10 qtm

QTMPandas 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 QTM¶

In [2]:

import pandas as pd
from vgridpandas import qtmpandas
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 = 16
df = df.qtm.latlon2qtm(resolution)
df.head()

import pandas as pd from vgridpandas import qtmpandas 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 = 16 df = df.qtm.latlon2qtm(resolution) df.head()

Out[2]:

	lon	lat	passenger_count	qtm	qtm_res
0	-73.993896	40.750111	1	2022030230110311	16
1	-73.976425	40.739811	1	2022030230113320	16
2	-73.968704	40.754246	5	2022030233221012	16
3	-73.863060	40.769581	5	2022030233011022	16
4	-73.945541	40.779423	1	2022030233223330	16

QTM to geo boundary¶

In [3]:

df = df.qtm.qtm2geo()
df.head()

df = df.qtm.qtm2geo() df.head()

Out[3]:

	lon	lat	passenger_count	qtm	qtm_res	geometry
0	-73.993896	40.750111	1	2022030230110311	16	POLYGON ((-73.99364 40.74829, -73.99247 40.751...
1	-73.976425	40.739811	1	2022030230113320	16	POLYGON ((-73.97353 40.7373, -73.97236 40.7400...
2	-73.968704	40.754246	5	2022030233221012	16	POLYGON ((-73.97144 40.75378, -73.96647 40.753...
3	-73.863060	40.769581	5	2022030233011022	16	POLYGON ((-73.86128 40.76752, -73.86009 40.770...
4	-73.945541	40.779423	1	2022030233223330	16	POLYGON ((-73.94594 40.7785, -73.94097 40.7785...

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

In [6]:

import geopandas as gpd
from vgridpandas import qtmpandas

gdf = gpd.read_file('https://raw.githubusercontent.com/opengeoshub/vopendata/refs/heads/main/shape/polygon.geojson')
resolution = 19
gdf_polyfill = gdf.qtm.polyfill(resolution, predicate = "intersects", compact = True, explode = True)
gdf_polyfill = gdf_polyfill.qtm.qtm2geo()
gdf_polyfill.plot(edgecolor = "white")

import geopandas as gpd from vgridpandas import qtmpandas gdf = gpd.read_file('https://raw.githubusercontent.com/opengeoshub/vopendata/refs/heads/main/shape/polygon.geojson') resolution = 19 gdf_polyfill = gdf.qtm.polyfill(resolution, predicate = "intersects", compact = True, explode = True) gdf_polyfill = gdf_polyfill.qtm.qtm2geo() gdf_polyfill.plot(edgecolor = "white")

Out[6]:

<Axes: >

QTM point binning¶

In [5]:

import geopandas as gpd
from vgridpandas import qtmpandas
resolution = 17

# 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.qtm.qtmbin(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 qtmpandas resolution = 17 # 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.qtm.qtmbin(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: >