09 dggrid
Working with DGGAL 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.
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# %pip install vgrid --upgrade
# %pip install vgrid --upgrade
latlon2dggal¶
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from vgrid.conversion.latlon2dggs import latlon2dggal
dggs_type = 'ivea3h' # choose one from ['gnosis','isea3h','isea9r','ivea3h',
# 'ivea9r','rtea3h','rtea9r','rhealpix']
resolution = 10
dggal_id = latlon2dggal(dggs_type, 10.775276, 106.706797, resolution)
dggal_id
from vgrid.conversion.latlon2dggs import latlon2dggal
dggs_type = 'ivea3h' # choose one from ['gnosis','isea3h','isea9r','ivea3h',
# 'ivea9r','rtea3h','rtea9r','rhealpix']
resolution = 10
dggal_id = latlon2dggal(dggs_type, 10.775276, 106.706797, resolution)
dggal_id
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'F7-13A8-A'
DGGAL to Shapely Polygon¶
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from vgrid.conversion.dggs2geo.dggal2geo import dggal2geo
dggal_geo = dggal2geo(dggs_type, dggal_id)
dggal_geo
from vgrid.conversion.dggs2geo.dggal2geo import dggal2geo
dggal_geo = dggal2geo(dggs_type, dggal_id)
dggal_geo
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DGGAL to GeoJSON¶
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import json
from vgrid.conversion.dggs2geo.dggal2geo import dggal2geojson
dggal_geojson = dggal2geojson(dggs_type, dggal_id)
print(json.dumps(dggal_geojson))
import json
from vgrid.conversion.dggs2geo.dggal2geo import dggal2geojson
dggal_geojson = dggal2geojson(dggs_type, dggal_id)
print(json.dumps(dggal_geojson))
{"type": "FeatureCollection", "features": [{"type": "Feature", "id": "F7-13A8-A", "geometry": {"type": "Polygon", "coordinates": [[[106.70965553835, 10.9406422752183], [106.726966046312, 10.9144210995878], [106.74427264577, 10.8881980346408], [106.761575346388, 10.861973076306], [106.778874157826, 10.8357462205326], [106.79616908974, 10.8095174632904], [106.777489769627, 10.782795377979], [106.7588147929, 10.7560753689138], [106.740144160683, 10.7293574146626], [106.72147787402, 10.7026414938987], [106.702815933878, 10.6759275854015], [106.666854595627, 10.675453423246], [106.630890845036, 10.674982369552], [106.59492469772, 10.6745144245366], [106.558956169298, 10.6740495884166], [106.522985275398, 10.6735878614067], [106.505656394776, 10.699822352912], [106.488323610457, 10.726054993477], [106.470986912826, 10.7522857873808], [106.453646292262, 10.7785147389226], [106.43630173914, 10.804741852424], [106.4549561401, 10.83143070554], [106.473614949226, 10.8581214927003], [106.492278165953, 10.8848142349236], [106.510945789642, 10.9115089532973], [106.52961781958, 10.9382056690242], [106.565630263882, 10.9386865160504], [106.60164027398, 10.9391706004178], [106.6376478338, 10.9396579218923], [106.673652927276, 10.9401484802382], [106.70965553835, 10.9406422752183]]]}, "properties": {"zoneID": "F7-13A8-A"}}]}
Vector to DGGAL¶
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from vgrid.conversion.vector2dggs.vector2dggal import vector2dggal
file_path = (
"https://raw.githubusercontent.com/opengeoshub/vopendata/main/shape/polygon2.geojson"
)
vector_to_dggal = vector2dggal(file_path, dggs_type=dggs_type, resolution=18, compact=False,
predicate = "intersects",output_format="gpd")
# Visualize the output
# vector_to_dggal.head()
vector_to_dggal.plot(edgecolor="white")
from vgrid.conversion.vector2dggs.vector2dggal import vector2dggal
file_path = (
"https://raw.githubusercontent.com/opengeoshub/vopendata/main/shape/polygon2.geojson"
)
vector_to_dggal = vector2dggal(file_path, dggs_type=dggs_type, resolution=18, compact=False,
predicate = "intersects",output_format="gpd")
# Visualize the output
# vector_to_dggal.head()
vector_to_dggal.plot(edgecolor="white")
Out[5]:
<Axes: >
DGGAL Binning¶
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from vgrid.binning.dggalbin import dggalbin
dggs_type = 'isea9r'
file_path = (
"https://raw.githubusercontent.com/opengeoshub/vopendata/main/csv/dist1_pois.csv"
)
stats = "majority"
dggal_bin = dggalbin(file_path,dggs_type=dggs_type, resolution=10, stats=stats,
numeric_field="confidence",
# category="category",
# method="grid",
output_format="gpd")
# dggal_bin.head()
dggal_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.dggalbin import dggalbin
dggs_type = 'isea9r'
file_path = (
"https://raw.githubusercontent.com/opengeoshub/vopendata/main/csv/dist1_pois.csv"
)
stats = "majority"
dggal_bin = dggalbin(file_path,dggs_type=dggs_type, resolution=10, stats=stats,
numeric_field="confidence",
# category="category",
# method="grid",
output_format="gpd")
# dggal_bin.head()
dggal_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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<Axes: >
Raster to DGGAL¶
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# from vgrid.conversion.raster2dggs.raster2dggal import raster2dggal
# 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_dggal = raster2dggal(raster_file,dggs_type="isea3h",resolution=1, output_format="gpd")
# raster_to_dggal.head()
# Visualize the output
# import folium
# m = folium.Map(tiles="CartoDB positron", max_zoom=28)
# dggal_layer = folium.GeoJson(
# raster_to_dggal,
# 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=["zone_id", "band_1", "band_2", "band_3"],
# aliases=["Zone 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(dggal_layer.get_bounds())
# # Display the map
# m
# from vgrid.conversion.raster2dggs.raster2dggal import raster2dggal
# 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_dggal = raster2dggal(raster_file,dggs_type="isea3h",resolution=1, output_format="gpd")
# raster_to_dggal.head()
# Visualize the output
# import folium
# m = folium.Map(tiles="CartoDB positron", max_zoom=28)
# dggal_layer = folium.GeoJson(
# raster_to_dggal,
# 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=["zone_id", "band_1", "band_2", "band_3"],
# aliases=["Zone 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(dggal_layer.get_bounds())
# # Display the map
# m
DGGAL Generator¶
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from vgrid.generator.dggalgen import dggalgen
dggs_type = "isea9r" # "gnosis", "isea3h", "isea9r", "ivea3h", "ivea9r", "rtea3h", "rtea9r", "rhealpix"
resolution = 1
dggal_grid = dggalgen(dggs_type=dggs_type,resolution=resolution, output_format="gpd")
dggal_grid.plot(edgecolor="white")
from vgrid.generator.dggalgen import dggalgen
dggs_type = "isea9r" # "gnosis", "isea3h", "isea9r", "ivea3h", "ivea9r", "rtea3h", "rtea9r", "rhealpix"
resolution = 1
dggal_grid = dggalgen(dggs_type=dggs_type,resolution=resolution, output_format="gpd")
dggal_grid.plot(edgecolor="white")
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<Axes: >
DGGAL Inspect¶
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from vgrid.stats.dggalstats import dggalinspect
dggs_type = "ivea3h" # choose one from ['gnosis','isea3h','isea9r','ivea3h',
# 'ivea9r','rtea3h','rtea9r','rhealpix']
resolution = 3
dggal_inspect = dggalinspect(dggs_type, resolution)
dggal_inspect
from vgrid.stats.dggalstats import dggalinspect
dggs_type = "ivea3h" # choose one from ['gnosis','isea3h','isea9r','ivea3h',
# 'ivea9r','rtea3h','rtea9r','rhealpix']
resolution = 3
dggal_inspect = dggalinspect(dggs_type, resolution)
dggal_inspect
Out[9]:
| geometry | zoneID | cell_area | cell_perimeter | crossed | norm_area | ipq | zsc | |
|---|---|---|---|---|---|---|---|---|
| 0 | POLYGON ((-168.8 65.34289, -167.64739 65.10319... | B0-0-D | 1.573648e+12 | 4.578743e+06 | True | 0.839171 | 0.943246 | 0.969712 |
| 1 | POLYGON ((-168.8 65.34289, -168.8 65.80636, -1... | B0-0-E | 1.889170e+12 | 5.141869e+06 | False | 1.007428 | 0.897922 | 0.945835 |
| 2 | POLYGON ((-162.08585 52.57334, -161.58833 53.0... | B0-0-F | 1.889170e+12 | 5.141822e+06 | False | 1.007428 | 0.897939 | 0.945844 |
| 3 | POLYGON ((-168.8 86.41452, -161.76574 86.14741... | B0-1-D | 1.889150e+12 | 5.133181e+06 | True | 1.007417 | 0.900955 | 0.947431 |
| 4 | POLYGON ((-168.8 86.41452, -168.8 86.89089, -1... | B0-1-E | 1.889161e+12 | 5.126934e+06 | False | 1.007423 | 0.903157 | 0.948588 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 267 | POLYGON ((-165.02536 -14.17022, -164.7863 -13.... | B9-7-E | 1.889155e+12 | 5.124955e+06 | False | 1.007420 | 0.903852 | 0.948953 |
| 268 | POLYGON ((-164.28408 -27.50412, -164.00617 -27... | B9-7-F | 1.889161e+12 | 5.127138e+06 | False | 1.007423 | 0.903086 | 0.948551 |
| 269 | POLYGON ((-154.32104 7.22817, -154.1105 6.752,... | B9-8-D | 1.889150e+12 | 5.132411e+06 | False | 1.007417 | 0.901225 | 0.947573 |
| 270 | POLYGON ((-154.32104 7.22817, -154.04875 7.705... | B9-8-E | 1.889170e+12 | 5.141657e+06 | False | 1.007428 | 0.897996 | 0.945874 |
| 271 | POLYGON ((-154.32104 -7.22817, -154.1105 -6.75... | B9-8-F | 1.889170e+12 | 5.141657e+06 | False | 1.007428 | 0.897996 | 0.945874 |
272 rows × 8 columns
Distribution of DGGAL Area Distortions¶
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from vgrid.stats.dggalstats import dggal_norm_area
dggal_norm_area(dggal_inspect,dggs_type=dggs_type, crs='proj=moll')
from vgrid.stats.dggalstats import dggal_norm_area
dggal_norm_area(dggal_inspect,dggs_type=dggs_type, crs='proj=moll')
Distribution of DGGAL IPQ Compactness¶
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from vgrid.stats.dggalstats import dggal_compactness
dggal_compactness(dggal_inspect,dggs_type, crs = "proj=moll")
from vgrid.stats.dggalstats import dggal_compactness
dggal_compactness(dggal_inspect,dggs_type, crs = "proj=moll")
DGGAL Statistics¶
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from vgrid.stats.dggalstats import dggalstats
dggs_type = 'rhealpix' # choose one from ['gnosis','isea3h','isea9r','ivea3h',
# 'ivea9r','rtea3h','rtea9r','rhealpix']
dggal_stats = dggalstats(dggs_type)
dggal_stats
from vgrid.stats.dggalstats import dggalstats
dggs_type = 'rhealpix' # choose one from ['gnosis','isea3h','isea9r','ivea3h',
# 'ivea9r','rtea3h','rtea9r','rhealpix']
dggal_stats = dggalstats(dggs_type)
dggal_stats
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| resolution | number_of_cells | avg_edge_len_m | avg_cell_area_m2 | |
|---|---|---|---|---|
| 0 | 0 | 6 | 9.220138e+06 | 8.501094e+13 |
| 1 | 1 | 54 | 3.073379e+06 | 9.445660e+12 |
| 2 | 2 | 486 | 1.024460e+06 | 1.049518e+12 |
| 3 | 3 | 4374 | 3.414866e+05 | 1.166131e+11 |
| 4 | 4 | 39366 | 1.138289e+05 | 1.295701e+10 |
| 5 | 5 | 354294 | 3.794295e+04 | 1.439668e+09 |
| 6 | 6 | 3188646 | 1.264765e+04 | 1.599631e+08 |
| 7 | 7 | 28697814 | 4.215884e+03 | 1.777368e+07 |
| 8 | 8 | 258280326 | 1.405295e+03 | 1.974853e+06 |
| 9 | 9 | 2324522934 | 4.684315e+02 | 2.194281e+05 |
| 10 | 10 | 20920706406 | 1.561438e+02 | 2.438090e+04 |
| 11 | 11 | 188286357654 | 5.204795e+01 | 2.708989e+03 |
| 12 | 12 | 1694577218886 | 1.734932e+01 | 3.009988e+02 |
| 13 | 13 | 15251194969974 | 5.783105e+00 | 3.344431e+01 |
| 14 | 14 | 137260754729766 | 1.927702e+00 | 3.716034e+00 |
| 15 | 15 | 1235346792567894 | 6.425672e-01 | 4.128927e-01 |
| 16 | 16 | 11118121133111046 | 2.141891e-01 | 4.587696e-02 |