11 olc

Working with OLC 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

latlon2olc¶

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from vgrid.conversion.latlon2dggs import latlon2olc

lat = 10.775276
lon = 106.706797
res = 8
olc_id = latlon2olc(lat, lon, res)
olc_id
from vgrid.conversion.latlon2dggs import latlon2olc

lat = 10.775276
lon = 106.706797
res = 8
olc_id = latlon2olc(lat, lon, res)
olc_id

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'7P28QPG4+'

OLC to Polygon¶

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from vgrid.conversion.dggs2geo.olc2geo  import olc2geo

olc_geo = olc2geo(olc_id)
olc_geo
from vgrid.conversion.dggs2geo.olc2geo  import olc2geo

olc_geo = olc2geo(olc_id)
olc_geo

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OLC to GeoJSON¶

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from vgrid.conversion.dggs2geo.olc2geo import olc2geojson

olc_geojson = olc2geojson(olc_id)
# olc_geojson
from vgrid.conversion.dggs2geo.olc2geo import olc2geojson

olc_geojson = olc2geojson(olc_id)
# olc_geojson

Vector to OLC¶

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from vgrid.conversion.vector2dggs.vector2olc import vector2olc

file_path = (
    "https://raw.githubusercontent.com/opengeoshub/vopendata/main/shape/polygon2.geojson"
)
vector_to_olc = vector2olc(file_path, resolution=8, compact=True, topology = True,
                            predicate = "intersects",output_format="gpd")
# Visualize the output
vector_to_olc.plot(edgecolor="white")
from vgrid.conversion.vector2dggs.vector2olc import vector2olc

file_path = (
    "https://raw.githubusercontent.com/opengeoshub/vopendata/main/shape/polygon2.geojson"
)
vector_to_olc = vector2olc(file_path, resolution=8, compact=True, topology = True,
                            predicate = "intersects",output_format="gpd")
# Visualize the output
vector_to_olc.plot(edgecolor="white")

Processing features: 100%|██████████| 1/1 [00:00<00:00, 10.04it/s]

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OLC Compact¶

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from vgrid.conversion.dggscompact.olccompact import olccompact

olc_compacted = olccompact(vector_to_olc, olc_id="olc", output_format="gpd")
olc_compacted.plot(edgecolor="white")
from vgrid.conversion.dggscompact.olccompact import olccompact

olc_compacted = olccompact(vector_to_olc, olc_id="olc", output_format="gpd")
olc_compacted.plot(edgecolor="white")

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OLC Expand¶

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from vgrid.conversion.dggscompact.olccompact import olcexpand

olc_expanded = olcexpand(vector_to_olc, resolution=10, output_format="gpd")
olc_expanded.plot()
from vgrid.conversion.dggscompact.olccompact import olcexpand

olc_expanded = olcexpand(vector_to_olc, resolution=10, output_format="gpd")
olc_expanded.plot()

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OLC Binning¶

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from vgrid.binning.olcbin import olcbin
import geopandas as gpd
file_path = (
    "https://raw.githubusercontent.com/opengeoshub/vopendata/main/csv/dist1_pois.csv"
)
stats = "count"
olc_bin =  olcbin(file_path, resolution=8, stats=stats, 
                # numeric_field="confidence",
                # category="category",
                output_format="gpd")
olc_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.olcbin import olcbin
import geopandas as gpd
file_path = (
    "https://raw.githubusercontent.com/opengeoshub/vopendata/main/csv/dist1_pois.csv"
)
stats = "count"
olc_bin =  olcbin(file_path, resolution=8, stats=stats, 
                # numeric_field="confidence",
                # category="category",
                output_format="gpd")
olc_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)
)   

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Raster to OLC¶

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"
)
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"
)
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

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["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(2.6640125000199077e-06, 0.0, 106.708118755,
       0.0, -2.6640136054383103e-06, 10.812568272)}

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Convert raster to OLC¶

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# %pip install folium
# %pip install folium

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from vgrid.conversion.raster2dggs.raster2olc    import raster2olc

raster_to_olc =  raster2olc(raster_file, output_format="gpd")

# Visualize the output
import folium

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

olc_layer = folium.GeoJson(
    raster_to_olc,
    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=["olc", "band_1", "band_2", "band_3"],
        aliases=["OLC 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(olc_layer.get_bounds())

# Display the map
m
from vgrid.conversion.raster2dggs.raster2olc    import raster2olc

raster_to_olc =  raster2olc(raster_file, output_format="gpd")

# Visualize the output
import folium

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

olc_layer = folium.GeoJson(
    raster_to_olc,
    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=["olc", "band_1", "band_2", "band_3"],
        aliases=["OLC 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(olc_layer.get_bounds())

# Display the map
m

Cell size: 0.08638527081938627 m2
Nearest olc resolution determined: 12

Converting raster to OLC: 100%|██████████| 6478/6478 [00:01<00:00, 5111.77 cells/s]

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

OLC Generator¶

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from vgrid.generator.olcgrid import olcgrid
olc_grid = olcgrid(resolution=4,output_format="gpd")
# olc_grid = olcgrid(resolution=8,bbox=[106.699007, 10.762811, 106.717674, 10.778649],output_format="gpd")
olc_grid.plot(edgecolor="white")
from vgrid.generator.olcgrid import olcgrid
olc_grid = olcgrid(resolution=4,output_format="gpd")
# olc_grid = olcgrid(resolution=8,bbox=[106.699007, 10.762811, 106.717674, 10.778649],output_format="gpd")
olc_grid.plot(edgecolor="white")

Generating OLC DGGS: 100%|██████████| 64800/64800 [00:06<00:00, 9444.80 cells/s]

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OLC Inspect¶

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from vgrid.stats.olcstats import olcinspect 
resolution = 4
olc_inspect = olcinspect(resolution)    
olc_inspect.head()
from vgrid.stats.olcstats import olcinspect 
resolution = 4
olc_inspect = olcinspect(resolution)    
olc_inspect.head()

Generating OLC DGGS: 100%|██████████| 64800/64800 [00:07<00:00, 8186.32 cells/s]

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	olc	resolution	center_lat	center_lon	cell_height	cell_area	cell_perimeter	geometry	crossed	norm_area	ipq	zsc	cvh
0	22220000+	4	-89.5	-179.5	111693.864914	1.088612e+08	225337.031822	POLYGON ((-180 -90, -180 -89, -179 -89, -179 -...	False	0.01383	0.026941	0.164138	1.0
1	22230000+	4	-89.5	-178.5	111693.864914	1.088612e+08	225337.031822	POLYGON ((-179 -90, -179 -89, -178 -89, -178 -...	False	0.01383	0.026941	0.164138	1.0
2	22240000+	4	-89.5	-177.5	111693.864914	1.088612e+08	225337.031822	POLYGON ((-178 -90, -178 -89, -177 -89, -177 -...	False	0.01383	0.026941	0.164138	1.0
3	22250000+	4	-89.5	-176.5	111693.864914	1.088612e+08	225337.031822	POLYGON ((-177 -90, -177 -89, -176 -89, -176 -...	False	0.01383	0.026941	0.164138	1.0
4	22260000+	4	-89.5	-175.5	111693.864914	1.088612e+08	225337.031822	POLYGON ((-176 -90, -176 -89, -175 -89, -175 -...	False	0.01383	0.026941	0.164138	1.0

OLC Normalized Area Histogram¶

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from vgrid.stats.olcstats import olc_norm_area_hist
olc_norm_area_hist(olc_inspect)
from vgrid.stats.olcstats import olc_norm_area_hist
olc_norm_area_hist(olc_inspect)

Distribution of OLC Area Distortions¶

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from vgrid.stats.olcstats import olc_norm_area
olc_norm_area(olc_inspect)
from vgrid.stats.olcstats import olc_norm_area
olc_norm_area(olc_inspect)

OLC 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.olcstats import olc_compactness_ipq_hist
olc_compactness_ipq_hist(olc_inspect)
from vgrid.stats.olcstats import olc_compactness_ipq_hist
olc_compactness_ipq_hist(olc_inspect)

Distribution of OLC IPQ Compactness¶

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from vgrid.stats.olcstats import olc_compactness_ipq
olc_compactness_ipq(olc_inspect)
from vgrid.stats.olcstats import olc_compactness_ipq
olc_compactness_ipq(olc_inspect)

OLC 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.olcstats import olc_compactness_cvh_hist
olc_compactness_cvh_hist(olc_inspect)
from vgrid.stats.olcstats import olc_compactness_cvh_hist
olc_compactness_cvh_hist(olc_inspect)

Distribution of OLC Convex hull Compactness¶

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from vgrid.stats.olcstats import olc_compactness_cvh
olc_compactness_cvh(olc_inspect)
from vgrid.stats.olcstats import olc_compactness_cvh
olc_compactness_cvh(olc_inspect)

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OLC Statistics¶

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

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from vgrid.stats import olcstats
olcstats()
from vgrid.stats import olcstats
olcstats()

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	resolution	number_of_cells	avg_edge_len_m	avg_cell_area_m2	cls_m
0	2	162	1.774416e+06	3.148553e+12	2.004280e+06
1	4	64800	8.872082e+04	7.871383e+09	1.001110e+05
2	6	25920000	4.436041e+03	1.967846e+07	5.005536e+03
3	8	10368000000	2.218020e+02	4.919614e+04	2.502768e+02
4	10	4147200000000	1.109010e+01	1.229904e+02	1.251384e+01
5	11	82944000000000	2.479822e+00	6.149518e+00	2.798180e+00
6	12	1658880000000000	5.545051e-01	3.074759e-01	6.256920e-01
7	13	33177600000000000	1.239911e-01	1.537380e-02	1.399090e-01
8	14	663552000000000000	2.772525e-02	7.686898e-04	3.128460e-02
9	15	13271040000000000000	6.199555e-03	3.843449e-05	6.995449e-03