verticapy.sql.geo.split_polygon_n#

verticapy.sql.geo.split_polygon_n(p: str, nbins: int = 100) → vDataFrame#

Splits a polygon into (nbins ²) smaller polygons of approximately equal total area. This process is inexact, and the split polygons have approximated edges; greater values for nbins produces more accurate and precise edge approximations.

Parameters#

p: str: String representation of the polygon.
nbins: int, optional: Number of bins used to cut the longitude and the latitude. Split polygons have approximated edges, and greater values for nbins leads to more accurate and precise edge approximations.

Returns#

vDataFrame: output vDataFrame that includes the new polygons.

Examples#

We import verticapy:

import verticapy as vp

Hint

By assigning an alias to verticapy, we mitigate the risk of code collisions with other libraries. This precaution is necessary because verticapy uses commonly known function names like “average” and “median”, which can potentially lead to naming conflicts. The use of an alias ensures that the functions from verticapy are used as intended without interfering with functions from other libraries.

Let’s use the following polygon.

p = 'POLYGON ((121.334030916 31.5081948415, 121.334030917 31.5079167872, 121.333748304 31.5081948413, 121.334030916 31.5081948415))'
poly = vp.vDataFrame({"triangle": [p]})
poly["triangle"].apply("ST_GeomFromText({})")
poly["triangle"].geo_plot(
    color="white",
    edgecolor="black",
)

../_images/sql_geo_functions_split_polygon_n.png

Now, let’s proceed to split the polygon into multiple parts.

from verticapy.sql.geo import split_polygon_n

split_p = split_polygon_n(p)
display(split_p)

	123 gid Integer 100%	🌎 Geometry(2024) 100%
1	99
2	100
3	199
4	200
5	201
6	299
7	300
8	301
9	302
10	399
11	400
12	401
13	402
14	403
15	499
16	500
17	501
18	502
19	503
20	504

Let’s visualize it.

split_p["geom"].geo_plot(
    color="white",
    edgecolor="black",
)

../_images/sql_geo_functions_split_polygon_n_2.png

Note

This function can be employed to partition the space into multiple smaller segments, enabling more precise analysis. It proves particularly useful for use cases such as analyzing density.