06 dggrid

Working with DGGRID 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.

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# %pip install vgrid --upgrade
# %pip install vgrid --upgrade

Download a portable version of DGGRID and start a DGGRID instance¶

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from dggrid4py import tool, DGGRIDv8

dggrid_exec = tool.get_portable_executable(".")

dggrid_instance = DGGRIDv8(
    executable=dggrid_exec,
    working_dir=".",
    capture_logs=True,
    silent=True,
    has_gdal=False,
    tmp_geo_out_legacy=True,
    debug=False,
)
from dggrid4py import tool, DGGRIDv8

dggrid_exec = tool.get_portable_executable(".")

dggrid_instance = DGGRIDv8(
    executable=dggrid_exec,
    working_dir=".",
    capture_logs=True,
    silent=True,
    has_gdal=False,
    tmp_geo_out_legacy=True,
    debug=False,
)

latlon2dggrid¶

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from vgrid.conversion.latlon2dggs import latlon2dggrid

dggs_type = "PLANETRISK"  # choose one from ['CUSTOM', 'SUPERFUND', 'PLANETRISK', 'ISEA3H', 'ISEA4H', 'ISEA4T', 'ISEA4D', 
                            # 'ISEA43H', 'ISEA7H', 'IGEO7', 'FULLER3H', 'FULLER4H', 'FULLER4T', 'FULLER4D', 'FULLER43H', 'FULLER7H']
resolution = 10
address_type = "SEQNUM"
# "Q2DI",  # quad number and (i, j) coordinates on that quad
# "SEQNUM",  # DGGS index - linear address (1 to size-of-DGG), not supported for parameter input_address_type if dggs_aperture_type is SEQUENCE
# "PLANE",  # (x, y) coordinates on unfolded ISEA plane,  only supported for parameter output_address_type;
# "Q2DD",  # quad number and (x, y) coordinates on that quad
# "PROJTRI",  # PROJTRI - triangle number and (x, y) coordinates within that triangle on the ISEA plane
# "VERTEX2DD",  # vertex number, triangle number, and (x, y) coordinates on ISEA plane
# "Z3",  # hexadecimal characters index system Z3 especially usefull for ISEA3H
# "Z3_STRING",  # numerical digits representation of Z3 (as characters, not an integer)
# "Z7",  # hexadecimal characters index system Z7 especially usefull for ISEA7H, also in preset IGEO7
# "Z7_STRING",  # numerical digits representation of Z7 (as characters, not an integer)
# "ZORDER",  # index system ZORDER especially usefull for ISEA3H, ISEA4H and mixed aperture
# "ZORDER_STRING",  # numerical digits representation of ZORDER (as characters, not an integer)
dggrid_id = latlon2dggrid(
    dggrid_instance, dggs_type, 10.775276, 106.706797, resolution,output_address_type='Z7_STRING')
dggrid_id
from vgrid.conversion.latlon2dggs import latlon2dggrid

dggs_type = "PLANETRISK"  # choose one from ['CUSTOM', 'SUPERFUND', 'PLANETRISK', 'ISEA3H', 'ISEA4H', 'ISEA4T', 'ISEA4D', 
                            # 'ISEA43H', 'ISEA7H', 'IGEO7', 'FULLER3H', 'FULLER4H', 'FULLER4T', 'FULLER4D', 'FULLER43H', 'FULLER7H']
resolution = 10
address_type = "SEQNUM"
# "Q2DI",  # quad number and (i, j) coordinates on that quad
# "SEQNUM",  # DGGS index - linear address (1 to size-of-DGG), not supported for parameter input_address_type if dggs_aperture_type is SEQUENCE
# "PLANE",  # (x, y) coordinates on unfolded ISEA plane,  only supported for parameter output_address_type;
# "Q2DD",  # quad number and (x, y) coordinates on that quad
# "PROJTRI",  # PROJTRI - triangle number and (x, y) coordinates within that triangle on the ISEA plane
# "VERTEX2DD",  # vertex number, triangle number, and (x, y) coordinates on ISEA plane
# "Z3",  # hexadecimal characters index system Z3 especially usefull for ISEA3H
# "Z3_STRING",  # numerical digits representation of Z3 (as characters, not an integer)
# "Z7",  # hexadecimal characters index system Z7 especially usefull for ISEA7H, also in preset IGEO7
# "Z7_STRING",  # numerical digits representation of Z7 (as characters, not an integer)
# "ZORDER",  # index system ZORDER especially usefull for ISEA3H, ISEA4H and mixed aperture
# "ZORDER_STRING",  # numerical digits representation of ZORDER (as characters, not an integer)
dggrid_id = latlon2dggrid(
    dggrid_instance, dggs_type, 10.775276, 106.706797, resolution,output_address_type='Z7_STRING')
dggrid_id

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'58692813'

DGGRID to Polygon (GeoDataFrame)¶

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from vgrid.conversion.dggs2geo.dggrid2geo import dggrid2geo
from vgrid.conversion.latlon2dggs import latlon2dggrid

dggs_type = "IGEO7"  # choose one from ['CUSTOM', 'SUPERFUND', 'PLANETRISK', 'ISEA3H', 'ISEA4H', 'ISEA4T', 'ISEA4D', 
                            # 'ISEA43H', 'ISEA7H', 'IGEO7', 'FULLER3H', 'FULLER4H', 'FULLER4T', 'FULLER4D', 'FULLER43H', 'FULLER7H']
resolution = 0
address_type = "SEQNUM"
# "Q2DI",  # quad number and (i, j) coordinates on that quad
# "SEQNUM",  # DGGS index - linear address (1 to size-of-DGG), not supported for parameter input_address_type if dggs_aperture_type is SEQUENCE
# "PLANE",  # (x, y) coordinates on unfolded ISEA plane,  only supported for parameter output_address_type;
# "Q2DD",  # quad number and (x, y) coordinates on that quad
# "PROJTRI",  # PROJTRI - triangle number and (x, y) coordinates within that triangle on the ISEA plane
# "VERTEX2DD",  # vertex number, triangle number, and (x, y) coordinates on ISEA plane
# "Z3",  # hexadecimal characters index system Z3 especially usefull for ISEA3H
# "Z3_STRING",  # numerical digits representation of Z3 (as characters, not an integer)
# "Z7",  # hexadecimal characters index system Z7 especially usefull for ISEA7H, also in preset IGEO7
# "Z7_STRING",  # numerical digits representation of Z7 (as characters, not an integer)
# "ZORDER",  # index system ZORDER especially usefull for ISEA3H, ISEA4H and mixed aperture
# "ZORDER_STRING",  # numerical digits representation of ZORDER (as characters, not an integer)
dggrid_id = latlon2dggrid(
    dggrid_instance, dggs_type, 10.775276, 106.706797, resolution,output_address_type='Z7_STRING')
dggrid_id
dggrid_geo = dggrid2geo(dggrid_instance, dggs_type, dggrid_id, resolution)
dggrid_geo
from vgrid.conversion.dggs2geo.dggrid2geo import dggrid2geo
from vgrid.conversion.latlon2dggs import latlon2dggrid

dggs_type = "IGEO7"  # choose one from ['CUSTOM', 'SUPERFUND', 'PLANETRISK', 'ISEA3H', 'ISEA4H', 'ISEA4T', 'ISEA4D', 
                            # 'ISEA43H', 'ISEA7H', 'IGEO7', 'FULLER3H', 'FULLER4H', 'FULLER4T', 'FULLER4D', 'FULLER43H', 'FULLER7H']
resolution = 0
address_type = "SEQNUM"
# "Q2DI",  # quad number and (i, j) coordinates on that quad
# "SEQNUM",  # DGGS index - linear address (1 to size-of-DGG), not supported for parameter input_address_type if dggs_aperture_type is SEQUENCE
# "PLANE",  # (x, y) coordinates on unfolded ISEA plane,  only supported for parameter output_address_type;
# "Q2DD",  # quad number and (x, y) coordinates on that quad
# "PROJTRI",  # PROJTRI - triangle number and (x, y) coordinates within that triangle on the ISEA plane
# "VERTEX2DD",  # vertex number, triangle number, and (x, y) coordinates on ISEA plane
# "Z3",  # hexadecimal characters index system Z3 especially usefull for ISEA3H
# "Z3_STRING",  # numerical digits representation of Z3 (as characters, not an integer)
# "Z7",  # hexadecimal characters index system Z7 especially usefull for ISEA7H, also in preset IGEO7
# "Z7_STRING",  # numerical digits representation of Z7 (as characters, not an integer)
# "ZORDER",  # index system ZORDER especially usefull for ISEA3H, ISEA4H and mixed aperture
# "ZORDER_STRING",  # numerical digits representation of ZORDER (as characters, not an integer)
dggrid_id = latlon2dggrid(
    dggrid_instance, dggs_type, 10.775276, 106.706797, resolution,output_address_type='Z7_STRING')
dggrid_id
dggrid_geo = dggrid2geo(dggrid_instance, dggs_type, dggrid_id, resolution)
dggrid_geo

Out[5]:

	dggrid_igeo7	geometry	resolution
0	6	POLYGON ((146.25 35.26439, 122.15516 9.27918e-...	0

DGGRID to GeoJSON¶

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from vgrid.conversion.dggs2geo.dggrid2geo import dggrid2geojson
dggs_type = "ISEA3H"  # choose one from ['CUSTOM', 'SUPERFUND', 'PLANETRISK', 'ISEA3H', 'ISEA4H', 'ISEA4T', 'ISEA4D', 
                            # 'ISEA43H', 'ISEA7H', 'IGEO7', 'FULLER3H', 'FULLER4H', 'FULLER4T', 'FULLER4D', 'FULLER43H', 'FULLER7H']

dggrid_geojson = dggrid2geojson(
    dggrid_instance, dggs_type, dggrid_id, resolution, address_type
)
dggrid_geojson
from vgrid.conversion.dggs2geo.dggrid2geo import dggrid2geojson
dggs_type = "ISEA3H"  # choose one from ['CUSTOM', 'SUPERFUND', 'PLANETRISK', 'ISEA3H', 'ISEA4H', 'ISEA4T', 'ISEA4D', 
                            # 'ISEA43H', 'ISEA7H', 'IGEO7', 'FULLER3H', 'FULLER4H', 'FULLER4T', 'FULLER4D', 'FULLER43H', 'FULLER7H']

dggrid_geojson = dggrid2geojson(
    dggrid_instance, dggs_type, dggrid_id, resolution, address_type
)
dggrid_geojson

Out[5]:

{'type': 'FeatureCollection',
 'features': [{'id': '0',
   'type': 'Feature',
   'properties': {'dggrid_isea3h': 6, 'resolution': 0},
   'geometry': {'type': 'Polygon',
    'coordinates': [[[146.25000032761872, 35.26438983390351],
      [122.15515744705257, 9.279183913463408e-10],
      [80.34484255294744, -9.279367045877242e-10],
      [56.24999999954312, 35.26438968503936],
      [101.24999999998248, 69.0948429046764],
      [146.25000032761872, 35.26438983390351]]]}}]}

Vector to DGGRID¶

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from vgrid.conversion.vector2dggs.vector2dggrid import vector2dggrid

file_path = (
    "https://raw.githubusercontent.com/opengeoshub/vopendata/main/shape/polygon2.geojson"
)
dggs_type = "ISEA4T"  # choose one from ['CUSTOM', 'SUPERFUND', 'PLANETRISK', 'ISEA3H', 'ISEA4H',
# 'ISEA4T', 'ISEA4D', 'ISEA43H', 'ISEA7H', 'IGEO7', 'FULLER3H', 'FULLER4H', 'FULLER4T', 'FULLER4D', 'FULLER43H', 'FULLER7H']
resolution = 15
vector_to_dggrid = vector2dggrid(
    dggrid_instance,
    dggs_type,
    file_path,
    resolution=resolution,
    compact=True,
    predicate="intersects",
    output_address_type=address_type,
    output_format="gpd",
    split_antimeridian=True,
    aggregate=True,
    include_properties = True
)
# Visualize the output
# vector_to_dggrid
vector_to_dggrid.plot(edgecolor="white")
from vgrid.conversion.vector2dggs.vector2dggrid import vector2dggrid

file_path = (
    "https://raw.githubusercontent.com/opengeoshub/vopendata/main/shape/polygon2.geojson"
)
dggs_type = "ISEA4T"  # choose one from ['CUSTOM', 'SUPERFUND', 'PLANETRISK', 'ISEA3H', 'ISEA4H',
# 'ISEA4T', 'ISEA4D', 'ISEA43H', 'ISEA7H', 'IGEO7', 'FULLER3H', 'FULLER4H', 'FULLER4T', 'FULLER4D', 'FULLER43H', 'FULLER7H']
resolution = 15
vector_to_dggrid = vector2dggrid(
    dggrid_instance,
    dggs_type,
    file_path,
    resolution=resolution,
    compact=True,
    predicate="intersects",
    output_address_type=address_type,
    output_format="gpd",
    split_antimeridian=True,
    aggregate=True,
    include_properties = True
)
# Visualize the output
# vector_to_dggrid
vector_to_dggrid.plot(edgecolor="white")

Processing features: 100%|██████████| 1/1 [00:01<00:00,  1.77s/it]

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<Axes: >

No description has been provided for this image

DGGRID Binning¶

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from vgrid.binning.dggridbin import dggridbin

dggs_type = "isea3h"  # choose one from ['CUSTOM', 'SUPERFUND', 'PLANETRISK', 'ISEA3H', 'ISEA4H',
# 'ISEA4T', 'ISEA4D', 'ISEA43H', 'ISEA7H', 'IGEO7', 'FULLER3H', 'FULLER4H', 'FULLER4T', 'FULLER4D', 'FULLER43H', 'FULLER7H']
file_path = (
    "https://raw.githubusercontent.com/opengeoshub/vopendata/main/csv/housing.csv"
)
stats = "max"
numeric_col="median_house_value"

dggrid_bin = dggridbin(
    dggrid_instance,
    dggs_type,
    file_path,
    resolution=10,
    stats=stats,
    numeric_col=numeric_col,
    # category_col="category",
    output_format="gpd",
)
# dggal_bin.head()
dggrid_bin.plot(
    column=f"{numeric_col}_{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.dggridbin import dggridbin

dggs_type = "isea3h"  # choose one from ['CUSTOM', 'SUPERFUND', 'PLANETRISK', 'ISEA3H', 'ISEA4H',
# 'ISEA4T', 'ISEA4D', 'ISEA43H', 'ISEA7H', 'IGEO7', 'FULLER3H', 'FULLER4H', 'FULLER4T', 'FULLER4D', 'FULLER43H', 'FULLER7H']
file_path = (
    "https://raw.githubusercontent.com/opengeoshub/vopendata/main/csv/housing.csv"
)
stats = "max"
numeric_col="median_house_value"

dggrid_bin = dggridbin(
    dggrid_instance,
    dggs_type,
    file_path,
    resolution=10,
    stats=stats,
    numeric_col=numeric_col,
    # category_col="category",
    output_format="gpd",
)
# dggal_bin.head()
dggrid_bin.plot(
    column=f"{numeric_col}_{stats}",  # numeric column to base the colors on
    cmap="Spectral_r",  # color scheme (matplotlib colormap)
    legend=True,
    linewidth=0.2,  # boundary width (optional)
)

Out[2]:

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DGGRID Resampling¶

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from vgrid.conversion.dggsresample import dggsresample
dggrid_resample = dggsresample(
    source_dggs=dggrid_bin,
    dggs_from=f"dggrid_{dggs_type}",
    dggs_to="a5",
    method = "nearest", # area_weighted, nearest
    dggs_col=f"dggrid_{dggs_type}",
    resample_col=f"{numeric_col}_{stats}",  # column on h3_bin with numbers
    fix_antimeridian=None,
    split_antimeridian=False,
)
dggrid_resample.plot(
    column=f"{numeric_col}_{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.conversion.dggsresample import dggsresample
dggrid_resample = dggsresample(
    source_dggs=dggrid_bin,
    dggs_from=f"dggrid_{dggs_type}",
    dggs_to="a5",
    method = "nearest", # area_weighted, nearest
    dggs_col=f"dggrid_{dggs_type}",
    resample_col=f"{numeric_col}_{stats}",  # column on h3_bin with numbers
    fix_antimeridian=None,
    split_antimeridian=False,
)
dggrid_resample.plot(
    column=f"{numeric_col}_{stats}",  # numeric column to base the colors on
    cmap="Spectral_r",  # color scheme (matplotlib colormap)
    legend=True,
    linewidth=0.2,  # boundary width (optional)
)

Generating A5 cells: 100%|██████████| 2055/2055 [00:00<00:00, 3373.34 cells/s]

Out[3]:

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Raster to DGGRID¶

Download and open raster¶

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

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

rgb.tif already exists. Skip downloading. Set overwrite=True to overwrite.
{'driver': 'GTiff', 'dtype': 'uint8', 'nodata': None, 'width': 240, 'height': 147, 'count': 3, 'crs': CRS.from_wkt('GEOGCS["WGS 84",DATUM["WGS_1984",SPHEROID["WGS 84",6378137,298.257223563,AUTHORITY["EPSG","7030"]],AUTHORITY["EPSG","6326"]],PRIMEM["Greenwich",0,AUTHORITY["EPSG","8901"]],UNIT["degree",0.0174532925199433,AUTHORITY["EPSG","9122"]],AXIS["Latitude",NORTH],AXIS["Longitude",EAST],AUTHORITY["EPSG","4326"]]'), 'transform': Affine(2.6640125000199077e-06, 0.0, 106.708118755,
       0.0, -2.6640136054383103e-06, 10.812568272)}

Out[10]:

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Convert raster to DGGRID¶

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from vgrid.conversion.raster2dggs.raster2dggrid import raster2dggrid

dggs_type = "ISEA3H"  # choose one from ['CUSTOM', 'SUPERFUND', 'PLANETRISK', 'ISEA3H', 'ISEA4H',
# 'ISEA4T', 'ISEA4D', 'ISEA43H', 'ISEA7H', 'IGEO7', 'FULLER3H', 'FULLER4H', 'FULLER4T', 'FULLER4D', 'FULLER43H', 'FULLER7H']
raster_to_dggrid = raster2dggrid(dggrid_instance, dggs_type, raster_file, resolution = 27, 
                                method = "binning", # binnning, nearest
                                stats = 'mean', output_format="gpd")
from vgrid.conversion.raster2dggs.raster2dggrid import raster2dggrid

dggs_type = "ISEA3H"  # choose one from ['CUSTOM', 'SUPERFUND', 'PLANETRISK', 'ISEA3H', 'ISEA4H',
# 'ISEA4T', 'ISEA4D', 'ISEA43H', 'ISEA7H', 'IGEO7', 'FULLER3H', 'FULLER4H', 'FULLER4T', 'FULLER4D', 'FULLER43H', 'FULLER7H']
raster_to_dggrid = raster2dggrid(dggrid_instance, dggs_type, raster_file, resolution = 27, 
                                method = "binning", # binnning, nearest
                                stats = 'mean', output_format="gpd")

Binning raster blocks to DGGRID:  14%|█▍        | 2/14 [08:59<54:14, 271.22s/ block]

Visualize the output¶

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# %pip install folium
# %pip install folium

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import folium

m = folium.Map(tiles="CartoDB positron", max_zoom=28)

dggrid_layer = folium.GeoJson(
    raster_to_dggrid,
    style_function=lambda x: {
        "fillColor": f"rgb({x['properties']['band_1']}, {x['properties']['band_2']}, {x['properties']['band_3']})",
        "fillOpacity": 1,
        "color": "black",
        "weight": 0.5,
    },
    popup=folium.GeoJsonPopup(
        fields=[ f"dggrid_{dggs_type}", "resolution", "band_1", "band_2", "band_3", "cell_area"],
        aliases=["DGGRID ID", "Resolution", "Band 1", "Band 2", "Band 3", "Area (m²)"],
        style="""
            background-color: white;
            border: 2px solid black;
            border-radius: 3px;
            box-shadow: 3px;
        """,
    ),
).add_to(m)

m.fit_bounds(dggrid_layer.get_bounds())

# Display the map
m
import folium

m = folium.Map(tiles="CartoDB positron", max_zoom=28)

dggrid_layer = folium.GeoJson(
    raster_to_dggrid,
    style_function=lambda x: {
        "fillColor": f"rgb({x['properties']['band_1']}, {x['properties']['band_2']}, {x['properties']['band_3']})",
        "fillOpacity": 1,
        "color": "black",
        "weight": 0.5,
    },
    popup=folium.GeoJsonPopup(
        fields=[ f"dggrid_{dggs_type}", "resolution", "band_1", "band_2", "band_3", "cell_area"],
        aliases=["DGGRID ID", "Resolution", "Band 1", "Band 2", "Band 3", "Area (m²)"],
        style="""
            background-color: white;
            border: 2px solid black;
            border-radius: 3px;
            box-shadow: 3px;
        """,
    ),
).add_to(m)

m.fit_bounds(dggrid_layer.get_bounds())

# Display the map
m

Out[5]:

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

DGGRID Generator¶

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from vgrid.generator.dggridgen import dggridgen

dggs_type = "IGEO7"  # choose one from ['CUSTOM', 'SUPERFUND', 'PLANETRISK', 'ISEA3H', 'ISEA4H', 'ISEA4T',
# 'ISEA4D', 'ISEA43H', 'ISEA7H', 'IGEO7', 'FULLER3H', 'FULLER4H', 'FULLER4T', 'FULLER4D', 'FULLER43H', 'FULLER7H']
resolution = 1
# bbox = [106.699007, 10.762811, 106.717674, 10.778649]  # (min_lon, min_lat, max_lon, max_lat)
options = {"densification": 30}
dggrid_gen = dggridgen(
    dggrid_instance, dggs_type=dggs_type, resolution=resolution, output_format="gpd",split_antimeridian = True, aggregate=True, options=options
)

dggrid_gen
dggrid_gen.plot(edgecolor="white")
from vgrid.generator.dggridgen import dggridgen

dggs_type = "IGEO7"  # choose one from ['CUSTOM', 'SUPERFUND', 'PLANETRISK', 'ISEA3H', 'ISEA4H', 'ISEA4T',
# 'ISEA4D', 'ISEA43H', 'ISEA7H', 'IGEO7', 'FULLER3H', 'FULLER4H', 'FULLER4T', 'FULLER4D', 'FULLER43H', 'FULLER7H']
resolution = 1
# bbox = [106.699007, 10.762811, 106.717674, 10.778649]  # (min_lon, min_lat, max_lon, max_lat)
options = {"densification": 30}
dggrid_gen = dggridgen(
    dggrid_instance, dggs_type=dggs_type, resolution=resolution, output_format="gpd",split_antimeridian = True, aggregate=True, options=options
)

dggrid_gen
dggrid_gen.plot(edgecolor="white")

Out[28]:

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DGGRID Inspect¶

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from vgrid.stats.dggridstats import dggridinspect

dggs_type = "IGEO7"  # choose one from ['CUSTOM', 'SUPERFUND', 'PLANETRISK', 'ISEA3H', 'ISEA4H', 'ISEA4T',
# 'ISEA4D', 'ISEA43H', 'ISEA7H', 'IGEO7', 'FULLER3H', 'FULLER4H', 'FULLER4T', 'FULLER4D', 'FULLER43H', 'FULLER7H']
resolution = 2
options = {"densification": 30}
dggrid_inspect = dggridinspect(dggrid_instance, dggs_type, resolution, options= options)

dggrid_inspect.head() 
from vgrid.stats.dggridstats import dggridinspect

dggs_type = "IGEO7"  # choose one from ['CUSTOM', 'SUPERFUND', 'PLANETRISK', 'ISEA3H', 'ISEA4H', 'ISEA4T',
# 'ISEA4D', 'ISEA43H', 'ISEA7H', 'IGEO7', 'FULLER3H', 'FULLER4H', 'FULLER4T', 'FULLER4D', 'FULLER43H', 'FULLER7H']
resolution = 2
options = {"densification": 30}
dggrid_inspect = dggridinspect(dggrid_instance, dggs_type, resolution, options= options)

dggrid_inspect.head() 

Out[ ]:

	cell_id	geometry	dggs_type	cell_area	cell_perimeter	crossed	resolution	norm_area	ipq	zsc	cvh
0	1	POLYGON ((1.90276 56.42667, 1.9831 56.63231, 2...	dggrid_igeo7	8.720078e+11	3.598777e+06	False	2	0.841425	0.846098	0.919051	0.978025
1	2	POLYGON ((-161.65762 62.34934, -161.58076 62.1...	dggrid_igeo7	8.720078e+11	3.598777e+06	False	2	0.841425	0.846098	0.919051	0.978024
6	7	POLYGON ((1.13825 71.41606, 0.75374 71.54114, ...	dggrid_igeo7	1.049443e+12	3.827704e+06	False	2	1.012638	0.900103	0.947763	0.997441
7	8	POLYGON ((3.97788 62.68734, 3.71863 62.8171, 3...	dggrid_igeo7	1.048125e+12	3.844866e+06	False	2	1.011365	0.890965	0.942941	0.995543
8	9	POLYGON ((-151.18725 53.3956, -151.27337 53.22...	dggrid_igeo7	1.044666e+12	3.838643e+06	False	2	1.008027	0.890905	0.942913	0.995547

DGGRID Normalized Area Histogram¶

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from vgrid.stats.dggridstats import dggrid_norm_area_hist

dggrid_norm_area_hist(dggs_type, dggrid_inspect)
from vgrid.stats.dggridstats import dggrid_norm_area_hist

dggrid_norm_area_hist(dggs_type, dggrid_inspect)

Distribution of DGGRID Area Distortions¶

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from vgrid.stats.dggridstats import dggrid_norm_area

dggrid_norm_area(dggs_type, dggrid_inspect, crs="proj=moll")
from vgrid.stats.dggridstats import dggrid_norm_area

dggrid_norm_area(dggs_type, dggrid_inspect, crs="proj=moll")

DGGRID IPQ Compactness Histogram¶

Isoperimetric Inequality (IPQ) Compactness (suggested by Osserman, 1978):

$$C_{IPQ} = \frac{4 \pi A}{p^2}$$ The range of the IPQ compactness metric is [0,1].

A circle represents the maximum compactness with a value of 1.

As shapes become more irregular or elongated, their compactness decreases toward 0.

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from vgrid.stats.dggridstats import dggrid_compactness_ipq_hist

dggrid_compactness_ipq_hist(dggs_type, dggrid_inspect)
from vgrid.stats.dggridstats import dggrid_compactness_ipq_hist

dggrid_compactness_ipq_hist(dggs_type, dggrid_inspect)

Distribution of DGGRID IPQ Compactness¶

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from vgrid.stats.dggridstats import dggrid_compactness_ipq

dggrid_compactness_ipq(dggs_type, dggrid_inspect, crs="proj=moll")
from vgrid.stats.dggridstats import dggrid_compactness_ipq

dggrid_compactness_ipq(dggs_type, dggrid_inspect, crs="proj=moll")

DGGRID Convex hull Compactness Histogram:¶

$$C_{CVH} = \frac{A}{A_{CVH}}$$

The range of the convex hull compactness metric is [0,1].

As shapes become more concave, their convex hull compactness decreases toward 0.

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from vgrid.stats.dggridstats import dggrid_compactness_cvh_hist

dggrid_compactness_cvh_hist(dggs_type, dggrid_inspect)
from vgrid.stats.dggridstats import dggrid_compactness_cvh_hist

dggrid_compactness_cvh_hist(dggs_type, dggrid_inspect)

Distribution of DGGRID Convex hull Compactness¶

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from vgrid.stats.dggridstats import dggrid_compactness_cvh

dggrid_compactness_cvh(dggs_type, dggrid_inspect)
from vgrid.stats.dggridstats import dggrid_compactness_cvh

dggrid_compactness_cvh(dggs_type, dggrid_inspect)

DGGRID Statistics¶

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

In [14]:

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from vgrid.stats.dggridstats import dggridstats

dggs_type = "IGEO7"  # choose one from ['CUSTOM', 'SUPERFUND', 'PLANETRISK', 'ISEA3H', 'ISEA4H', 'ISEA4T',
# 'ISEA4D', 'ISEA43H', 'ISEA7H', 'IGEO7', 'FULLER3H', 'FULLER4H', 'FULLER4T', 'FULLER4D', 'FULLER43H', 'FULLER7H']
dggrid_stats = dggridstats(dggrid_instance, dggs_type, "m")
dggrid_stats
from vgrid.stats.dggridstats import dggridstats

dggs_type = "IGEO7"  # choose one from ['CUSTOM', 'SUPERFUND', 'PLANETRISK', 'ISEA3H', 'ISEA4H', 'ISEA4T',
# 'ISEA4D', 'ISEA43H', 'ISEA7H', 'IGEO7', 'FULLER3H', 'FULLER4H', 'FULLER4T', 'FULLER4D', 'FULLER43H', 'FULLER7H']
dggrid_stats = dggridstats(dggrid_instance, dggs_type, "m")
dggrid_stats

Out[14]:

	resolution	number_of_cells	area_m2	cls_m	intercell_m
0	0	12	5.100656e+13	8.199500e+06	7.053652e+06
1	1	72	7.286652e+12	3.053223e+06	2.666030e+06
2	2	492	1.040950e+12	1.151643e+06	1.007665e+06
3	3	3432	1.487072e+11	4.351531e+05	3.808614e+05
4	4	24012	2.124388e+10	1.644656e+05	1.439521e+05
5	5	168072	3.034840e+09	6.216178e+04	5.440878e+04
6	6	1176492	4.335486e+08	2.349492e+04	2.056458e+04
7	7	8235432	6.193552e+07	8.880245e+03	7.772682e+03
8	8	57648012	8.847931e+06	3.356417e+03	2.937798e+03
9	9	403536072	1.263990e+06	1.268606e+03	1.110383e+03
10	10	2824752492	1.805700e+05	4.794882e+02	4.196854e+02
11	11	19773267432	2.579570e+04	1.812295e+02	1.586262e+02
12	12	138412872012	3.685100e+03	6.849830e+01	5.995506e+01
13	13	968890104072	5.264000e+02	2.588990e+01	2.266088e+01
14	14	6782230728492	7.520000e+01	9.785500e+00	8.565008e+00
15	15	47475615099432	1.070000e+01	3.698600e+00	3.237269e+00
16	16	332329305696012	1.500000e+00	1.397900e+00	1.223573e+00
17	17	2326305139872072	2.000000e-01	5.284000e-01	4.624670e-01
18	18	16284135979104492	0.000000e+00	1.997000e-01	1.747961e-01
19	19	113988951853731424	0.000000e+00	7.550000e-02	6.606671e-02
20	20	797922662976120064	0.000000e+00	2.850000e-02	2.497087e-02