25 a5 segments

A5 Segments 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 [ ]:

# %pip install vgrid --upgrade

# %pip install vgrid --upgrade

A5 Segments¶

Evaluate how a52geo cell area changes with the segments option (1, 10, 100, ..., 1000000).

Higher segment counts can be slow (especially 1000000).

In [2]:

from vgrid.stats.a5_segments import a5_segments

a5_segments_gdf = a5_segments(
    a5_id="6361180000000000",
    # segments=[1, 10, 100, 1000],  # optional smaller set for a quick run
    output="a5_segments.parquet",
)
a5_segments_gdf[
    ["a5_id", "resolution", "segments", "cell_area", "norm_area", "area_error"]
]

from vgrid.stats.a5_segments import a5_segments a5_segments_gdf = a5_segments( a5_id="6361180000000000", # segments=[1, 10, 100, 1000], # optional smaller set for a quick run output="a5_segments.parquet", ) a5_segments_gdf[ ["a5_id", "resolution", "segments", "cell_area", "norm_area", "area_error"] ]

A5 segments: 100%|██████████| 7/7 [01:10<00:00, 10.02s/ value]

Saved 7 rows to D:\Github\vgrid\docs\notebooks\a5_segments.parquet

Out[2]:

	a5_id	resolution	segments	cell_area	norm_area	area_error
0	6361180000000000	8	1	5.188960e+08	1.000059	5.855149e-03
1	6361180000000000	8	10	5.188659e+08	1.000001	5.855145e-05
2	6361180000000000	8	100	5.188656e+08	1.000000	5.855412e-07
3	6361180000000000	8	1000	5.188656e+08	1.000000	5.860601e-09
4	6361180000000000	8	10000	5.188656e+08	1.000000	9.974244e-11
5	6361180000000000	8	100000	5.188656e+08	1.000000	7.109979e-10
6	6361180000000000	8	1000000	5.188656e+08	1.000000	1.697442e-09

In [2]:

a5_segments_gdf.drop(columns="geometry").to_csv("a5_segments.csv", index=False)

a5_segments_gdf.drop(columns="geometry").to_csv("a5_segments.csv", index=False)

Plot cell area by segments¶

In [3]:

import matplotlib.pyplot as plt

fig, ax = plt.subplots(figsize=(8, 5))
ax.plot(
    a5_segments_gdf["segments"],
    a5_segments_gdf["cell_area"],
    marker="o",
)
ax.set_xscale("log")
ax.set_xlabel("segments")
ax.set_ylabel("cell_area (m²)")
ax.set_title("A5 cell area by segments")
ax.grid(True, which="both", alpha=0.3)
plt.tight_layout()
plt.show()

import matplotlib.pyplot as plt fig, ax = plt.subplots(figsize=(8, 5)) ax.plot( a5_segments_gdf["segments"], a5_segments_gdf["cell_area"], marker="o", ) ax.set_xscale("log") ax.set_xlabel("segments") ax.set_ylabel("cell_area (m²)") ax.set_title("A5 cell area by segments") ax.grid(True, which="both", alpha=0.3) plt.tight_layout() plt.show()

Area error by theoretical mean area¶

area_error = 100 * abs(norm_area - 1) (percent)

Log-log scale makes the convergence after segments > 100 easier to see.

In [ ]:

fig, ax = plt.subplots(figsize=(8, 5))
ax.plot(
    a5_segments_gdf["segments"],
    a5_segments_gdf["area_error"],
    marker="o",
)
ax.set_xscale("log")
ax.set_yscale("log")
ax.set_xlabel("A5 segments")
ax.set_ylabel("Area error (%)")
ax.set_title("A5 area error by segments")
ax.grid(True, which="both", alpha=0.3)
plt.tight_layout()
plt.show()

fig, ax = plt.subplots(figsize=(8, 5)) ax.plot( a5_segments_gdf["segments"], a5_segments_gdf["area_error"], marker="o", ) ax.set_xscale("log") ax.set_yscale("log") ax.set_xlabel("A5 segments") ax.set_ylabel("Area error (%)") ax.set_title("A5 area error by segments") ax.grid(True, which="both", alpha=0.3) plt.tight_layout() plt.show()

Plot geometries¶

In [3]:

a5_segments_gdf.plot(edgecolor="black", alpha=0.4)

a5_segments_gdf.plot(edgecolor="black", alpha=0.4)

Out[3]:

<Axes: >