05 isea4t

Working with Open-EAGGR ISEA4T 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 [5]:

# %pip install vgrid --upgrade

# %pip install vgrid --upgrade

latlon2isea4t¶

In [6]:

from vgrid.conversion.latlon2dggs import latlon2isea4t

lat = 10.775276
lon = 106.706797
res = 12
isea4t_id = latlon2isea4t(lat, lon, res)
isea4t_id

from vgrid.conversion.latlon2dggs import latlon2isea4t lat = 10.775276 lon = 106.706797 res = 12 isea4t_id = latlon2isea4t(lat, lon, res) isea4t_id

Out[6]:

'13102313331320'

ISEA4T to Shapely Polygon¶

In [7]:

from vgrid.conversion.dggs2geo.isea4t2geo  import isea4t2geo

isea4t_geo = isea4t2geo(isea4t_id)
isea4t_geo

from vgrid.conversion.dggs2geo.isea4t2geo import isea4t2geo isea4t_geo = isea4t2geo(isea4t_id) isea4t_geo

Out[7]:

ISEA4T to GeoJSON¶

In [8]:

from vgrid.conversion.dggs2geo.isea4t2geo import isea4t2geojson

isea4t_geojson = isea4t2geojson(isea4t_id)
isea4t_geojson

from vgrid.conversion.dggs2geo.isea4t2geo import isea4t2geojson isea4t_geojson = isea4t2geojson(isea4t_id) isea4t_geojson

Out[8]:

{'type': 'FeatureCollection',
 'features': [{'type': 'Feature',
   'geometry': {'type': 'Polygon',
    'coordinates': (((106.70243255134056, 10.782716201741922),
      (106.69332798020412, 10.770749913453548),
      (106.71159691050727, 10.767101348690764),
      (106.70243255134056, 10.782716201741922)),)},
   'properties': {'isea4t': '13102313331320',
    'resolution': 12,
    'center_lat': 10.7735225,
    'center_lon': 106.7024525,
    'avg_edge_len': 1897.259,
    'cell_area': 1523297.174,
    'cell_perimeter': 5691.776}}]}

Vector to ISEA4T¶

In [9]:

from vgrid.conversion.vector2dggs.vector2isea4t import vector2isea4t

file_path = (
    "https://raw.githubusercontent.com/opengeoshub/vopendata/main/shape/polygon.geojson"
)
vector_to_isea4t = vector2isea4t(file_path, resolution=16, compact=False, 
                                predicate = "intersects",output_format="gpd")
# Visualize the output
vector_to_isea4t.plot(edgecolor="white")

from vgrid.conversion.vector2dggs.vector2isea4t import vector2isea4t file_path = ( "https://raw.githubusercontent.com/opengeoshub/vopendata/main/shape/polygon.geojson" ) vector_to_isea4t = vector2isea4t(file_path, resolution=16, compact=False, predicate = "intersects",output_format="gpd") # Visualize the output vector_to_isea4t.plot(edgecolor="white")

Processing features: 100%|██████████| 4/4 [00:00<00:00,  8.38it/s]

Out[9]:

<Axes: >

ISEA4T Compact¶

In [10]:

from vgrid.conversion.dggscompact.isea4tcompact import isea4tcompact

isea4t_compacted = isea4tcompact( vector_to_isea4t, output_format="gpd")
isea4t_compacted.plot(edgecolor="white")

from vgrid.conversion.dggscompact.isea4tcompact import isea4tcompact isea4t_compacted = isea4tcompact( vector_to_isea4t, output_format="gpd") isea4t_compacted.plot(edgecolor="white")

Out[10]:

<Axes: >

ISEA4T Expand¶

In [11]:

from vgrid.conversion.dggscompact.isea4tcompact import isea4texpand

isea4t_expanded = isea4texpand(
    vector_to_isea4t, resolution=17, output_format="gpd"
)
isea4t_expanded.plot(edgecolor="white")

from vgrid.conversion.dggscompact.isea4tcompact import isea4texpand isea4t_expanded = isea4texpand( vector_to_isea4t, resolution=17, output_format="gpd" ) isea4t_expanded.plot(edgecolor="white")

Out[11]:

<Axes: >

ISEA4T Binning¶

In [12]:

from vgrid.binning.isea4tbin import isea4tbin
import geopandas as gpd
file_path = (
    "https://raw.githubusercontent.com/opengeoshub/vopendata/main/csv/dist1_pois.csv"
)
stats = "count"
isea4t_bin = isea4tbin(file_path, resolution=15, stats=stats, 
                # numeric_field="confidence",
                # category="category",
                output_format="gpd")
isea4t_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.isea4tbin import isea4tbin import geopandas as gpd file_path = ( "https://raw.githubusercontent.com/opengeoshub/vopendata/main/csv/dist1_pois.csv" ) stats = "count" isea4t_bin = isea4tbin(file_path, resolution=15, stats=stats, # numeric_field="confidence", # category="category", output_format="gpd") isea4t_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 ISEA4T DGGS: 100%|██████████| 767/767 [00:00<00:00, 1686.13 cells/s]

Out[12]:

<Axes: >

Raster to ISEA4T¶

In [13]:

# %pip install folium

# %pip install folium

In [14]:

from vgrid.conversion.raster2dggs.raster2isea4t import raster2isea4t
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_isea4t =  raster2isea4t(raster_file,output_format="gpd")

# Visualize the output 
import folium

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

isea4t_layer = folium.GeoJson(
    raster_to_isea4t,
    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=["isea4t", "band_1", "band_2", "band_3"],
        aliases=["ISEA4T 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(isea4t_layer.get_bounds())

# Display the map
m

from vgrid.conversion.raster2dggs.raster2isea4t import raster2isea4t 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_isea4t = raster2isea4t(raster_file,output_format="gpd") # Visualize the output import folium m = folium.Map(tiles="CartoDB positron", max_zoom=28) isea4t_layer = folium.GeoJson( raster_to_isea4t, 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=["isea4t", "band_1", "band_2", "band_3"], aliases=["ISEA4T 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(isea4t_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 isea4t resolution determined: 23

Resampling: 100%|██████████| 8486/8486 [00:02<00:00, 4010.21 cells/s]
Converting raster to ISEA4T: 100%|██████████| 8375/8375 [00:00<00:00, 13742.36 cells/s]

Out[14]:

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

ISEA4T Generator¶

In [15]:

from vgrid.generator.isea4tgrid import isea4tgrid

isea4t_grid = isea4tgrid(resolution=0,output_format="gpd")
# isea4t_grid = isea4tgrid(resolution=16,bbox=[106.699007, 10.762811, 106.717674, 10.778649],output_format="gpd")

isea4t_grid.plot(edgecolor='white')

from vgrid.generator.isea4tgrid import isea4tgrid isea4t_grid = isea4tgrid(resolution=0,output_format="gpd") # isea4t_grid = isea4tgrid(resolution=16,bbox=[106.699007, 10.762811, 106.717674, 10.778649],output_format="gpd") isea4t_grid.plot(edgecolor='white')

Generating ISEA4T DGGS: 100%|██████████| 20/20 [00:00<00:00, 1288.85 cells/s]

Out[15]:

<Axes: >

ISEA4T Inspect¶

In [16]:

from vgrid.stats.isea4tstats import isea4tinspect
resolution = 5
isea4t_inspect = isea4tinspect(resolution)
isea4t_inspect.head()

from vgrid.stats.isea4tstats import isea4tinspect resolution = 5 isea4t_inspect = isea4tinspect(resolution) isea4t_inspect.head()

Generating ISEA4T DGGS: 100%|██████████| 20480/20480 [00:04<00:00, 4096.87 cells/s]

Out[16]:

	isea4t	resolution	center_lat	center_lon	avg_edge_len	cell_area	cell_perimeter	geometry	crossed	norm_area	ipq	zsc
0	0000000	5	52.798043	-144.000000	249995.286	2.706746e+10	749985.858	POLYGON ((-144 51.51071, -145.88123 53.44171, ...	False	1.086805	0.604716	0.777614
1	0000001	5	53.993894	-144.000000	230922.987	2.284392e+10	692768.962	POLYGON ((-144 55.09826, -145.88123 53.44171, ...	False	0.917222	0.598141	0.773379
2	0000002	5	52.190361	-145.690007	230945.739	2.284876e+10	692837.218	POLYGON ((-145.88123 53.44171, -147.18879 51.6...	False	0.917417	0.598150	0.773384
3	0000003	5	52.190361	-142.309993	230945.739	2.284876e+10	692837.218	POLYGON ((-142.11877 53.44171, -144 51.51071, ...	False	0.917417	0.598150	0.773384
4	0000010	5	50.215849	-144.000000	238726.467	2.462431e+10	716179.400	POLYGON ((-144 51.51071, -145.58435 49.56842, ...	False	0.988708	0.603296	0.776702

Distribution of ISEA4T Area Distortions¶

In [17]:

from vgrid.stats.isea4tstats import isea4t_norm_area
isea4t_norm_area(isea4t_inspect)

from vgrid.stats.isea4tstats import isea4t_norm_area isea4t_norm_area(isea4t_inspect)

Distribution of ISEA4T IPQ Compactness¶

In [18]:

from vgrid.stats.isea4tstats import isea4t_compactness
isea4t_compactness(isea4t_inspect)

from vgrid.stats.isea4tstats import isea4t_compactness isea4t_compactness(isea4t_inspect)

ISEA4T Statistics¶

In [19]:

from vgrid.stats.isea4tstats import isea4tstats
isea4tstats('km')

from vgrid.stats.isea4tstats import isea4tstats isea4tstats('km')

Out[19]:

	resolution	number_of_cells	avg_edge_len_km	avg_cell_area_km2
0	0	20	7.674458e+06	2.550328e+13
1	1	80	3.837229e+06	6.375820e+12
2	2	320	1.918614e+06	1.593955e+12
3	3	1280	9.593072e+05	3.984888e+11
4	4	5120	4.796536e+05	9.962219e+10
5	5	20480	2.398268e+05	2.490555e+10
6	6	81920	1.199134e+05	6.226387e+09
7	7	327680	5.995670e+04	1.556597e+09
8	8	1310720	2.997835e+04	3.891492e+08
9	9	5242880	1.498918e+04	9.728730e+07
10	10	20971520	7.494588e+03	2.432182e+07
11	11	83886080	3.747294e+03	6.080456e+06
12	12	335544320	1.873647e+03	1.520114e+06
13	13	1342177280	9.368235e+02	3.800285e+05
14	14	5368709120	4.684117e+02	9.500713e+04
15	15	21474836480	2.342059e+02	2.375178e+04
16	16	85899345920	1.171029e+02	5.937945e+03
17	17	343597383680	5.855147e+01	1.484486e+03
18	18	1374389534720	2.927573e+01	3.711216e+02
19	19	5497558138880	1.463787e+01	9.278040e+01
20	20	21990232555520	7.318933e+00	2.319510e+01
21	21	87960930222080	3.659467e+00	5.798775e+00
22	22	351843720888320	1.829733e+00	1.449694e+00
23	23	1407374883553280	9.148667e-01	3.624234e-01
24	24	5629499534213120	4.574333e-01	9.060586e-02
25	25	22517998136852480	2.287167e-01	2.265146e-02
26	26	90071992547409920	1.143583e-01	5.662866e-03
27	27	360287970189639680	5.717917e-02	1.415717e-03
28	28	1441151880758558720	2.858958e-02	3.539291e-04
29	29	5764607523034234880	1.429479e-02	8.848228e-05
30	30	23058430092136939520	7.147396e-03	2.212057e-05
31	31	92233720368547758080	3.573698e-03	5.530143e-06
32	32	368934881474191032320	1.786849e-03	1.382536e-06
33	33	1475739525896764129280	8.934245e-04	3.456339e-07
34	34	5902958103587056517120	4.467122e-04	8.640848e-08
35	35	23611832414348226068480	2.233561e-04	2.160212e-08
36	36	94447329657392904273920	1.116781e-04	5.400530e-09
37	37	377789318629571617095680	5.583903e-05	1.350132e-09
38	38	1511157274518286468382720	2.791952e-05	3.375331e-10
39	39	6044629098073145873530880	1.395976e-05	8.438328e-11