19 digipin

Working with India's DIGIPIN in Vgrid DGGS¶

Full Vgrid DGGS documentation is available at vgrid document.

To work with Vgrid DGGS directly in GeoPandas and Pandas, please use vgridpandas. Full Vgridpandas DGGS documentation is available at vgridpandas 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 vgrid¶

Uncomment the following line to install vgrid.

In [1]:

# %pip install vgrid --upgrade

# %pip install vgrid --upgrade

Latlon to DIGIPIN¶

In [2]:

from vgrid.conversion.latlon2dggs import latlon2digipin

lat = 17.414718
lon = 78.482992
res = 10
digipin_id = latlon2digipin(lat, lon, res)
digipin_id

from vgrid.conversion.latlon2dggs import latlon2digipin lat = 17.414718 lon = 78.482992 res = 10 digipin_id = latlon2digipin(lat, lon, res) digipin_id

Out[2]:

'422-2PL-9857'

DIGIPIN to Polygon¶

In [3]:

from vgrid.conversion.dggs2geo.digipin2geo import digipin2geo

digipin_geo = digipin2geo(digipin_id)
digipin_geo

from vgrid.conversion.dggs2geo.digipin2geo import digipin2geo digipin_geo = digipin2geo(digipin_id) digipin_geo

Out[3]:

DIGIPIN to GeoJSON¶

In [ ]:

from vgrid.conversion.dggs2geo.digipin2geo import digipin2geojson

digipin_geojson = digipin2geojson(digipin_id)
# digipin_geojson

from vgrid.conversion.dggs2geo.digipin2geo import digipin2geojson digipin_geojson = digipin2geojson(digipin_id) # digipin_geojson

Vector to DIGIPIN¶

In [2]:

from vgrid.conversion.vector2dggs.vector2digipin import vector2digipin

file_path = "https://raw.githubusercontent.com/opengeoshub/vopendata/main/shape/india_polygon.geojson"
vector_to_digipin = vector2digipin(
    file_path,
    compact=True,
    resolution=5,
    topology=False,
    predicate="intersects",
    output_format="gpd",
)
# Visualize the output

vector_to_digipin.plot(edgecolor="white")

from vgrid.conversion.vector2dggs.vector2digipin import vector2digipin file_path = "https://raw.githubusercontent.com/opengeoshub/vopendata/main/shape/india_polygon.geojson" vector_to_digipin = vector2digipin( file_path, compact=True, resolution=5, topology=False, predicate="intersects", output_format="gpd", ) # Visualize the output vector_to_digipin.plot(edgecolor="white")

Processing features: 100%|██████████| 1/1 [00:00<00:00, 11.50it/s]

Out[2]:

<Axes: >

DIGIPIN Compact¶

In [3]:

from vgrid.conversion.dggscompact.digipincompact import digipincompact

digipin_compacted = digipincompact(
    vector_to_digipin, digipin_id="digipin", output_format="gpd"
)
digipin_compacted.plot(edgecolor="white")

from vgrid.conversion.dggscompact.digipincompact import digipincompact digipin_compacted = digipincompact( vector_to_digipin, digipin_id="digipin", output_format="gpd" ) digipin_compacted.plot(edgecolor="white")

Out[3]:

<Axes: >

DIGIPIN Expand¶

In [4]:

from vgrid.conversion.dggscompact.digipincompact import digipinexpand

digipin_expanded = digipinexpand(vector_to_digipin, resolution=5, output_format="gpd")
digipin_expanded.plot(edgecolor="white")

from vgrid.conversion.dggscompact.digipincompact import digipinexpand digipin_expanded = digipinexpand(vector_to_digipin, resolution=5, output_format="gpd") digipin_expanded.plot(edgecolor="white")

Out[4]:

<Axes: >

DIGIPIN Binning¶

In [8]:

from vgrid.binning.digipinbin import digipinbin

file_path = "https://raw.githubusercontent.com/opengeoshub/vopendata/main/shape/india_point.geojson"
stats = "count"
digipin_bin = digipinbin(
    file_path,
    resolution=5,
    stats=stats,
    output_format="gpd",
)
digipin_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 vgrid.binning.digipinbin import digipinbin file_path = "https://raw.githubusercontent.com/opengeoshub/vopendata/main/shape/india_point.geojson" stats = "count" digipin_bin = digipinbin( file_path, resolution=5, stats=stats, output_format="gpd", ) digipin_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: >

Raster to DIGIPIN¶

Download and open raster¶

In [9]:

from vgrid.utils.io import download_file
import rasterio
from rasterio.plot import show

raster_url = (
    "https://raw.githubusercontent.com/opengeoshub/vopendata/main/raster/india_rgb.tif"
)
raster_file = download_file(raster_url)
src = rasterio.open(raster_file, "r")
print(src.meta)
show(src)

from vgrid.utils.io import download_file import rasterio from rasterio.plot import show raster_url = ( "https://raw.githubusercontent.com/opengeoshub/vopendata/main/raster/india_rgb.tif" ) raster_file = download_file(raster_url) src = rasterio.open(raster_file, "r") print(src.meta) show(src)

WARNING [rasterio._env:368  open()] CPLE_AppDefined in PROJ: proj_create_from_database: Cannot find proj.db

india_rgb.tif already exists. Skip downloading. Set overwrite=True to overwrite.
{'driver': 'GTiff', 'dtype': 'uint8', 'nodata': None, 'width': 191, 'height': 125, 'count': 3, 'crs': CRS.from_wkt('GEOGCS["WGS 84",DATUM["World Geodetic System 1984",SPHEROID["WGS 84",6378137,298.257223563]],PRIMEM["Greenwich",0],UNIT["degree",0.0174532925199433,AUTHORITY["EPSG","9122"]],AXIS["Latitude",NORTH],AXIS["Longitude",EAST]]'), 'transform': Affine(9.384238219892975e-05, 0.0, 72.99933512,
       0.0, -9.384238399999845e-05, 19.098565477)}

Out[9]:

<Axes: >

Convert raster to DIGIPIN¶

In [10]:

# %pip install folium

# %pip install folium

In [ ]:

from vgrid.conversion.raster2dggs.raster2digipin import raster2digipin

raster_to_digipin = raster2digipin(raster_file, resolution=8, output_format="gpd")

# Visualize the output
import folium

m = folium.Map(tiles="CartoDB positron", max_zoom=25)

digipin_layer = folium.GeoJson(
    raster_to_digipin,
    style_function=lambda x: {
        "fillColor": f"rgb({x['properties']['band_1']}, {x['properties']['band_2']}, {x['properties']['band_3']})",
        "fillOpacity": 1,
        "color": "black",
        "weight": 1,
    },
    popup=folium.GeoJsonPopup(
        fields=["digipin", "band_1", "band_2", "band_3"],
        aliases=["DIGIPIN ID", "Band 1", "Band 2", "Band 3"],
        style="""
            background-color: white;
            border: 2px solid black;
            border-radius: 3px;
            box-shadow: 3px;
        """,
    ),
).add_to(m)

m.fit_bounds(digipin_layer.get_bounds())

# Display the map
m

from vgrid.conversion.raster2dggs.raster2digipin import raster2digipin raster_to_digipin = raster2digipin(raster_file, resolution=8, output_format="gpd") # Visualize the output import folium m = folium.Map(tiles="CartoDB positron", max_zoom=25) digipin_layer = folium.GeoJson( raster_to_digipin, style_function=lambda x: { "fillColor": f"rgb({x['properties']['band_1']}, {x['properties']['band_2']}, {x['properties']['band_3']})", "fillOpacity": 1, "color": "black", "weight": 1, }, popup=folium.GeoJsonPopup( fields=["digipin", "band_1", "band_2", "band_3"], aliases=["DIGIPIN ID", "Band 1", "Band 2", "Band 3"], style=""" background-color: white; border: 2px solid black; border-radius: 3px; box-shadow: 3px; """, ), ).add_to(m) m.fit_bounds(digipin_layer.get_bounds()) # Display the map m

Converting raster to DIGIPIN: 100%|██████████| 726/726 [00:00<00:00, 65161.56 cells/s]

Out[ ]:

Make this Notebook Trusted to load map: File -> Trust Notebook

DIGIPIN Generator¶

In [1]:

from vgrid.generator.digipingrid import digipingrid

bbox = [77.95, 17.01, 79.0, 17.98]
digipin_grid = digipingrid(resolution=5, bbox=bbox, output_format="gpd")
digipin_grid.plot(edgecolor="white")

from vgrid.generator.digipingrid import digipingrid bbox = [77.95, 17.01, 79.0, 17.98] digipin_grid = digipingrid(resolution=5, bbox=bbox, output_format="gpd") digipin_grid.plot(edgecolor="white")

Out[1]:

<Axes: >

DIGIPIN Statistics¶

Characteristic Length Scale (CLS - suggested by Ralph Kahn): the diameter of a spherical cap of the same cell's area

In [13]:

from vgrid.stats import digipinstats

digipinstats("m")

from vgrid.stats import digipinstats digipinstats("m")

Out[13]:

	resolution	number_of_cells	avg_edge_len_m	avg_cell_area_m2	cls_m
0	1	16	968549.418022	9.380880e+11	1.093226e+06
1	2	256	242137.354505	5.863050e+10	2.732280e+05
2	3	4096	60534.338626	3.664406e+09	6.830577e+04
3	4	65536	15133.584657	2.290254e+08	1.707642e+04
4	5	1048576	3783.396164	1.431409e+07	4.269105e+03
5	6	16777216	945.849041	8.946304e+05	1.067276e+03
6	7	268435456	236.462260	5.591440e+04	2.668191e+02
7	8	4294967296	59.115565	3.494650e+03	6.670477e+01
8	9	68719476736	14.778891	2.184156e+02	1.667619e+01
9	10	1099511627776	3.694723	1.365098e+01	4.169048e+00