08 isea3h

Working with Open-EAGGR ISEA3H 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

latlon2isea3h¶

In [1]:

from vgrid.conversion.latlon2dggs import latlon2isea3h

lat = 10.775276
lon = 106.706797
res = 12
isea3h_id = latlon2isea3h(lat, lon, res)
isea3h_id

from vgrid.conversion.latlon2dggs import latlon2isea3h lat = 10.775276 lon = 106.706797 res = 12 isea3h_id = latlon2isea3h(lat, lon, res) isea3h_id

Out[1]:

'1312279,-13'

ISEA3H to Polygon¶

In [3]:

from vgrid.conversion.dggs2geo.isea3h2geo import isea3h2geo

isea3h_geo = isea3h2geo(isea3h_id)
isea3h_geo

from vgrid.conversion.dggs2geo.isea3h2geo import isea3h2geo isea3h_geo = isea3h2geo(isea3h_id) isea3h_geo

Out[3]:

ISEA3H to GeoJSON¶

In [2]:

from vgrid.conversion.dggs2geo.isea3h2geo import isea3h2geojson

isea3h_geojson = isea3h2geojson(isea3h_id)
# isea3h_geojson

from vgrid.conversion.dggs2geo.isea3h2geo import isea3h2geojson isea3h_geojson = isea3h2geojson(isea3h_id) # isea3h_geojson

Vector to ISEA3H¶

In [5]:

from vgrid.conversion.vector2dggs.vector2isea3h import vector2isea3h

file_path = "https://raw.githubusercontent.com/opengeoshub/vopendata/main/shape/polygon2.geojson"
vector_to_isea3h = vector2isea3h(
    file_path,
    resolution=20,
    compact=True,
    topology=True,
    predicate="intersects",
    output_format="gpd",
)
# vector_to_isea3h
vector_to_isea3h.plot(edgecolor="white")

from vgrid.conversion.vector2dggs.vector2isea3h import vector2isea3h file_path = "https://raw.githubusercontent.com/opengeoshub/vopendata/main/shape/polygon2.geojson" vector_to_isea3h = vector2isea3h( file_path, resolution=20, compact=True, topology=True, predicate="intersects", output_format="gpd", ) # vector_to_isea3h vector_to_isea3h.plot(edgecolor="white")

Processing features: 100%|██████████| 1/1 [00:00<00:00,  3.19it/s]

Out[5]:

<Axes: >

ISEA3H Compact¶

In [6]:

from vgrid.conversion.dggscompact.isea3hcompact import isea3hcompact

isea3h_compacted = isea3hcompact(vector_to_isea3h, output_format="gpd")
isea3h_compacted.plot(edgecolor="white")

from vgrid.conversion.dggscompact.isea3hcompact import isea3hcompact isea3h_compacted = isea3hcompact(vector_to_isea3h, output_format="gpd") isea3h_compacted.plot(edgecolor="white")

Out[6]:

<Axes: >

ISEA3H Expand¶

In [7]:

from vgrid.conversion.dggscompact.isea3hcompact import isea3hexpand

isea3h_expanded = isea3hexpand(vector_to_isea3h, resolution=21, output_format="gpd")
isea3h_expanded.plot(edgecolor="white")

from vgrid.conversion.dggscompact.isea3hcompact import isea3hexpand isea3h_expanded = isea3hexpand(vector_to_isea3h, resolution=21, output_format="gpd") isea3h_expanded.plot(edgecolor="white")

Out[7]:

<Axes: >

ISEA3H Generator¶

In [8]:

from vgrid.generator.isea3hgrid import isea3hgrid

isea3h_grid = isea3hgrid(resolution=1, output_format="gpd", fix_antimeridian="split")
isea3h_grid.plot(edgecolor="white")

from vgrid.generator.isea3hgrid import isea3hgrid isea3h_grid = isea3hgrid(resolution=1, output_format="gpd", fix_antimeridian="split") isea3h_grid.plot(edgecolor="white")

Generating ISEA3H DGGS: 100%|██████████| 140/140 [00:00<00:00, 1940.79 cells/s]

Out[8]:

<Axes: >

ISEA3H Inspect¶

In [ ]:

from vgrid.stats.isea3hstats import isea3hinspect

resolution = 3
isea3h_inspect = isea3hinspect(resolution)
isea3h_inspect.head()

from vgrid.stats.isea3hstats import isea3hinspect resolution = 3 isea3h_inspect = isea3hinspect(resolution) isea3h_inspect.head()

Generating ISEA3H DGGS: 100%|██████████| 2220/2220 [00:00<00:00, 6005.68 cells/s]

Out[ ]:

	isea3h	resolution	center_lat	center_lon	avg_edge_len	cell_area	cell_perimeter	geometry	crossed	norm_area	ipq	zsc	cvh
0	00030,0	3	52.796491	-144.000000	859534.472320	1.927693e+12	5.157207e+06	POLYGON ((-131.28474 52.37359, -136.05347 59.1...	False	1.017643	0.910791	0.952553	1.0
1	0003-1,0	3	38.976164	-144.000000	853461.320639	1.893931e+12	5.120768e+06	POLYGON ((-134.70124 38.65144, -138.77139 45.8...	False	0.999820	0.907620	0.950924	1.0
2	00030,1	3	57.699020	-121.162465	852569.447432	1.889909e+12	5.115417e+06	POLYGON ((-108 54.85114, -108 62.05445, -124.6...	False	0.997697	0.907588	0.950912	1.0
3	00031,0	3	65.610319	-144.000000	854256.212533	1.895550e+12	5.125537e+06	POLYGON ((-124.68962 64.8145, -131.10399 71.49...	False	1.000674	0.906706	0.950444	1.0
5	0003-1,1	3	45.137589	-128.011036	855172.453011	1.899693e+12	5.131035e+06	POLYGON ((-117.92421 43.54752, -119.12139 50.6...	False	1.002862	0.906741	0.950459	1.0

ISEA3H Normalized Area Histogram¶

In [10]:

from vgrid.stats.isea3hstats import isea3h_norm_area_hist

isea3h_norm_area_hist(isea3h_inspect)

from vgrid.stats.isea3hstats import isea3h_norm_area_hist isea3h_norm_area_hist(isea3h_inspect)

Distribution of ISEA3H Area Distortions¶

In [11]:

from vgrid.stats.isea3hstats import isea3h_norm_area

isea3h_norm_area(isea3h_inspect)

from vgrid.stats.isea3hstats import isea3h_norm_area isea3h_norm_area(isea3h_inspect)

ISEA3H IPQ Compactness Histogram¶

Isoperimetric Inequality (IPQ) Compactness (suggested by Osserman, 1978):

$$C_{IPQ} = \frac{4 \pi A}{p^2}$$ The range of the IPQ compactness metric is [0,1].

A circle represents the maximum compactness with a value of 1.

As shapes become more irregular or elongated, their compactness decreases toward 0.

In [12]:

from vgrid.stats.isea3hstats import isea3h_compactness_ipq_hist

isea3h_compactness_ipq_hist(isea3h_inspect)

from vgrid.stats.isea3hstats import isea3h_compactness_ipq_hist isea3h_compactness_ipq_hist(isea3h_inspect)

Distribution of ISEA3H IPQ Compactness¶

In [13]:

from vgrid.stats.isea3hstats import isea3h_compactness_ipq

isea3h_compactness_ipq(isea3h_inspect)

from vgrid.stats.isea3hstats import isea3h_compactness_ipq isea3h_compactness_ipq(isea3h_inspect)

ISEA3H Convex hull Compactness Histogram:¶

$$C_{CVH} = \frac{A}{A_{CVH}}$$

The range of the convex hull compactness metric is [0,1].

As shapes become more concave, their convex hull compactness decreases toward 0.

In [14]:

from vgrid.stats.isea3hstats import isea3h_compactness_cvh_hist

isea3h_compactness_cvh_hist(isea3h_inspect)

from vgrid.stats.isea3hstats import isea3h_compactness_cvh_hist isea3h_compactness_cvh_hist(isea3h_inspect)

Distribution of ISEA3H Convex hull Compactness¶

In [15]:

from vgrid.stats.isea3hstats import isea3h_compactness_cvh

isea3h_compactness_cvh(isea3h_inspect)

from vgrid.stats.isea3hstats import isea3h_compactness_cvh isea3h_compactness_cvh(isea3h_inspect)

ISEA3H Statistics¶

Characteristic Length Scale (CLS - suggested by Ralph Kahn): the diameter of a spherical cap of the same cell's area

In [16]:

from vgrid.stats.isea3hstats import isea3hstats

isea3hstats("km")

from vgrid.stats.isea3hstats import isea3hstats isea3hstats("km")

Out[16]:

	resolution	number_of_cells	avg_edge_len_km	avg_cell_area_km2	cls_km
0	0	12	9907.682608	4.250547e+07	7462.813606
1	1	32	2476.920652	1.593955e+07	4528.782056
2	2	92	1460.808105	5.544192e+06	2661.730568
3	3	272	849.576775	1.875241e+06	1546.144590
4	4	812	491.710032	6.281596e+05	894.497750
5	5	2432	284.122285	2.097309e+05	516.792340
6	6	7292	164.083063	6.994866e+04	298.438386
7	7	21872	94.742062	2.332048e+04	172.316612
8	8	65612	54.701023	7.773969e+03	99.489569
9	9	196832	31.581971	2.591375e+03	57.440816
10	10	590492	18.233921	8.637977e+02	33.163564
11	11	1771472	10.527371	2.879332e+02	19.147011
12	12	5314412	6.077983	9.597781e+01	11.054535
13	13	15943232	3.509125	3.199261e+01	6.382340
14	14	47829692	2.025995	1.066420e+01	3.684846
15	15	143489072	1.169709	3.554735e+00	2.127447
16	16	430467212	0.675332	1.184912e+00	1.228282
17	17	1291401632	0.389903	3.949706e-01	0.709149
18	18	3874204892	0.225111	1.316569e-01	0.409427
19	19	11622614672	0.129968	4.388562e-02	0.236383
20	20	34867844012	0.075037	1.462854e-02	0.136476
21	21	104603532032	0.043323	4.876180e-03	0.078794
22	22	313810596092	0.025012	1.625393e-03	0.045492
23	23	941431788272	0.014441	5.417977e-04	0.026265
24	24	2824295364812	0.008337	1.805992e-04	0.015164
25	25	8472886094432	0.004814	6.019975e-05	0.008755
26	26	25418658283292	0.002779	2.006658e-05	0.005055
27	27	76255974849872	0.001605	6.688861e-06	0.002918
28	28	228767924549612	0.000926	2.229620e-06	0.001685
29	29	686303773648832	0.000535	7.432068e-07	0.000973
30	30	2058911320946492	0.000309	2.477356e-07	0.000562
31	31	6176733962839472	0.000178	8.257853e-08	0.000324
32	32	18530201888518412	0.000103	2.752618e-08	0.000187
33	33	55590605665555232	0.000059	9.175392e-09	0.000108
34	34	166771816996665692	0.000034	3.058464e-09	0.000062
35	35	500315450989997072	0.000020	1.019488e-09	0.000036
36	36	1500946352969991212	0.000011	3.398293e-10	0.000021
37	37	4502839058909973632	0.000007	1.132764e-10	0.000012
38	38	13508517176729920892	0.000004	3.775882e-11	0.000007
39	39	40525551530189762672	0.000002	1.258627e-11	0.000004
40	40	121576654590569288012	0.000001	4.195424e-12	0.000002