandas import olcpandas gdf = gpd.read_file('https://raw.githubusercontent.com/opengeoshub/vopendata/refs/heads/main/shape/polygon.geojson') resolution = 8 gdf_polyfill = gdf.olc.polyfill(resolution, predicate = "intersects", compact = False) gdf_polyfill.head() gdf_polyfill = gdf_polyfill.olc.olc2geo(olc_column = "olc") gdf_polyfill.plot(edgecolor = "white")

Out[4]:

<Axes: >

OLC point binning¶

In [5]:

from vgridpandas import olcpandas
# 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 = 8
stats = "count"
df_bin = df.olc.olcbin(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 olcpandas # 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 = 8 stats = "count" df_bin = df.olc.olcbin(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: >