Added function generate_country_industrial_cells, its tests, test files

GDE_TOOLS_create_industrial_cells.py

@@ -36,7 +36,7 @@ from copy import deepcopy
 import numpy as np
 import pandas as pd
 import geopandas as gpd
-from shapely.geometry import Polygon
+from shapely.geometry import Point, Polygon
 
 
 def define_corners(centr_lon, centr_lat, width_EW, width_NS):
@@ -1203,3 +1203,345 @@ def get_relative_area_range(cells_gdf):
     areas_range_relative = areas_range / areas.mean()
 
     return areas_range_relative
+
+
+def generate_country_industrial_cells(
+    country,
+    col_lon,
+    col_lat,
+    width_EW,
+    width_NS,
+    id_str,
+    precision_points,
+    precision_cells,
+    aggr_model_pathname,
+    boundaries_pathname,
+    boundaries_type="shp",
+    consistency_checks=True,
+    autoadjust_overlap_gap=True,
+    in_crs="EPSG:4326",
+    consistency_tol_dist=0.05,
+    consistency_tol_area=0.05,
+):
+    """This function reads the input aggregated exposure model of the country (location defined
+    by aggr_model_pathname and country), identifies the unique points present in this input
+    model, and generates cell geometries around these points of widths width_EW and width_NS
+    (in the east-west and north-south directions, respectively). It then adjusts these
+    geometries so that the cells do not overlap and that consecutive cells do not have small
+    gaps in between them. This adjustment is necessary because it cannot be guaranteed that
+    the input points are spaced with sufficient accuracy. The identification of unique points
+    and the adjustment of the geometries is carried out according to precision levels specified
+    by precision_points and precision_cells.
+
+    If consistency_checks is True, four consistency checks are run:
+
+    1) Function overlap_by_full_geom_intersection() is used to verify that the created cells do
+    not overlap with one another. The cells pass this check if no overlaps are found (output
+    parameter overlap_found is "False").
+
+    2) Functions swell_cells_with_buffer(), overlap_by_full_geom_intersection() and
+    auto_adjust_overlaps_gaps() are used to verify that the created cells do not have gaps in
+    between them where there should not be any. The cells pass this check if no gaps are found
+    (output parameter gap_found is "False").
+
+    3) Function get_distance_centroids() is used to calculate the maximum distance between the
+    original points and the final centroids of the generated cells (max_dist_centr). This
+    maximum distance is compared against the tolerance, defined as:
+
+        max_dist_centr <= min(width_EW, width_NS)*consistency_tol_dist
+
+    The cells pass this check if max_dist_centr is smaller than the tolerance (output parameter
+    big_dist_diff is "False").
+
+    4) Function get_relative_area_range() is used to calcuate the variability of the areas of
+    the resulting cells, as [max-min]/mean. This value is compared against consistency_tol_area:
+    if the former is smaller, the cells pass this check (output parameter big_area_diff is
+    "False").
+
+    If consistency_checks is False, the consistency checks are not run and all associated output
+    parameters take the value "Not_Checked".
+
+    If both consistency_checks and autoadjust_overlap_gap are True, the function automatically
+    adjusts the geometry of the cells if overlaps or gaps are identified. Gaps between cells
+    that are located diagonally with one another and that have other neighbouring cells in other
+    directions are ignored.
+
+    If autoadjust_overlap_gap is True, overlap_found and gap_found will be False, because the
+    function keeps on adjusting the geometries of the cells until no overlaps or gaps are found.
+
+    The adjusted cells are then cropped as per the country's boundaries (input file defined by
+    boundaries_pathname). The consistency checks are run before this step because the cropping
+    will affect the location of the centroids of the cells and their areas.
+
+    Args:
+        country (str): Name of country (as in the files of the aggregated model).
+        col_lon (str): Name of the column in the file of the aggregated exposure model that
+                       contains longitudes.
+        col_lat (str): Name of the column in the file of the aggregated exposure model that
+                       contains latitudes.
+        width_EW (float): Width of the cell in the east-west direction, in degrees, >0.
+        width_NS (float): Width of the cell in the north-south direction, in degrees, >0.
+        id_str (str): First part of the string used to generate IDs of the inidividual points.
+                      in which the aggregated exposure model is defined. E.g. "IND_".
+        precision_points (int): Number of decimal places to be used to determine unique points
+                                present in the aggregated exposure model (the aggregated model
+                                will most likely have several entries for the "same" point).
+        precision_cells (dictionary): Dictionary with four keys, "lon_w", "lat_s", "lon_e", and
+                                      "lat_n". Each key contains a formatting string such as
+                                      '{:.3f}', for example. It is used to round the coordinates
+                                      of the cells' corners to a certain precision to identify
+                                      those that need to be adjusted to be the exact same value.
+        aggr_model_pathname (str): Path where the files of the aggregated model are located.
+        boundaries_pathname (str): Path where the boundaries of the country are located.
+        boundaries_type (str): File type containing the boundaries ("shp"=Shapefile, "gpkg"=
+                               Geopackage). Default: "shp".
+        consistency_checks (bool): If True, consistency checks will be run to check the quality
+                                   of the output produced. If False, they will not.
+                                   Default: True.
+        autoadjust_overlap_gap (bool): If True, the code will automatically adjust cells for
+                                       which an overlap or gap was found (consistency_checks
+                                       needs to be True for this check to be run). If False, no
+                                       automatic adjustment will be carried out.
+        in_crs (str): CRS of the data (default="EPSG:4326").
+        consistency_tol_dist (float): Tolerance to assess how large the maximum distance between
+                                      the original points and the centroids of the generated
+                                      cells is with respect to the width of the cells. The value
+                                      of consistency_tol_dist is multiplied by the smallest of
+                                      width_EW and width_NS to define the tolerance in degrees.
+                                      Default: 0.05 (i.e. 5% of the smallest width). Only needed
+                                      if consistency_checks is True.
+        consistency_tol_area (float): Tolerance to assess how large the variability of the area
+                                      of the generated cells is. The value of
+                                      consistency_tol_area is compared against the relative area
+                                      range, which is calculated as ([max-min]/mean) of all
+                                      cells. Default: 0.05. Only needed if consistency_checks is
+                                      True.
+
+    Returns:
+        cells_adj_bound_gdf (GeoDataFrame): GeoPandas GeoDataFrame with the cells defined around
+                                            the points at which the aggregated model is defined
+                                            for the country. The cells have already been
+                                            "cropped" around the country boundaries.
+                                            Each row is one of these points. It contains the
+                                            following columns:
+                                            - id: ID of the point, given by id_str and an
+                                                  incremental integer.
+                                            - coordinates: the names of these columns are given
+                                                           by col_lon and col_lat. They contain
+                                                           the original coordinates as given in
+                                                           the input aggregated exposure model.
+                                            - lon_w, lat_s, lon_e, lat_n: coordinates that
+                                                                          define the corners of
+                                                                          the cells.
+                                            - geometry: (Shapely) polygons of the output cells.
+        aggr_mod_df (DataFrame): Pandas DataFrame with the aggregated exposure model of the
+                                 country. The content is exactly the same as in the original
+                                 input file, plus two additional columns "ID_99" and "NAME_99",
+                                 both of which contain the IDs of the cells of cells_adj_gdf
+                                 associated with each entry of aggr_mod_df.
+        overlap_found (str): "True" if consistency_checks=True and an overlap between the
+                             calculated cell geometries was found, "False" if consistency_checks
+                             =True and an overlap was not found (which means that the cells pass
+                             this consistency check), "Not_Checked" if consistency_checks=False,
+                             i.e. the consistency checks were not run.
+        gap_found (str): "True" if consistency_checks=True and a gap between the calculated cell
+                         geometries was found, "False" if consistency_checks=True and a gap was
+                         not found (which means that the cells pass this consistency check),
+                         "Not_Checked" if consistency_checks=False, i.e. the consistency checks
+                         were not run.
+        big_dist_diff (str): "True" if consistency_checks=True and the maximum distance between
+                             the original input points and the centroids of the calculated cells
+                             (before the cropping as per country boundaries) is larger than the
+                             tolerance, "False" if consistency_checks=True and the maximum
+                             distance is smaller than the tolerance (which means that the cells
+                             pass this consistency check), "Not_Checked" if consistency_checks=
+                             False, i.e. the consistency checks were not run.
+        big_area_diff (str): "True" if consistency_checks=True and the variability of the areas
+                             of the resulting cells before the cropping as per country
+                             boundaries) is larger than the tolerance, "False" if
+                             consistency_checks=True and the variability is smaller than the
+                             tolerance (which means that the cells pass this consistency check),
+                             "Not_Checked" if consistency_checks=False, i.e. the consistency
+                             checks were not run.
+        country_id (str): ID of the country as specified in the country boundaries file given
+                          as input.
+    """
+
+    # Return the input in_df if width_EW or width_NS are not valid:
+    if (width_EW <= 0.0) or (width_NS <= 0):
+        print(
+            "ERROR in generate_country_industrial_cells: "
+            "with_EW and width_NS need to be positive numbers >=0"
+        )
+        return
+
+    # Load exposure file of the aggregated exposure model:
+    aggr_model_filepath = os.path.join(aggr_model_pathname, "Exposure_Ind_%s.csv" % (country))
+    if not os.path.isfile(aggr_model_filepath):
+        print("ERROR in generate_country_industrial_cells: aggregated model file not found")
+        return
+    aggr_mod_df = pd.read_csv(aggr_model_filepath, sep=",")  # aggr_mod_df is a Pandas DataFrame
+
+    # Load country boundaries:
+    boundaries_filepath = os.path.join(
+        boundaries_pathname, "Adm0_%s.%s" % (country, boundaries_type)
+    )
+    if not os.path.isfile(boundaries_filepath):
+        print("ERROR in generate_country_industrial_cells: country boundaries file not found")
+        return
+    bounds_gdf = gpd.read_file(boundaries_filepath)
+
+    # Retrieve unique points in the exposure file, determined with a specific precision
+    # (points_gdf is a GeoPandas GeoDataFrame, ids_aggr is an array of strings with length
+    # equal to the number of rows of aggr_mod_df):
+    points_gdf, ids_aggr = retrieve_unique_points(
+        aggr_mod_df, col_lon, col_lat, id_str, precision=precision_points, in_crs=in_crs
+    )
+
+    # Add the IDs of the unique points to the DataFrame of the input aggregated model:
+    aggr_mod_df["ID_99"] = ids_aggr
+    aggr_mod_df["NAME_99"] = ids_aggr
+
+    # Define cells around the unique points (cells_gdf is a GeoPandas GeoDataFrame):
+    cells_gdf = define_cells_in_dataframe(
+        points_gdf, col_lon, col_lat, width_EW, width_NS, in_crs=in_crs
+    )
+
+    # Create dictionaries with all coordinates of the corners of the cells:
+    coords_dict = create_dict_all_coordinates(cells_gdf, precision_cells)
+    if len(coords_dict.keys()) < 1:
+        print(
+            "ERROR in generate_country_industrial_cells: create_dict_all_coordinates "
+            "has returned an empty dictionary"
+        )
+        return
+
+    # Create dictionary with unique values of the coordinates of the corners of the cells, to
+    # a certain precision (given by precision_cells when generating coords_dict):
+    coords_uq = create_dict_unique_coordinates(coords_dict)
+
+    # Adjust all the coordinates of the corners of the cells (coords_dict) by taking the
+    # average value of all instances of that coordinate that "should be the same", as identified
+    # in coords_uq:
+    coords_dict_adj = adjust_coords(coords_dict, coords_uq)  # coords_dict_adj is a dictionary
+
+    # Generate final output with adjusted cell geometries (cells_adj_gdf is a
+    # GeoPandas GeoDataFrame):
+    cells_adj_gdf = build_adjusted_cells_dataframe(cells_gdf, coords_dict_adj)
+
+    # Run consistency checks if requested:
+    if not consistency_checks:
+        overlap_found = "Not_Checked"
+        gap_found = "Not_Checked"
+        big_dist_diff = "Not_Checked"
+        big_area_diff = "Not_Checked"
+    else:
+        # Consistency check 1: the output geometries should not overlap
+        num_overlaps = 999  # Initialise variable for the while loop to run at least once
+        while num_overlaps > 0:
+            intsect_gdf = overlap_by_full_geom_intersection(cells_adj_gdf, "id_1", "id_2")
+            num_overlaps = intsect_gdf.shape[0]
+            if num_overlaps > 0:
+                if not autoadjust_overlap_gap:  # The user specified not to automatically adjust
+                    overlap_found = "True"
+                    break
+                # Automatically adjust the overlaps
+                cells_adj_gdf, _ = auto_adjust_overlaps_gaps(
+                    cells_adj_gdf,
+                    intsect_gdf,
+                    col_lon,
+                    col_lat,
+                    width_EW,
+                    width_NS,
+                    "overlap",
+                    "id_1",
+                    "id_2",
+                )
+            else:  # No overlaps found, the while loop will end
+                overlap_found = "False"
+
+        # Consistency check 2:
+        gaps_found = True  # Initialise variable for the while loop to run at least once
+        while gaps_found:
+            # Expand the cells by 25% of their dimensions in all directions:
+            cells_adj_offset_gdf = swell_cells_with_buffer(
+                cells_adj_gdf, 0.25 * width_EW, 0.25 * width_NS
+            )
+
+            # Identify intersections in the expanded version:
+            intsect_gdf = overlap_by_full_geom_intersection(
+                cells_adj_offset_gdf, "id_1", "id_2"
+            )
+
+            # Automatically adjust the potential gaps and store it in auxiliary variable to
+            # determine if there are gaps or not:
+            cells_adj_aux_gdf, gaps_found = auto_adjust_overlaps_gaps(
+                cells_adj_gdf,
+                intsect_gdf,
+                col_lon,
+                col_lat,
+                width_EW,
+                width_NS,
+                "gap",
+                "id_1",
+                "id_2",
+            )
+
+            if not autoadjust_overlap_gap:  # The user specified not to automatically adjust
+                if gaps_found:
+                    gap_found = "True"
+                else:
+                    gap_found = "False"
+                break  # This will be the result of the check, the while loop will not run again
+
+            # Adopt the auxiliary adjusted version:
+            cells_adj_gdf = deepcopy(cells_adj_aux_gdf)
+            gap_found = str(gaps_found)
+
+        # Consistency check 3: maximum distance between original points and final centroids:
+        max_dist_centr = get_distance_centroids(cells_adj_gdf, col_lon, col_lat)
+        # Compare the maximum distance against the tolerance:
+        if max_dist_centr > min(width_EW, width_NS) * consistency_tol_dist:
+            big_dist_diff = "True"
+        else:
+            big_dist_diff = "False"
+
+        # Consistency check 4: stability/variability of area of resulting cells:
+        rel_area_range = get_relative_area_range(cells_gdf)
+        # Compare the relative area range ([max-min]/mean) against the tolerance:
+        if rel_area_range > consistency_tol_area:
+            big_area_diff = "True"
+        else:
+            big_area_diff = "False"
+
+    # Intersect cells with admnistrative boundary of country:
+    cells_adj_bound_gdf = gpd.overlay(cells_adj_gdf, bounds_gdf, how="intersection")
+    # Eliminate columns that are not useful:
+    if "ID_0" in cells_adj_bound_gdf.columns:
+        country_id = str(cells_adj_bound_gdf["ID_0"].values[0])
+        del cells_adj_bound_gdf["ID_0"]
+    else:
+        country_id = "UNK"
+    if "NAME_0" in cells_adj_bound_gdf.columns:
+        del cells_adj_bound_gdf["NAME_0"]
+    if "id_2" in cells_adj_bound_gdf.columns:
+        del cells_adj_bound_gdf["id_2"]
+    if "id_1" in cells_adj_bound_gdf.columns:
+        cells_adj_bound_gdf = cells_adj_bound_gdf.rename(columns={"id_1": "id"})
+    # Update columns "lon_w", "lat_s", "lon_e", and "lat_n" of cells_adj_bound_gdf:
+    for row in range(0, cells_adj_bound_gdf.shape[0]):
+        geometry_row = cells_adj_bound_gdf["geometry"].values[row]
+        cells_adj_bound_gdf["lon_w"].values[row] = geometry_row.bounds[0]
+        cells_adj_bound_gdf["lon_e"].values[row] = geometry_row.bounds[2]
+        cells_adj_bound_gdf["lat_s"].values[row] = geometry_row.bounds[1]
+        cells_adj_bound_gdf["lat_n"].values[row] = geometry_row.bounds[3]
+
+    return (
+        cells_adj_bound_gdf,
+        aggr_mod_df,
+        overlap_found,
+        gap_found,
+        big_dist_diff,
+        big_area_diff,
+        country_id,
+    )

tests/data/GDE_TOOLS_create_industrial_cells/Adm0_Country_A.prj

+GEOGCS["GCS_WGS_1984",DATUM["D_WGS_1984",SPHEROID["WGS_1984",6378137,298.257223563]],PRIMEM["Greenwich",0],UNIT["Degree",0.017453292519943295]]
\ No newline at end of file

tests/data/GDE_TOOLS_create_industrial_cells/Adm0_Country_A.qpj

+GEOGCS["WGS 84",DATUM["WGS_1984",SPHEROID["WGS 84",6378137,298.257223563,AUTHORITY["EPSG","7030"]],AUTHORITY["EPSG","6326"]],PRIMEM["Greenwich",0,AUTHORITY["EPSG","8901"]],UNIT["degree",0.0174532925199433,AUTHORITY["EPSG","9122"]],AUTHORITY["EPSG","4326"]]

tests/data/GDE_TOOLS_create_industrial_cells/Adm0_Country_B.prj

+GEOGCS["GCS_WGS_1984",DATUM["D_WGS_1984",SPHEROID["WGS_1984",6378137,298.257223563]],PRIMEM["Greenwich",0],UNIT["Degree",0.017453292519943295]]
\ No newline at end of file

tests/data/GDE_TOOLS_create_industrial_cells/Adm0_Country_B.qpj

+GEOGCS["WGS 84",DATUM["WGS_1984",SPHEROID["WGS 84",6378137,298.257223563,AUTHORITY["EPSG","7030"]],AUTHORITY["EPSG","6326"]],PRIMEM["Greenwich",0,AUTHORITY["EPSG","8901"]],UNIT["degree",0.0174532925199433,AUTHORITY["EPSG","9122"]],AUTHORITY["EPSG","4326"]]

tests/data/GDE_TOOLS_create_industrial_cells/Adm0_Country_C.prj

+GEOGCS["GCS_WGS_1984",DATUM["D_WGS_1984",SPHEROID["WGS_1984",6378137,298.257223563]],PRIMEM["Greenwich",0],UNIT["Degree",0.017453292519943295]]
\ No newline at end of file

tests/data/GDE_TOOLS_create_industrial_cells/Adm0_Country_C.qpj

+GEOGCS["WGS 84",DATUM["WGS_1984",SPHEROID["WGS 84",6378137,298.257223563,AUTHORITY["EPSG","7030"]],AUTHORITY["EPSG","6326"]],PRIMEM["Greenwich",0,AUTHORITY["EPSG","8901"]],UNIT["degree",0.0174532925199433,AUTHORITY["EPSG","9122"]],AUTHORITY["EPSG","4326"]]

tests/data/GDE_TOOLS_create_industrial_cells/Exposure_Ind_Country_A.csv

+LONGITUDE,LATITUDE,TAXONOMY,BUILDINGS,DWELLINGS,OCCUPANTS_PER_ASSET,COST_PER_AREA_EUR,ID_1,NAME_1,ID_2,NAME_2
+12,50,BdgClassA,50,50,500,2000,1,State 1,11,Municipality 1
+12,50,BdgClassB,30,30,400,2000,1,State 1,11,Municipality 1
+12,50,BdgClassC,20,20,300,2000,1,State 1,11,Municipality 1
+12,50.0082,BdgClassA,40,40,400,2000,1,State 1,11,Municipality 1
+12,50.0082,BdgClassB,40,40,533,2000,1,State 1,11,Municipality 1
+12,50.0082,BdgClassC,30,30,450,2000,1,State 1,11,Municipality 1
+12.0082,50,BdgClassA,15,15,150,2000,1,State 1,11,Municipality 1
+12.0082,50,BdgClassD,30,30,500,2000,1,State 1,11,Municipality 1
+12.0081,50.0082,BdgClassA,20,20,200,2000,2,State 2,25,Municipality 5
+12.0081,50.0082,BdgClassB,15,15,200,2000,2,State 2,25,Municipality 5
+12.0081,50.0082,BdgClassC,25,25,375,2000,2,State 2,25,Municipality 5
+12.0081,50.0082,BdgClassD,30,30,500,2000,2,State 2,25,Municipality 5
+12.0162,50.0162,BdgClassB,45,45,600,2000,2,State 2,27,Municipality 7
+12.0162,50.0162,BdgClassD,60,60,1000,2000,2,State 2,27,Municipality 7
\ No newline at end of file

tests/data/GDE_TOOLS_create_industrial_cells/Exposure_Ind_Country_A_output.csv

+LONGITUDE,LATITUDE,TAXONOMY,BUILDINGS,DWELLINGS,OCCUPANTS_PER_ASSET,COST_PER_AREA_EUR,ID_1,NAME_1,ID_2,NAME_2,ID_99,NAME_99
+12,50,BdgClassA,50,50,500,2000,1,State 1,11,Municipality 1,CA_IND_0,CA_IND_0
+12,50,BdgClassB,30,30,400,2000,1,State 1,11,Municipality 1,CA_IND_0,CA_IND_0
+12,50,BdgClassC,20,20,300,2000,1,State 1,11,Municipality 1,CA_IND_0,CA_IND_0
+12,50.0082,BdgClassA,40,40,400,2000,1,State 1,11,Municipality 1,CA_IND_1,CA_IND_1
+12,50.0082,BdgClassB,40,40,533,2000,1,State 1,11,Municipality 1,CA_IND_1,CA_IND_1
+12,50.0082,BdgClassC,30,30,450,2000,1,State 1,11,Municipality 1,CA_IND_1,CA_IND_1
+12.0082,50,BdgClassA,15,15,150,2000,1,State 1,11,Municipality 1,CA_IND_3,CA_IND_3
+12.0082,50,BdgClassD,30,30,500,2000,1,State 1,11,Municipality 1,CA_IND_3,CA_IND_3
+12.0081,50.0082,BdgClassA,20,20,200,2000,2,State 2,25,Municipality 5,CA_IND_2,CA_IND_2
+12.0081,50.0082,BdgClassB,15,15,200,2000,2,State 2,25,Municipality 5,CA_IND_2,CA_IND_2
+12.0081,50.0082,BdgClassC,25,25,375,2000,2,State 2,25,Municipality 5,CA_IND_2,CA_IND_2
+12.0081,50.0082,BdgClassD,30,30,500,2000,2,State 2,25,Municipality 5,CA_IND_2,CA_IND_2
+12.0162,50.0162,BdgClassB,45,45,600,2000,2,State 2,27,Municipality 7,CA_IND_4,CA_IND_4
+12.0162,50.0162,BdgClassD,60,60,1000,2000,2,State 2,27,Municipality 7,CA_IND_4,CA_IND_4
\ No newline at end of file

tests/data/GDE_TOOLS_create_industrial_cells/Exposure_Ind_Country_B.csv

+LONGITUDE,LATITUDE,TAXONOMY,BUILDINGS,DWELLINGS,OCCUPANTS_PER_ASSET,COST_PER_AREA_EUR,ID_1,NAME_1,ID_2,NAME_2
+12,50,BdgClassA,50,50,500,2000,1,State 1,11,Municipality 1
+12,50,BdgClassB,30,30,400,2000,1,State 1,11,Municipality 1
+12,50,BdgClassC,20,20,300,2000,1,State 1,11,Municipality 1
+12.001,50.0082,BdgClassA,40,40,400,2000,1,State 1,11,Municipality 1
+12.001,50.0082,BdgClassB,40,40,533,2000,1,State 1,11,Municipality 1
+12.001,50.0082,BdgClassC,30,30,450,2000,1,State 1,11,Municipality 1
+12.0082,50,BdgClassA,15,15,150,2000,1,State 1,11,Municipality 1
+12.0082,50,BdgClassD,30,30,500,2000,1,State 1,11,Municipality 1
+12.0081,50.0082,BdgClassA,20,20,200,2000,2,State 2,25,Municipality 5
+12.0081,50.0082,BdgClassB,15,15,200,2000,2,State 2,25,Municipality 5
+12.0081,50.0082,BdgClassC,25,25,375,2000,2,State 2,25,Municipality 5
+12.0081,50.0082,BdgClassD,30,30,500,2000,2,State 2,25,Municipality 5
+12.0162,50.0162,BdgClassB,45,45,600,2000,2,State 2,27,Municipality 7
+12.0162,50.0162,BdgClassD,60,60,1000,2000,2,State 2,27,Municipality 7
\ No newline at end of file

tests/data/GDE_TOOLS_create_industrial_cells/Exposure_Ind_Country_B_output.csv

+LONGITUDE,LATITUDE,TAXONOMY,BUILDINGS,DWELLINGS,OCCUPANTS_PER_ASSET,COST_PER_AREA_EUR,ID_1,NAME_1,ID_2,NAME_2,ID_99,NAME_99
+12,50,BdgClassA,50,50,500,2000,1,State 1,11,Municipality 1,CB_IND_0,CB_IND_0
+12,50,BdgClassB,30,30,400,2000,1,State 1,11,Municipality 1,CB_IND_0,CB_IND_0
+12,50,BdgClassC,20,20,300,2000,1,State 1,11,Municipality 1,CB_IND_0,CB_IND_0
+12.001,50.0082,BdgClassA,40,40,400,2000,1,State 1,11,Municipality 1,CB_IND_1,CB_IND_1
+12.001,50.0082,BdgClassB,40,40,533,2000,1,State 1,11,Municipality 1,CB_IND_1,CB_IND_1
+12.001,50.0082,BdgClassC,30,30,450,2000,1,State 1,11,Municipality 1,CB_IND_1,CB_IND_1
+12.0082,50,BdgClassA,15,15,150,2000,1,State 1,11,Municipality 1,CB_IND_3,CB_IND_3
+12.0082,50,BdgClassD,30,30,500,2000,1,State 1,11,Municipality 1,CB_IND_3,CB_IND_3
+12.0081,50.0082,BdgClassA,20,20,200,2000,2,State 2,25,Municipality 5,CB_IND_2,CB_IND_2
+12.0081,50.0082,BdgClassB,15,15,200,2000,2,State 2,25,Municipality 5,CB_IND_2,CB_IND_2
+12.0081,50.0082,BdgClassC,25,25,375,2000,2,State 2,25,Municipality 5,CB_IND_2,CB_IND_2
+12.0081,50.0082,BdgClassD,30,30,500,2000,2,State 2,25,Municipality 5,CB_IND_2,CB_IND_2
+12.0162,50.0162,BdgClassB,45,45,600,2000,2,State 2,27,Municipality 7,CB_IND_4,CB_IND_4
+12.0162,50.0162,BdgClassD,60,60,1000,2000,2,State 2,27,Municipality 7,CB_IND_4,CB_IND_4
\ No newline at end of file