10 mgrs

MGRSPandas 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 MGRS¶

In [5]:

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[5]:

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

MGRS to geo boundary¶

In [6]:

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

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

Out[6]:

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

MGRS point binning¶

In [8]:

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[8]:

<Axes: >