02 s2

Working with S2 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¶

In [1]:

# %pip install vgrid --upgrade

# %pip install vgrid --upgrade

latlon2s2¶

In [2]:

from vgrid.conversion.latlon2dggs import latlon2s2

lat = 10.775276
lon = 106.706797
res = 12
s2_token = latlon2s2(lat, lon, res)
s2_token

from vgrid.conversion.latlon2dggs import latlon2s2 lat = 10.775276 lon = 106.706797 res = 12 s2_token = latlon2s2(lat, lon, res) s2_token

Out[2]:

'31752f5'

S2 to Shapely Polygon¶

In [3]:

from vgrid.conversion.dggs2geo.s22geo import s22geo
s2_geo = s22geo(s2_token)
s2_geo

from vgrid.conversion.dggs2geo.s22geo import s22geo s2_geo = s22geo(s2_token) s2_geo

Out[3]:

S2 to GeoJSON¶

In [4]:

from vgrid.conversion.dggs2geo.s22geo import s22geojson
import json
s2_geojson = s22geojson(s2_token)
s2_geojson

from vgrid.conversion.dggs2geo.s22geo import s22geojson import json s2_geojson = s22geojson(s2_token) s2_geojson

Out[4]:

{'type': 'FeatureCollection',
 'features': [{'type': 'Feature',
   'geometry': {'type': 'Polygon',
    'coordinates': (((106.69987670323877, 10.767383458965485),
      (106.72346389403498, 10.766083802809371),
      (106.72346389403498, 10.787862253881839),
      (106.69987670323877, 10.789164413734015),
      (106.69987670323877, 10.767383458965485)),)},
   'properties': {'s2': '31752f5',
    'resolution': 12,
    'center_lat': 10.7776235,
    'center_lon': 106.7116701,
    'avg_edge_len': 2496.415,
    'cell_area': 6214807.33,
    'cell_perimeter': 9985.661}}]}

Vector to S2¶

In [5]:

from vgrid.conversion.vector2dggs.vector2s2 import vector2s2

file_path = (
    "https://raw.githubusercontent.com/opengeoshub/vopendata/main/shape/polygon.geojson"
)
vector_to_s2 = vector2s2(file_path, resolution=17, compact=False, 
                predicate = "intersects", output_format="gpd")
# Visualize vector_to_s2
vector_to_s2.plot(edgecolor="white")

from vgrid.conversion.vector2dggs.vector2s2 import vector2s2 file_path = ( "https://raw.githubusercontent.com/opengeoshub/vopendata/main/shape/polygon.geojson" ) vector_to_s2 = vector2s2(file_path, resolution=17, compact=False, predicate = "intersects", output_format="gpd") # Visualize vector_to_s2 vector_to_s2.plot(edgecolor="white")

Processing features: 100%|██████████| 4/4 [00:00<00:00, 11.68it/s]

Out[5]:

<Axes: >

S2 Compact¶

In [6]:

from vgrid.conversion.dggscompact.s2compact import s2compact

s2_compacted = s2compact(vector_to_s2, s2_token="s2", output_format="gpd")
s2_compacted.plot(edgecolor="white")

from vgrid.conversion.dggscompact.s2compact import s2compact s2_compacted = s2compact(vector_to_s2, s2_token="s2", output_format="gpd") s2_compacted.plot(edgecolor="white")

Out[6]:

<Axes: >

S2 Expand¶

In [7]:

from vgrid.conversion.dggscompact.s2compact import s2expand

s2_expanded = s2expand(vector_to_s2, resolution=18, output_format="gpd")
s2_expanded.plot(edgecolor="white")

from vgrid.conversion.dggscompact.s2compact import s2expand s2_expanded = s2expand(vector_to_s2, resolution=18, output_format="gpd") s2_expanded.plot(edgecolor="white")

Out[7]:

<Axes: >

S2 Binning¶

In [3]:

from vgrid.binning.s2bin import s2bin
import geopandas as gpd
file_path = (
    "https://raw.githubusercontent.com/opengeoshub/vopendata/main/csv/dist1_pois.csv"
)
stats = "count"
s2_bin = s2bin(file_path, resolution=16, stats=stats, 
                # numeric_field="confidence",
                # category="category",
                output_format="gpd")
s2_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.s2bin import s2bin import geopandas as gpd file_path = ( "https://raw.githubusercontent.com/opengeoshub/vopendata/main/csv/dist1_pois.csv" ) stats = "count" s2_bin = s2bin(file_path, resolution=16, stats=stats, # numeric_field="confidence", # category="category", output_format="gpd") s2_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 S2 DGGS: 100%|██████████| 737/737 [00:00<00:00, 1236.72 cells/s]

Out[3]:

<Axes: >

Raster to S2¶

In [9]:

# %pip install folium

# %pip install folium

In [10]:

from vgrid.conversion.raster2dggs.raster2s2 import raster2s2
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_s2 =  raster2s2(raster_file,output_format="gpd")
# Visualize raster_to_s2
import folium

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

s2_layer = folium.GeoJson(
    raster_to_s2,
    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=["s2", "resolution", "band_1", "band_2", "band_3", "cell_area"],
        aliases=["S2 Token", "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(s2_layer.get_bounds())

# Display the map
m

from vgrid.conversion.raster2dggs.raster2s2 import raster2s2 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_s2 = raster2s2(raster_file,output_format="gpd") # Visualize raster_to_s2 import folium m = folium.Map(tiles="CartoDB positron", max_zoom=28) s2_layer = folium.GeoJson( raster_to_s2, 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=["s2", "resolution", "band_1", "band_2", "band_3", "cell_area"], aliases=["S2 Token", "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(s2_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 s2 resolution determined: 24

Converting raster to S2: 100%|██████████| 8231/8231 [00:03<00:00, 2724.89 cells/s]

Out[10]:

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

S2 Generator¶

In [11]:

from vgrid.generator.s2grid import s2grid

s2_grid = s2grid(resolution=0,output_format="gpd")
# s2_grid = s2grid(resolution=16,bbox=[106.699007, 10.762811, 106.717674, 10.778649],output_format="gpd")  
s2_grid.plot(edgecolor='white')

from vgrid.generator.s2grid import s2grid s2_grid = s2grid(resolution=0,output_format="gpd") # s2_grid = s2grid(resolution=16,bbox=[106.699007, 10.762811, 106.717674, 10.778649],output_format="gpd") s2_grid.plot(edgecolor='white')

Generating S2 DGGS: 100%|██████████| 6/6 [00:00<00:00, 730.91 cells/s]

Out[11]:

<Axes: >

S2 Inspect¶

In [12]:

from vgrid.stats.s2stats import s2inspect
resolution = 6
s2_inspect = s2inspect(resolution)
s2_inspect.head()

from vgrid.stats.s2stats import s2inspect resolution = 6 s2_inspect = s2inspect(resolution) s2_inspect.head()

Generating S2 DGGS: 100%|██████████| 24576/24576 [00:12<00:00, 1957.86 cells/s]

Out[12]:

	s2	resolution	center_lat	center_lon	avg_edge_len	cell_area	cell_perimeter	geometry	crossed	norm_area	ipq	zsc
0	0001	6	-34.976691	-44.394709	126254.075	1.391628e+10	505016.299	POLYGON ((-45 -35.26439, -43.79085 -35.82442, ...	False	0.670514	0.685681	0.828047
1	0003	6	-35.529669	-43.170222	127426.102	1.427186e+10	509704.406	POLYGON ((-43.79085 -35.82442, -42.55096 -36.3...	False	0.687647	0.690325	0.830846
2	0005	6	-34.378795	-43.170143	128679.032	1.464789e+10	514716.129	POLYGON ((-43.79085 -34.68431, -42.55096 -35.2...	False	0.705765	0.694783	0.833525
3	0007	6	-33.834099	-44.394632	127398.731	1.426656e+10	509594.924	POLYGON ((-45 -34.13233, -43.79085 -34.68431, ...	False	0.687392	0.690365	0.830871
4	0009	6	-32.679004	-44.394554	128450.519	1.460280e+10	513802.077	POLYGON ((-45 -32.98768, -43.79085 -33.53064, ...	False	0.703593	0.695111	0.833721

Distribution of S2 Area Distortions¶

In [13]:

from vgrid.stats.s2stats import s2_norm_area
s2_norm_area(s2_inspect)

from vgrid.stats.s2stats import s2_norm_area s2_norm_area(s2_inspect)

Distribution of S2 IPQ Compactness¶

In [14]:

from vgrid.stats.s2stats import s2_compactness
s2_compactness(s2_inspect)

from vgrid.stats.s2stats import s2_compactness s2_compactness(s2_inspect)

S2 Statistics¶

In [15]:

from vgrid.stats.s2stats import s2stats
s2stats('km')

from vgrid.stats.s2stats import s2stats s2stats('km')

Out[15]:

	resolution	number_of_cells	avg_edge_len_km	avg_cell_area_km2
0	0	6	9220.137605	8.501094e+07
1	1	24	4610.068803	2.125273e+07
2	2	96	2305.034401	5.313184e+06
3	3	384	1152.517201	1.328296e+06
4	4	1536	576.258600	3.320740e+05
5	5	6144	288.129300	8.301849e+04
6	6	24576	144.064650	2.075462e+04
7	7	98304	72.032325	5.188656e+03
8	8	393216	36.016163	1.297164e+03
9	9	1572864	18.008081	3.242910e+02
10	10	6291456	9.004041	8.107275e+01
11	11	25165824	4.502020	2.026819e+01
12	12	100663296	2.251010	5.067047e+00
13	13	402653184	1.125505	1.266762e+00
14	14	1610612736	0.562753	3.166904e-01
15	15	6442450944	0.281376	7.917261e-02
16	16	25769803776	0.140688	1.979315e-02
17	17	103079215104	0.070344	4.948288e-03
18	18	412316860416	0.035172	1.237072e-03
19	19	1649267441664	0.017586	3.092680e-04
20	20	6597069766656	0.008793	7.731700e-05
21	21	26388279066624	0.004397	1.932925e-05
22	22	105553116266496	0.002198	4.832312e-06
23	23	422212465065984	0.001099	1.208078e-06
24	24	1688849860263936	0.000550	3.020195e-07
25	25	6755399441055744	0.000275	7.550488e-08
26	26	27021597764222976	0.000137	1.887622e-08
27	27	108086391056891904	0.000069	4.719055e-09
28	28	432345564227567616	0.000034	1.179764e-09
29	29	1729382256910270464	0.000017	2.949409e-10
30	30	6917529027641081856	0.000009	7.373523e-11