Check input data for Tessellation for potential errors.
Tessellation requires data of relatively high level of precision and there
are three particular patterns causing issues.
1. Features will collapse into empty polygon - these do not have tessellation
cell in the end.
2. Features will split into MultiPolygon - at some cases, features with narrow links
between parts split into two during ‘shrinking’. In most cases that is not an issue
and resulting tessellation is correct anyway, but sometimes this result in a cell
being MultiPolygon, which is not correct.
3. Overlapping features - features which overlap even after ‘shrinking’ cause
invalid tessellation geometry.
CheckTessellationInput will check for all of these. Overlapping features
have to be fixed prior Tessellation. Features which will split will cause issues
only sometimes, so
should be checked and fixed if necessary. Features which will collapse could
ignored, but they will have to excluded from next steps of tessellation-based analysis.
GeoDataFrame containing objects to be used as gdf in Tessellation
distance for negative buffer
check for features which would collapse to empty polygon
check for features which would split into Multi-type
check for overlapping features (after negative buffer)
>>> check = CheckTessellationData(df)
Collapsed features : 3157
Split features : 519
Overlapping features: 22
features which would collapse to empty polygon
features which would split into Multi-type
overlapping features (after negative buffer)
Initialize self. See help(type(self)) for accurate signature.
__init__(gdf[, shrink, collapse, split, overlap])