12 tilecode

TilecodePandas 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 Tilecode¶

In [2]:

import pandas as pd
from vgridpandas import tilecodepandas          
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.tilecode.latlon2tilecode(resolution)
df.head()

import pandas as pd from vgridpandas import tilecodepandas 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.tilecode.latlon2tilecode(resolution) df.head()

Out[2]:

	lon	lat	passenger_count	tilecode_res
tilecode
z10x301y384	-73.993896	40.750111	1	10
z10x301y384	-73.976425	40.739811	1	10
z10x301y384	-73.968704	40.754246	5	10
z10x301y384	-73.863060	40.769581	5	10
z10x301y384	-73.945541	40.779423	1	10

Tilecode to geo boundary¶

In [3]:

df = df.tilecode.tilecode2geo()
df.head()

df = df.tilecode.tilecode2geo() df.head()

Out[3]:

	lon	lat	passenger_count	tilecode_res	geometry
tilecode
z10x301y384	-73.993896	40.750111	1	10	POLYGON ((-74.17969 40.71396, -73.82812 40.713...
z10x301y384	-73.976425	40.739811	1	10	POLYGON ((-74.17969 40.71396, -73.82812 40.713...
z10x301y384	-73.968704	40.754246	5	10	POLYGON ((-74.17969 40.71396, -73.82812 40.713...
z10x301y384	-73.863060	40.769581	5	10	POLYGON ((-74.17969 40.71396, -73.82812 40.713...
z10x301y384	-73.945541	40.779423	1	10	POLYGON ((-74.17969 40.71396, -73.82812 40.713...

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

In [4]:

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 = 18
gdf_polyfill = gdf.tilecode.polyfill(resolution, predicate = "intersects", compact = True)
gdf_polyfill = gdf_polyfill.tilecode.tilecode2geo(tilecode_column = "tilecode")
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 = 18 gdf_polyfill = gdf.tilecode.polyfill(resolution, predicate = "intersects", compact = True) gdf_polyfill = gdf_polyfill.tilecode.tilecode2geo(tilecode_column = "tilecode") gdf_polyfill.plot(edgecolor='white')

Out[4]:

<Axes: >

Tilecode point binning¶

In [5]:

from vgridpandas import tilecodepandas
import geopandas as gpd

# 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 = 18
stats = "count"
df_bin = df.tilecode.tilecodebin(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 tilecodepandas import geopandas as gpd # 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 = 18 stats = "count" df_bin = df.tilecode.tilecodebin(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: >