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.

In [20]:

# %pip install vgrid --upgrade

# %pip install vgrid --upgrade

latlon2olc¶

In [21]:

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

Out[21]:

'7P28QPG4+'

OLC to Shapely Polygon¶

In [22]:

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

Out[22]:

OLC to GeoJSON¶

In [23]:

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

Out[23]:

{'type': 'FeatureCollection',
 'features': [{'type': 'Feature',
   'geometry': {'type': 'Polygon',
    'coordinates': (((106.705, 10.775),
      (106.7075, 10.775),
      (106.7075, 10.7775),
      (106.705, 10.7775),
      (106.705, 10.775)),)},
   'properties': {'olc': '7P28QPG4+',
    'resolution': 8,
    'center_lat': 10.77625,
    'center_lon': 106.70625,
    'cell_width': 273.424,
    'cell_height': 276.533,
    'cell_area': 75610.384,
    'cell_perimeter': 1099.911}}]}

Vector to OLC¶

In [24]:

from vgrid.conversion.vector2dggs.vector2olc import vector2olc

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

Processing features: 100%|██████████| 4/4 [00:00<00:00,  6.02it/s]

Out[24]:

<Axes: >

OLC Compact¶

In [25]:

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")

Out[25]:

<Axes: >

OLC Expand¶

In [26]:

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()

Out[26]:

<Axes: >

OLC Binning¶

In [27]:

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) )

Out[27]:

<Axes: >

Raster to OLC¶

In [28]:

# %pip install folium

# %pip install folium

In [29]:

from vgrid.conversion.raster2dggs.raster2olc    import raster2olc
from vgrid.utils.io import download_file          

raster_url = (
    "https://raw.githubusercontent.com/opengeoshub/vopendata/main/raster/rgb.tif"
)
raster_file = download_file(raster_url)

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 from vgrid.utils.io import download_file raster_url = ( "https://raw.githubusercontent.com/opengeoshub/vopendata/main/raster/rgb.tif" ) raster_file = download_file(raster_url) 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

WARNING [rasterio._env:368  open()] CPLE_AppDefined in PROJ: proj_create_from_database: Cannot find proj.db
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.
Nearest olc resolution determined: 12

Resampling: 100%|██████████| 6478/6478 [00:00<00:00, 8643.38 cells/s]
Converting raster to OLC: 100%|██████████| 6478/6478 [00:00<00:00, 6720.02 cells/s]

Out[29]:

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

OLC Generator¶

In [30]:

from vgrid.generator.olcgrid import olcgrid
olc_grid = olcgrid(resolution=2,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=2,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%|██████████| 162/162 [00:00<00:00, 3480.27 cells/s]

Out[30]:

<Axes: >

OLC Inspect¶

In [31]:

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 [00:24<00:00, 2649.47 cells/s]

Out[31]:

	olc	resolution	center_lat	center_lon	cell_width	cell_height	cell_area	cell_perimeter	geometry	crossed	norm_area	ipq	zsc
0	22220000+	4	-89.5	-179.5	0.0	111693.865	1.088612e+08	225337.032	POLYGON ((-180 -90, -180 -89, -179 -89, -179 -...	False	0.01383	0.026941	0.164138
1	22230000+	4	-89.5	-178.5	0.0	111693.865	1.088612e+08	225337.032	POLYGON ((-179 -90, -179 -89, -178 -89, -178 -...	False	0.01383	0.026941	0.164138
2	22240000+	4	-89.5	-177.5	0.0	111693.865	1.088612e+08	225337.032	POLYGON ((-178 -90, -178 -89, -177 -89, -177 -...	False	0.01383	0.026941	0.164138
3	22250000+	4	-89.5	-176.5	0.0	111693.865	1.088612e+08	225337.032	POLYGON ((-177 -90, -177 -89, -176 -89, -176 -...	False	0.01383	0.026941	0.164138
4	22260000+	4	-89.5	-175.5	0.0	111693.865	1.088612e+08	225337.032	POLYGON ((-176 -90, -176 -89, -175 -89, -175 -...	False	0.01383	0.026941	0.164138

Distribution of OLC Area Distortions¶

In [32]:

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)

Distribution of OLC IPQ Compactness¶

In [33]:

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

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

OLC Statistics¶

In [34]:

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

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

Out[34]:

	resolution	number_of_cells	avg_edge_len_km	avg_cell_area_km2
0	2	162	1774.416309	3.148553e+06
1	4	64800	88.720815	7.871383e+03
2	6	25920000	4.436041	1.967846e+01
3	8	10368000000	0.221802	4.919614e-02
4	10	4147200000000	0.011090	1.229904e-04
5	11	82944000000000	0.002480	6.149518e-06
6	12	1658880000000000	0.000555	3.074759e-07
7	13	33177600000000000	0.000124	1.537380e-08
8	14	663552000000000000	0.000028	7.686898e-10
9	15	13271040000000000000	0.000006	3.843449e-11