I have a shapefile containing the map of Milano that contain square polygons with different size, I mean small, medium and big squares and each square has an identifier and I want to fill these polygons with the data which is reported in another file. The Shapefile polygons are as follows:

                  id                                           geometry
0           3939_1_1  POLYGON ((504174.2673271392 5003118.268122713,...
1         3939_1_2_1  POLYGON ((508268.9938896392 5003118.268122713,...
2     3939_1_2_2_3_3  POLYGON ((511851.8796318267 5005064.825739901,...
3     3939_1_2_2_3_0  POLYGON ((511340.0388115142 5005064.825739901,...
4       3939_1_2_2_0  POLYGON ((510316.3571708892 5004415.973200838,...
5         3939_1_2_3  POLYGON ((510316.3571708892 5005713.678278963,...

This is also the data file that I have

3939_0_0    5.906552126050579   848.3214545643237
3939_0_1    5.82302081392767    1465.1476909159426
3939_0_2    11.465018361277856  742.8107082619533
3939_0_3_1  3.820090639450465   325.98139282061385
3939_0_3_3  5.1252845330079415  558.5800634528392

and I have plotted these data on Milano shapefile and I have created heatmaps but since the squares do not have the same size the data must be normalized over each polygon area. Is there any suggestion how can I normalize my data over each polygon area?

here is some part of the code that I have created these heatmaps.

import pandas as pd
import geopandas as gpd
from geopandas import GeoSeries, GeoDataFrame
mapDir = "/home/foroogh/PhD/milano-grid"
mapName = "intersection_Milano_W_GRIDIT_NEW.shp"
map_path = os.path.join(mapDir, mapName)
milano = gpd.read_file(map_path)
df1 = pd.read_csv(file_,sep='\t',index_col=None, header=None, names=    ['squareid','nofcalls','callduration'])
df1 = milano.set_index("id").join(df1.set_index('squareid'))
fig = plt.figure()
df1.plot(column = "callduration", cmap = "jet", figsize = (20,20))
filename3 = str(file_.split("/")[-1])+'.png'
plt.savefig(os.path.join(root_path2, os.path.basename(file_)))
plt.close(' ')

Use the area method to retrieve polygons areas and normalize your data:

gdf['data_norm'] = gdf['data']/gdf.area

Unit is the same as the one defining your polygons geometry (and your projection). Using equal area projection make sense. You can also add unit conversion to the formula.


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