14 mgrs

MGRSPandas 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 MGRS¶

In [2]:

import pandas as pd
from vgridpandas import mgrspandas          
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 = 2
df = df.mgrs.latlon2mgrs(resolution)
df.head()

import pandas as pd from vgridpandas import mgrspandas 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 = 2 df = df.mgrs.latlon2mgrs(resolution) df.head()

Out[2]:

	lon	lat	passenger_count	mgrs	mgrs_res
0	-73.993896	40.750111	1	18TWL8411	2
1	-73.976425	40.739811	1	18TWL8610	2
2	-73.968704	40.754246	5	18TWL8711	2
3	-73.863060	40.769581	5	18TWL9513	2
4	-73.945541	40.779423	1	18TWL8814	2

MGRS to geo boundary¶

In [3]:

df = df.mgrs.mgrs2geo()
df.head()

df = df.mgrs.mgrs2geo() df.head()

Out[3]:

	lon	lat	passenger_count	mgrs	mgrs_res	geometry
0	-73.993896	40.750111	1	18TWL8411	2	POLYGON ((-74.00503 40.74566, -73.99305 40.745...
1	-73.976425	40.739811	1	18TWL8610	2	POLYGON ((-73.98148 40.73645, -73.9695 40.7364...
2	-73.968704	40.754246	5	18TWL8711	2	POLYGON ((-73.9695 40.74535, -73.95752 40.7453...
3	-73.863060	40.769581	5	18TWL9513	2	POLYGON ((-73.87445 40.76248, -73.86246 40.762...
4	-73.945541	40.779423	1	18TWL8814	2	POLYGON ((-73.95724 40.77227, -73.94524 40.772...

MGRS point binning¶

In [4]:

import geopandas as gpd
from vgridpandas import mgrspandas

# 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 = 3
stats = "count"
df_bin = df.mgrs.mgrsbin(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 mgrspandas # 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 = 3 stats = "count" df_bin = df.mgrs.mgrsbin(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[4]:

<Axes: >