03 a5

Working with A5 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

latlon2a5¶

In [2]:

from vgrid.conversion.latlon2dggs import latlon2a5

lat = 10.775276
lon = 106.706797
res = 12
a5_hex = latlon2a5(lat, lon, res)
a5_hex

from vgrid.conversion.latlon2dggs import latlon2a5 lat = 10.775276 lon = 106.706797 res = 12 a5_hex = latlon2a5(lat, lon, res) a5_hex

Out[2]:

'7a9408e800000000'

A5 to Shapely Polygon¶

In [3]:

from vgrid.conversion.dggs2geo.a52geo import a52geo
a5_geo = a52geo(a5_hex)
a5_geo

from vgrid.conversion.dggs2geo.a52geo import a52geo a5_geo = a52geo(a5_hex) a5_geo

Out[3]:

A5 to GeoJSON¶

In [4]:

from vgrid.conversion.dggs2geo.a52geo import a52geojson
import json
a5_geojson = a52geojson(a5_hex)
a5_geojson

from vgrid.conversion.dggs2geo.a52geo import a52geojson import json a5_geojson = a52geojson(a5_hex) a5_geojson

Out[4]:

{'type': 'FeatureCollection',
 'features': [{'type': 'Feature',
   'geometry': {'type': 'Polygon',
    'coordinates': (((106.71239575763866, 10.78701022358562),
      (106.70535378338678, 10.778851358998635),
      (106.70299425998425, 10.76831657110021),
      (106.71315636275037, 10.767875103340318),
      (106.71725325821848, 10.777905595108349),
      (106.71239575763866, 10.78701022358562)),)},
   'properties': {'a5': '7a9408e800000000',
    'resolution': 12,
    'center_lat': 10.7758211,
    'center_lon': 106.7103093,
    'avg_edge_len': 1165.531,
    'cell_area': 2026822.224,
    'cell_perimeter': 5827.657}}]}

Vector to A5¶

In [1]:

from vgrid.conversion.vector2dggs.vector2a5 import vector2a5

file_path = (
    "https://raw.githubusercontent.com/opengeoshub/vopendata/main/shape/polygon.geojson"
)
vector_to_a5 = vector2a5(file_path, compact=False, resolution=16, 
                predicate = "intersects", output_format="gpd")
# Visualize vector_to_a5
vector_to_a5.plot(edgecolor="white")

from vgrid.conversion.vector2dggs.vector2a5 import vector2a5 file_path = ( "https://raw.githubusercontent.com/opengeoshub/vopendata/main/shape/polygon.geojson" ) vector_to_a5 = vector2a5(file_path, compact=False, resolution=16, predicate = "intersects", output_format="gpd") # Visualize vector_to_a5 vector_to_a5.plot(edgecolor="white")

ERROR [fiona._env:387  _read_file_fiona()] PROJ: proj_identify: Cannot find proj.db
Processing features: 100%|██████████| 4/4 [00:00<00:00,  7.80it/s]

Out[1]:

<Axes: >

A5 Compact¶

In [2]:

from vgrid.conversion.dggscompact.a5compact import a5compact

a5_compacted = a5compact(vector_to_a5, a5_hex="a5", output_format="gpd")
a5_compacted.plot(edgecolor="white")

from vgrid.conversion.dggscompact.a5compact import a5compact a5_compacted = a5compact(vector_to_a5, a5_hex="a5", output_format="gpd") a5_compacted.plot(edgecolor="white")

Out[2]:

<Axes: >

A5 Expand¶

In [3]:

from vgrid.conversion.dggscompact.a5compact import a5expand

a5_expanded = a5expand(vector_to_a5, resolution=17, output_format="gpd")
a5_expanded.plot(edgecolor="white")

from vgrid.conversion.dggscompact.a5compact import a5expand a5_expanded = a5expand(vector_to_a5, resolution=17, output_format="gpd") a5_expanded.plot(edgecolor="white")

Out[3]:

<Axes: >

A5 Binning¶

In [5]:

from vgrid.binning.a5bin import a5bin
file_path = (
    "https://raw.githubusercontent.com/opengeoshub/vopendata/main/csv/dist1_pois.csv"
)
stats = "variety"
a5_bin = a5bin(file_path, resolution=15, stats=stats, 
                numeric_field="confidence",
                # category="category",
                output_format="gpd")

a5_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.a5bin import a5bin file_path = ( "https://raw.githubusercontent.com/opengeoshub/vopendata/main/csv/dist1_pois.csv" ) stats = "variety" a5_bin = a5bin(file_path, resolution=15, stats=stats, numeric_field="confidence", # category="category", output_format="gpd") a5_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) )

Generating A5 DGGS: 100%|██████████| 945/945 [00:00<00:00, 2271.71 cells/s]

Out[5]:

<Axes: >

Raster to A5¶

In [9]:

# %pip install folium

# %pip install folium

In [10]:

from vgrid.conversion.raster2dggs.raster2a5 import raster2a5
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_a5 =  raster2a5(raster_file,resolution=23,output_format="gpd")
# Visualize raster_to_a5
import folium

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

a5_layer = folium.GeoJson(
    raster_to_a5,
    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=["a5", "resolution", "band_1", "band_2", "band_3", "cell_area"],
        aliases=["A5 Hex", "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(a5_layer.get_bounds())

# Display the map
m

from vgrid.conversion.raster2dggs.raster2a5 import raster2a5 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_a5 = raster2a5(raster_file,resolution=23,output_format="gpd") # Visualize raster_to_a5 import folium m = folium.Map(tiles="CartoDB positron", max_zoom=28) a5_layer = folium.GeoJson( raster_to_a5, 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=["a5", "resolution", "band_1", "band_2", "band_3", "cell_area"], aliases=["A5 Hex", "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(a5_layer.get_bounds()) # Display the map m

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.

Building GeoDataFrame: 100%|██████████| 6392/6392 [00:35<00:00, 182.26 cells/s]

Out[10]:

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

A5 Generator¶

In [11]:

from vgrid.generator.a5grid import a5grid

a5_grid = a5grid(resolution=2,output_format="gpd")
# a5_grid = a5grid(resolution=16,bbox=[106.699007, 10.762811, 106.717674, 10.778649],output_format="gpd")  
a5_grid.plot(edgecolor='white')

from vgrid.generator.a5grid import a5grid a5_grid = a5grid(resolution=2,output_format="gpd") # a5_grid = a5grid(resolution=16,bbox=[106.699007, 10.762811, 106.717674, 10.778649],output_format="gpd") a5_grid.plot(edgecolor='white')

Generating A5 DGGS: 100%|██████████| 648/648 [00:05<00:00, 117.51 cells/s]

Out[11]:

<Axes: >

A5 Inspect¶

In [2]:

from vgrid.stats.a5stats import a5inspect
resolution = 5
a5_inspect = a5inspect(resolution)
a5_inspect.head()

from vgrid.stats.a5stats import a5inspect resolution = 5 a5_inspect = a5inspect(resolution) a5_inspect.head()

Generating A5 DGGS:   0%|          | 0/41472 [00:00<?, ? cells/s]

Generating A5 DGGS: 100%|██████████| 41472/41472 [00:20<00:00, 2054.53 cells/s]

Out[2]:

	a5	resolution	center_lat	center_lon	avg_edge_len	cell_area	cell_perimeter	geometry	crossed	norm_area	ipq	zsc
0	c402000000000000	5	-88.778429	143.247090	146990.185	3.311882e+10	734950.924	POLYGON ((267 -90, 168.65049 -89.33266, 168.65...	True	0.997333	0.770492	0.877748
1	c406000000000000	5	-87.450371	171.582970	148114.039	3.318690e+10	740570.194	POLYGON ((159 -86.10242, 157.01838 -86.76115, ...	True	0.999383	0.760403	0.871983
2	c412000000000000	5	-86.388526	-169.620901	148227.201	3.317992e+10	741136.006	POLYGON ((-181.58039 -86.57893, -174.9477 -87....	True	0.999172	0.759083	0.871225
3	c44a000000000000	5	-84.470901	-177.258397	150415.196	3.323008e+10	752075.979	POLYGON ((-165 -85.42943, -168.30096 -84.80452...	True	1.000683	0.738274	0.859201
4	c446000000000000	5	-82.927396	-173.663764	149871.712	3.319273e+10	749358.559	POLYGON ((-181.52273 -81.83357, -180.65159 -82...	True	0.999558	0.742803	0.861832

Distribution of A5 Area Distortions¶

In [3]:

from vgrid.stats.a5stats import a5_norm_area
a5_norm_area(a5_inspect)

from vgrid.stats.a5stats import a5_norm_area a5_norm_area(a5_inspect)

ERROR [fiona._env:387  _read_file_fiona()] PROJ: proj_identify: Cannot find proj.db

Distribution of A5 IPQ Compactness¶

In [4]:

from vgrid.stats.a5stats import a5_compactness
a5_compactness(a5_inspect)

from vgrid.stats.a5stats import a5_compactness a5_compactness(a5_inspect)

ERROR [fiona._env:387  _read_file_fiona()] PROJ: proj_identify: Cannot find proj.db

A5 Statistics¶

In [1]:

from vgrid.stats.a5stats import a5stats
a5stats('km')

from vgrid.stats.a5stats import a5stats a5stats('km')

Out[1]:

	resolution	number_of_cells	avg_edge_len_km	avg_cell_area_km2
0	0	12	4970.475286	4.250547e+07
1	1	60	2222.864124	8.501094e+06
2	2	240	1111.432062	2.125273e+06
3	3	960	555.716031	5.313184e+05
4	4	3840	277.858015	1.328296e+05
5	5	15360	138.929008	3.320740e+04
6	6	61440	69.464504	8.301849e+03
7	7	245760	34.732252	2.075462e+03
8	8	983040	17.366126	5.188656e+02
9	9	3932160	8.683063	1.297164e+02
10	10	15728640	4.341531	3.242910e+01
11	11	62914560	2.170766	8.107275e+00
12	12	251658240	1.085383	2.026819e+00
13	13	1006632960	0.542691	5.067047e-01
14	14	4026531840	0.271346	1.266762e-01
15	15	16106127360	0.135673	3.166904e-02
16	16	64424509440	0.067836	7.917261e-03
17	17	257698037760	0.033918	1.979315e-03
18	18	1030792151040	0.016959	4.948288e-04
19	19	4123168604160	0.008480	1.237072e-04
20	20	16492674416640	0.004240	3.092680e-05
21	21	65970697666560	0.002120	7.731700e-06
22	22	263882790666240	0.001060	1.932925e-06
23	23	1055531162664960	0.000530	4.832312e-07
24	24	4222124650659840	0.000265	1.208078e-07
25	25	16888498602639360	0.000132	3.020195e-08
26	26	67553994410557440	0.000066	7.550488e-09
27	27	270215977642229760	0.000033	1.887622e-09
28	28	1080863910568919040	0.000017	4.719055e-10
29	29	4323455642275676160	0.000008	1.179764e-10