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Enhanced Thiessen Polygon Method with 99.9% of Maximum Size Threshold #14

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Merged
YaoTingYao merged 1 commit into from
Mar 19, 2024
Merged

Enhanced Thiessen Polygon Method with 99.9% of Maximum Size Threshold #14

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32 changes: 28 additions & 4 deletions model_evaluation.py
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Original file line number Diff line number Diff line change
Expand Up @@ -13,6 +13,7 @@
import pandas as pd
from PyQt5.QtCore import QObject, pyqtSignal
import shutil
from geopandas import GeoDataFrame

# GDAL exceptions
gdal.UseExceptions()
Expand Down Expand Up @@ -322,6 +323,28 @@ def vector_to_raster(self,vector_fn,in_fn,raster_fn,data_type, nodata=None):
out_ds.FlushCache()
return

def remove_edge_cells(self, full_voronoi_grid: GeoDataFrame, area_mask: GeoDataFrame,
area_percentile_threshold: float) -> GeoDataFrame:
'''
Ensure thiessen polygon cells retain percentile threshold of maximum size after intersection with mask of the jurisdiction
:param full_voronoi_grid: thiessen polygon dataframe
:param area_mask: mask of the jurisdiction
:param area_percentile_threshold: area percentile threshold
:return thiessen_gdf: result dataframe
'''
thiessen_gdf = gpd.overlay(full_voronoi_grid, area_mask, how="intersection")
# get area of each polygon
thiessen_gdf["area"] = thiessen_gdf.area

max_area = thiessen_gdf["area"].max()
# using the area, calculate size of cell compared to max
thiessen_gdf["percentcell"] = thiessen_gdf["area"] / max_area

# select cells with more than thresh% of their area within the mask
thiessen_gdf = thiessen_gdf[thiessen_gdf["percentcell"] > area_percentile_threshold]

return thiessen_gdf

def create_thiessen_polygon (self, grid_area, mask, density, deforestation, out_fn, raster_fn):
'''
Create thiessen polygon
Expand Down Expand Up @@ -376,9 +399,10 @@ def create_thiessen_polygon (self, grid_area, mask, density, deforestation, out_
# Convert the study area to GeoDataFrame.
polydf = gpd.GeoDataFrame(geometry=[polygon], crs=mask_df.crs)

# Only preserved Thiessen Polygon within mask by spatial join with mask polygon
thiessen_gdf = gpd.sjoin(voronois, polydf, how="inner", predicate="within")
#explicitly set the crs
# Ensure Thiessen Polygon cells retain 99.9% of maximum size after intersection with study area
thiessen_gdf = self.remove_edge_cells(voronois, polydf, 0.999)

# Set the crs
thiessen_gdf.crs = mask_df.crs

self.progress_updated.emit(40)
Expand Down Expand Up @@ -438,7 +462,7 @@ def create_thiessen_polygon (self, grid_area, mask, density, deforestation, out_

# Export to csv
csv_file_path = out_fn.split('.')[0]+'.csv'
csv=clipped_gdf.drop('geometry', axis=1).to_csv(csv_file_path, columns=['ID', 'Actual Deforestation(ha)', 'Predicted Deforestation(ha)','Residuals(ha)'],
clipped_gdf.drop('geometry', axis=1).to_csv(csv_file_path, columns=['ID', 'Actual Deforestation(ha)', 'Predicted Deforestation(ha)','Residuals(ha)'],
index=False)

# Rename columns title for shapefile
Expand Down