07 ease

Working with GEMS-UMN EASE-DGGS 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

latlon2ease¶

In [1]:

from vgrid.conversion.latlon2dggs import latlon2ease

lat = 10.775276
lon = 106.706797
res = 4
ease_id = latlon2ease(lat, lon, res)
ease_id

from vgrid.conversion.latlon2dggs import latlon2ease lat = 10.775276 lon = 106.706797 res = 4 ease_id = latlon2ease(lat, lon, res) ease_id

Out[1]:

'L4.165767.02.02.22.45'

EASE to Polygon¶

In [3]:

from vgrid.conversion.dggs2geo.ease2geo import ease2geo

ease_geo = ease2geo(ease_id)
ease_geo

from vgrid.conversion.dggs2geo.ease2geo import ease2geo ease_geo = ease2geo(ease_id) ease_geo

Out[3]:

EASE to GeoJSON¶

In [2]:

from vgrid.conversion.dggs2geo.ease2geo import ease2geojson

ease_geojson = ease2geojson(ease_id)
# ease_geojson

from vgrid.conversion.dggs2geo.ease2geo import ease2geojson ease_geojson = ease2geojson(ease_id) # ease_geojson

Vector to EASE¶

In [10]:

from vgrid.conversion.vector2dggs.vector2ease import vector2ease

file_path = "https://raw.githubusercontent.com/opengeoshub/vopendata/main/shape/polygon2.geojson"
vector_to_ease = vector2ease(
    file_path,
    resolution=4,
    compact=True,
    topology=False,
    predicate="intersects",
    output_format="gpd",
)
# Visualize the output
vector_to_ease.plot(edgecolor="white")

from vgrid.conversion.vector2dggs.vector2ease import vector2ease file_path = "https://raw.githubusercontent.com/opengeoshub/vopendata/main/shape/polygon2.geojson" vector_to_ease = vector2ease( file_path, resolution=4, compact=True, topology=False, predicate="intersects", output_format="gpd", ) # Visualize the output vector_to_ease.plot(edgecolor="white")

Processing features: 100%|██████████| 1/1 [00:02<00:00,  2.58s/it]

Out[10]:

<Axes: >

EASE Compact¶

In [11]:

from vgrid.conversion.dggscompact.easecompact import easecompact

ease_compacted = easecompact(vector_to_ease, output_format="gpd")
ease_compacted.plot(edgecolor="white")

from vgrid.conversion.dggscompact.easecompact import easecompact ease_compacted = easecompact(vector_to_ease, output_format="gpd") ease_compacted.plot(edgecolor="white")

Out[11]:

<Axes: >

EASE Expand¶

In [12]:

from vgrid.conversion.dggscompact import easeexpand

ease_expanded = easeexpand(vector_to_ease, resolution=4, output_format="gpd")
ease_expanded.plot(edgecolor="white")

from vgrid.conversion.dggscompact import easeexpand ease_expanded = easeexpand(vector_to_ease, resolution=4, output_format="gpd") ease_expanded.plot(edgecolor="white")

Out[12]:

<Axes: >

EASE Generator¶

In [8]:

from vgrid.generator.easegrid import easegrid

ease_grid = easegrid(
    resolution=3,
    bbox=[106.699007, 10.762811, 106.717674, 10.778649],
    output_format="gpd",
)
ease_grid.plot(edgecolor="white")

from vgrid.generator.easegrid import easegrid ease_grid = easegrid( resolution=3, bbox=[106.699007, 10.762811, 106.717674, 10.778649], output_format="gpd", ) ease_grid.plot(edgecolor="white")

Generating EASE DGGS: 100%|██████████| 4/4 [00:00<00:00, 335.58 cells/s]

Out[8]:

<Axes: >

EASE Inspect¶

In [ ]:

from vgrid.stats.easestats import easeinspect

resolution = 0
ease_inspect = easeinspect(resolution)
ease_inspect.head()

from vgrid.stats.easestats import easeinspect resolution = 0 ease_inspect = easeinspect(resolution) ease_inspect.head()

EASE Normalized Area Histogram¶

In [ ]:

from vgrid.stats.easestats import ease_norm_area_hist

ease_norm_area_hist(ease_inspect)

from vgrid.stats.easestats import ease_norm_area_hist ease_norm_area_hist(ease_inspect)

Distribution of EASE Area Distortions¶

In [ ]:

from vgrid.stats.easestats import ease_norm_area

ease_norm_area(ease_inspect)

from vgrid.stats.easestats import ease_norm_area ease_norm_area(ease_inspect)

EASE 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 [ ]:

from vgrid.stats.easestats import ease_compactness_ipq_hist

ease_compactness_ipq_hist(ease_inspect)

from vgrid.stats.easestats import ease_compactness_ipq_hist ease_compactness_ipq_hist(ease_inspect)

Distribution of EASE IPQ Compactness¶

In [ ]:

from vgrid.stats.easestats import ease_compactness_ipq

ease_compactness_ipq(ease_inspect)

from vgrid.stats.easestats import ease_compactness_ipq ease_compactness_ipq(ease_inspect)

EASE 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 [ ]:

from vgrid.stats.easestats import ease_compactness_cvh_hist

ease_compactness_cvh_hist(ease_inspect)

from vgrid.stats.easestats import ease_compactness_cvh_hist ease_compactness_cvh_hist(ease_inspect)

Distribution of EASE Convex hull Compactness¶

In [ ]:

from vgrid.stats.easestats import ease_compactness_cvh

ease_compactness_cvh(ease_inspect)

from vgrid.stats.easestats import ease_compactness_cvh ease_compactness_cvh(ease_inspect)

EASE Statistics¶

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

In [ ]:

from vgrid.stats.easestats import easestats

easestats()

from vgrid.stats.easestats import easestats easestats()

Out[ ]:

	resolution	number_of_cells	avg_edge_len_m	avg_cell_area_m2	cls_m
0	0	391384	36032.220841	1.298321e+09	40658.024589
1	1	6262144	9008.055210	8.114506e+07	10164.502105
2	2	56359296	3002.685070	9.016118e+06	3388.167288
3	3	507233664	1000.895023	1.001791e+06	1129.389093
4	4	50723366400	100.089502	1.001791e+04	112.938909
5	5	5072336640000	10.008950	1.001791e+02	11.293891
6	6	507233664000000	1.000895	1.001791e+00	1.129389