updated

Author: bogdanoancea

fig_pca_iforest.pdf

@@ -179,6 +179,20 @@ def download_tertiary_share(year: int) -> pd.DataFrame:
     return df
 
 
+def download_population_density(year: int) -> pd.DataFrame:
+    """
+    Population density at NUTS2:
+      dataset: demo_r_d3dens
+    """
+    logging.info("Downloading population density from demo_r_d3dens ...")
+    df = eurostat.get_data_df("demo_r_d3dens")
+    df = eurostat_wide_to_long(df)
+
+    df = filter_nuts2_and_year(df, year)
+    df = df[["geo", "time", "values"]].rename(columns={"values": "pop_density"})
+    return df
+
+
 def build_regional_dataset(year: int) -> pd.DataFrame:
     """
     Download all indicators for the specified year and merge by (geo, time).
@@ -191,10 +205,10 @@ def build_regional_dataset(year: int) -> pd.DataFrame:
     df_gdp = download_gdp_pc(year)
     df_unemp = download_unemployment_rate(year)
     df_tert = download_tertiary_share(year)
-
+    df_pop = download_population_density(year)
     # Merge step-by-step
     df = df_gdp.copy()
-    for other in [df_unemp, df_tert]:
+    for other in [df_unemp, df_tert, df_pop]:
         df = pd.merge(df, other, on=["geo", "time"], how="outer")
 
     # Rename and basic cleaning
@@ -206,7 +220,7 @@ def build_regional_dataset(year: int) -> pd.DataFrame:
     df["region_code"] = df["region_code"].astype(str)
     df = df[df["region_code"].str.len() == 4].copy()
 
-    indicator_cols = ["gdp_pc_pps", "unemployment_rate", "tertiary_share_25_64"]
+    indicator_cols = ["gdp_pc_pps", "unemployment_rate", "tertiary_share_25_64", "pop_density"]
 
     # Collapse duplicates per region_code: first non-null per column
     def first_non_null(series: pd.Series):
@@ -219,6 +233,7 @@ def first_non_null(series: pd.Series):
             "gdp_pc_pps": first_non_null,
             "unemployment_rate": first_non_null,
             "tertiary_share_25_64": first_non_null,
+            "pop_density": first_non_null,
         }
     )
 
@@ -364,9 +379,9 @@ def plot_pca_iforest(df_out: pd.DataFrame, output_path: str = "fig_pca_iforest.p
     plt.scatter(normal["PC1"], normal["PC2"], alpha=0.6, label="Normal regions")
     plt.scatter(anomalies["PC1"], anomalies["PC2"], marker="x", s=80, label="IForest anomalies")
 
-    plt.xlabel("PC1")
-    plt.ylabel("PC2")
-    plt.title("PCA of regional indicators (Isolation Forest anomalies)")
+    plt.xlabel("PC1", fontsize = 14)
+    plt.ylabel("PC2", fontsize = 14)
+    plt.title("PCA of regional indicators (Isolation Forest anomalies)", fontsize = 16)
     plt.legend()
     plt.tight_layout()
     plt.savefig(output_path)
@@ -481,7 +496,7 @@ def main():
     # ----------------------------------------------------------
     # Select the indicator columns
     # ----------------------------------------------------------
-    indicators = ["gdp_pc_pps", "unemployment_rate", "tertiary_share_25_64"]
+    indicators = ["gdp_pc_pps", "unemployment_rate", "pop_density", "tertiary_share_25_64"]
 
     # Extract indicator matrix
     X = anom[indicators].copy()
@@ -493,15 +508,15 @@ def main():
     # Build heatmap DataFrame with region_code as index
     heat_df = pd.DataFrame(
         X_std,
-        columns=["GDP per capita (std)", "Unemployment rate (std)", "Tertiary education (std)"],
+        columns=["GDP per capita (std)", "Unemployment rate (std)", "Population density", "Tertiary education (std)"],
         index=anom["region_code"]
     )
 
     # ----------------------------------------------------------
     # Plot heatmap
     # ----------------------------------------------------------
-    plt.figure(figsize=(10, 16))
-    sns.heatmap(
+    plt.figure(figsize=(8, 8))
+    ax = sns.heatmap(
         heat_df,
         cmap="coolwarm",
         center=0,
@@ -510,9 +525,23 @@ def main():
         cbar_kws={"label": "Standardized value (z-score)"}
     )
 
-    plt.title("Standardized Indicators for Anomalous NUTS2 Regions (2022)", fontsize=14)
-    plt.xlabel("Indicators")
-    plt.ylabel("Region")
+    # Axis titles
+    plt.title(
+        "Standardized Indicators for Anomalous NUTS2 Regions (2022)",
+        fontsize=16
+    )
+    plt.xlabel("Indicators", fontsize=14)
+    plt.ylabel("Region", fontsize=14)
+
+    # Tick labels (x = indicators, y = regions)
+    plt.xticks(fontsize=12, rotation=45, ha="right")
+    plt.yticks(fontsize=12)
+
+    # Colour bar label + tick labels
+    cbar = ax.collections[0].colorbar
+    cbar.ax.set_ylabel("Standardized value (z-score)", fontsize=14)
+    cbar.ax.tick_params(labelsize=12)
+
     plt.tight_layout()
     plt.savefig("heatmap_anomalies.pdf")
     plt.close()

heatmap_anomalies.pdf

@@ -1,297 +1,302 @@
-region_code,year,gdp_pc_pps,unemployment_rate,tertiary_share_25_64
-AT11,2022,86.0,3.9,31.4
-AT12,2022,101.0,13.2,34.1
-AT13,2022,140.0,20.7,45.6
-AT21,2022,110.0,3.9,33.3
-AT22,2022,114.0,9.9,32.2
-AT31,2022,127.0,6.6,32.4
-AT32,2022,149.0,2.8,33.2
-AT33,2022,127.0,2.8,31.9
-AT34,2022,147.0,3.0,29.9
-BE10,2022,194.0,23.8,53.5
-BE21,2022,141.0,8.0,47.1
-BE22,2022,96.0,7.7,43.7
-BE23,2022,113.0,5.0,49.6
-BE24,2022,134.0,12.9,51.6
-BE25,2022,116.0,5.6,39.8
-BE31,2022,145.0,8.2,60.7
-BE32,2022,73.0,20.5,34.2
-BE33,2022,86.0,14.6,41.5
-BE34,2022,74.0,4.7,42.1
-BE35,2022,79.0,16.2,43.3
-BG31,2022,41.0,23.8,18.8
-BG32,2022,42.0,19.5,26.3
-BG33,2022,48.0,13.0,28.7
-BG34,2022,58.0,12.3,21.8
-BG41,2022,97.0,12.3,43.2
-BG42,2022,43.0,6.3,22.8
-CH01,2022,,11.1,43.5
-CH02,2022,,8.7,40.8
-CH03,2022,,7.7,44.8
-CH04,2022,,8.0,55.2
-CH05,2022,,4.5,39.2
-CH06,2022,,5.1,44.1
-CH07,2022,,11.3,39.5
-CY00,2022,98.0,7.2,48.0
-CZ01,2022,199.0,2.1,51.5
-CZ02,2022,80.0,5.7,24.9
-CZ03,2022,74.0,9.5,21.7
-CZ04,2022,62.0,15.4,15.6
-CZ05,2022,71.0,9.8,22.9
-CZ06,2022,83.0,9.0,27.2
-CZ07,2022,71.0,18.0,24.0
-CZ08,2022,70.0,22.3,22.4
-DE11,2022,147.0,5.2,36.2
-DE12,2022,127.0,5.8,36.2
-DE13,2022,109.0,2.3,33.2
-DE14,2022,125.0,2.1,35.0
-DE21,2022,172.0,4.3,42.3
-DE22,2022,119.0,,27.2
-DE23,2022,124.0,,30.4
-DE24,2022,109.0,3.0,27.4
-DE25,2022,128.0,2.7,33.5
-DE26,2022,115.0,2.2,29.8
-DE27,2022,111.0,2.2,29.8
-DE30,2022,123.0,15.1,48.2
-DE40,2022,88.0,2.9,29.5
-DE50,2022,139.0,5.6,31.0
-DE60,2022,202.0,9.2,38.1
-DE71,2022,146.0,8.1,34.6
-DE72,2022,99.0,2.6,31.3
-DE73,2022,107.0,2.4,25.9
-DE80,2022,86.0,3.5,25.5
-DE91,2022,126.0,3.1,32.8
-DE92,2022,110.0,5.9,30.5
-DE93,2022,85.0,2.6,27.7
-DE94,2022,108.0,5.2,27.9
-DEA1,2022,119.0,8.9,28.7
-DEA2,2022,122.0,7.4,34.4
-DEA3,2022,99.0,8.8,27.4
-DEA4,2022,110.0,2.4,26.7
-DEA5,2022,102.0,8.4,26.4
-DEB1,2022,97.0,2.8,26.2
-DEB2,2022,87.0,,29.9
-DEB3,2022,117.0,6.4,32.5
-DEC0,2022,100.0,3.7,26.8
-DED2,2022,94.0,3.8,34.2
-DED4,2022,84.0,2.3,25.0
-DED5,2022,97.0,3.5,35.9
-DEE0,2022,87.0,12.1,25.3
-DEF0,2022,99.0,8.0,26.5
-DEG0,2022,86.0,2.7,27.8
-DK01,2022,192.0,8.6,53.1
-DK02,2022,87.0,7.3,32.9
-DK03,2022,111.0,7.6,34.7
-DK04,2022,113.0,7.1,41.3
-DK05,2022,100.0,7.6,34.8
-EA20,2022,,12.2,34.9
-EE00,2022,84.0,12.0,42.1
-EL30,2022,92.0,19.4,45.6
-EL41,2022,41.0,9.5,26.7
-EL42,2022,65.0,12.5,28.6
-EL43,2022,57.0,14.1,28.9
-EL51,2022,44.0,14.1,27.6
-EL52,2022,54.0,15.5,33.1
-EL53,2022,56.0,11.9,27.6
-EL54,2022,43.0,13.0,30.9
-EL61,2022,51.0,10.8,33.5
-EL62,2022,58.0,16.7,18.5
-EL63,2022,49.0,10.7,25.4
-EL64,2022,63.0,17.6,22.7
-EL65,2022,59.0,15.1,26.5
-ES11,2022,81.0,13.9,42.2
-ES12,2022,81.0,17.8,43.3
-ES13,2022,81.0,14.1,45.3
-ES21,2022,111.0,16.3,55.0
-ES22,2022,106.0,18.6,48.3
-ES23,2022,93.0,14.6,41.8
-ES24,2022,100.0,15.4,42.5
-ES30,2022,120.0,16.2,49.8
-ES41,2022,84.0,14.1,39.8
-ES42,2022,73.0,20.1,31.4
-ES43,2022,67.0,23.2,31.8
-ES51,2022,101.0,16.2,43.6
-ES52,2022,76.0,18.9,37.2
-ES53,2022,95.0,14.3,33.8
-ES61,2022,66.0,26.4,35.0
-ES62,2022,74.0,17.6,30.9
-ES63,2022,65.0,38.0,21.1
-ES64,2022,60.0,37.5,34.0
-ES70,2022,68.0,23.5,36.6
-FI19,2022,95.0,14.3,40.7
-FI1B,2022,135.0,16.6,50.5
-FI1C,2022,94.0,13.6,37.3
-FI1D,2022,93.0,14.5,38.2
-FI20,2022,109.0,,36.1
-FR10,2022,159.0,11.7,55.0
-FRB0,2022,80.0,13.4,34.9
-FRC1,2022,82.0,13.3,34.5
-FRC2,2022,74.0,12.9,36.8
-FRD1,2022,78.0,10.9,33.8
-FRD2,2022,85.0,13.6,35.4
-FRE1,2022,81.0,15.1,34.6
-FRE2,2022,75.0,16.7,31.8
-FRF1,2022,89.0,13.7,40.5
-FRF2,2022,89.0,14.1,28.9
-FRF3,2022,70.0,11.1,30.3
-FRG0,2022,87.0,11.3,40.0
-FRH0,2022,82.0,10.1,42.1
-FRI1,2022,85.0,13.3,41.2
-FRI2,2022,73.0,17.0,35.9
-FRI3,2022,80.0,14.2,36.8
-FRJ1,2022,74.0,15.6,37.6
-FRJ2,2022,86.0,9.0,44.3
-FRK1,2022,77.0,11.2,33.8
-FRK2,2022,96.0,13.1,46.8
-FRL0,2022,104.0,12.9,41.9
-FRM0,2022,76.0,6.1,32.2
-FRY1,2022,66.0,26.9,24.2
-FRY2,2022,69.0,16.5,29.3
-FRY3,2022,45.0,27.1,22.0
-FRY4,2022,63.0,25.5,24.0
-FRY5,2022,27.0,,
-HR02,2022,50.0,16.5,17.4
-HR03,2022,69.0,11.2,25.1
-HR05,2022,119.0,11.2,44.5
-HR06,2022,59.0,5.5,20.1
-HU11,2022,163.0,10.3,55.8
-HU12,2022,66.0,5.8,32.3
-HU21,2022,70.0,1.6,22.6
-HU22,2022,70.0,7.5,22.8
-HU23,2022,52.0,13.6,22.0
-HU31,2022,49.0,13.3,20.0
-HU32,2022,49.0,17.5,20.8
-HU33,2022,55.0,13.2,22.8
-IE04,2022,102.0,4.8,49.6
-IE05,2022,269.0,8.0,50.5
-IE06,2022,265.0,11.1,57.0
-IS00,2022,,7.3,42.9
-ITC1,2022,100.0,9.6,19.9
-ITC2,2022,127.0,5.4,19.6
-ITC3,2022,102.0,11.5,22.3
-ITC4,2022,133.0,6.4,21.8
-ITF1,2022,83.0,11.1,21.6
-ITF2,2022,72.0,15.1,20.2
-ITF3,2022,63.0,25.3,16.9
-ITF4,2022,64.0,14.1,16.2
-ITF5,2022,75.0,6.6,17.5
-ITF6,2022,57.0,16.7,16.6
-ITG1,2022,61.0,22.9,15.2
-ITG2,2022,71.0,14.7,17.9
-ITH1,2022,162.0,3.2,18.1
-ITH2,2022,127.0,4.9,21.4
-ITH3,2022,110.0,6.4,19.7
-ITH4,2022,104.0,7.7,21.3
-ITH5,2022,118.0,7.0,22.8
-ITI1,2022,103.0,8.0,21.5
-ITI2,2022,83.0,10.2,22.5
-ITI3,2022,90.0,8.3,22.6
-ITI4,2022,115.0,11.0,26.7
-LT01,2022,131.0,14.9,62.1
-LT02,2022,70.0,16.9,39.7
-LU00,2022,252.0,8.7,52.3
-LV00,2022,69.0,14.9,39.5
-ME00,2022,49.0,,
-MK00,2022,42.0,,
-MT00,2022,105.0,4.4,31.8
-NL11,2022,140.0,7.2,42.4
-NL12,2022,101.0,6.4,34.1
-NL13,2022,94.0,4.7,36.6
-NL21,2022,114.0,4.6,40.9
-NL22,2022,113.0,6.0,42.4
-NL23,2022,102.0,6.6,35.9
-NL31,2022,,6.0,56.1
-NL32,2022,175.0,5.8,51.6
-NL33,2022,,6.4,45.7
-NL34,2022,110.0,3.3,32.1
-NL35,2022,159.0,,
-NL36,2022,129.0,,
-NL41,2022,136.0,4.6,43.0
-NL42,2022,111.0,6.3,37.8
-NO02,2022,,,39.2
-NO06,2022,,2.5,47.3
-NO07,2022,,2.2,42.9
-NO08,2022,,9.8,51.9
-NO09,2022,,8.4,42.3
-NO0A,2022,,6.9,48.6
-PL21,2022,70.0,3.3,36.2
-PL22,2022,82.0,2.5,33.0
-PL41,2022,82.0,2.5,28.9
-PL42,2022,64.0,2.4,33.0
-PL43,2022,64.0,,28.8
-PL51,2022,84.0,15.5,36.1
-PL52,2022,64.0,2.7,28.0
-PL61,2022,65.0,4.8,28.2
-PL62,2022,56.0,3.4,28.0
-PL63,2022,77.0,2.6,38.6
-PL71,2022,74.0,3.8,30.4
-PL72,2022,57.0,4.7,30.9
-PL81,2022,55.0,5.6,29.8
-PL82,2022,55.0,5.4,30.6
-PL84,2022,61.0,2.5,34.7
-PL91,2022,152.0,3.1,57.7
-PL92,2022,74.0,4.0,29.7
-PT11,2022,66.0,6.1,25.8
-PT15,2022,84.0,7.1,25.3
-PT16,2022,,4.8,
-PT17,2022,,11.0,
-PT18,2022,,5.1,
-PT19,2022,68.0,4.1,30.6
-PT1A,2022,120.0,9.6,41.5
-PT1B,2022,53.0,14.2,32.4
-PT1C,2022,74.0,5.7,22.3
-PT1D,2022,60.0,5.8,22.7
-PT20,2022,67.0,7.6,16.6
-PT30,2022,82.0,6.8,22.2
-RO11,2022,67.0,8.3,18.2
-RO12,2022,66.0,22.4,18.9
-RO21,2022,44.0,11.3,14.0
-RO22,2022,58.0,16.7,14.0
-RO31,2022,57.0,16.7,13.7
-RO32,2022,179.0,3.1,42.2
-RO41,2022,59.0,22.4,17.6
-RO42,2022,74.0,3.8,18.3
-RS11,2022,74.0,14.7,42.7
-RS12,2022,44.0,13.3,21.4
-RS21,2022,30.0,10.5,18.8
-RS22,2022,34.0,14.4,21.0
-SE11,2022,155.0,23.8,57.4
-SE12,2022,99.0,31.5,45.4
-SE21,2022,99.0,21.8,40.3
-SE22,2022,98.0,28.1,49.2
-SE23,2022,111.0,21.0,48.8
-SE31,2022,94.0,25.1,38.3
-SE32,2022,104.0,23.2,42.9
-SE33,2022,122.0,19.5,43.8
-SI03,2022,72.0,10.5,35.7
-SI04,2022,107.0,7.7,45.1
-SK01,2022,150.0,2.3,47.0
-SK02,2022,64.0,16.6,24.8
-SK03,2022,61.0,41.6,26.6
-SK04,2022,53.0,63.2,28.2
-TR10,2022,111.0,9.4,28.2
-TR21,2022,85.0,7.2,20.4
-TR22,2022,62.0,5.1,23.4
-TR31,2022,84.0,13.0,27.7
-TR32,2022,60.0,7.2,24.6
-TR33,2022,57.0,4.8,18.0
-TR41,2022,74.0,7.8,25.4
-TR42,2022,90.0,9.3,21.9
-TR51,2022,89.0,12.3,33.6
-TR52,2022,55.0,5.5,19.0
-TR61,2022,68.0,8.0,26.7
-TR62,2022,57.0,11.2,21.4
-TR63,2022,45.0,12.2,19.9
-TR71,2022,51.0,7.3,20.1
-TR72,2022,50.0,8.0,22.3
-TR81,2022,53.0,9.8,19.7
-TR82,2022,48.0,5.3,20.0
-TR83,2022,40.0,5.4,19.1
-TR90,2022,38.0,5.8,21.1
-TRA1,2022,40.0,6.7,20.6
-TRA2,2022,28.0,12.3,13.6
-TRB1,2022,37.0,6.0,21.6
-TRB2,2022,25.0,16.6,13.1
-TRC1,2022,50.0,8.8,18.3
-TRC2,2022,26.0,10.8,15.1
-TRC3,2022,35.0,16.7,14.9
+region_code,year,gdp_pc_pps,unemployment_rate,tertiary_share_25_64,pop_density
+AL01,2022,,,,74.0
+AL02,2022,,,,238.8
+AL03,2022,,,,64.9
+AT11,2022,86.0,3.9,31.4,79.2
+AT12,2022,101.0,13.2,34.1,90.4
+AT13,2022,140.0,20.7,45.6,4941.5
+AT21,2022,110.0,3.9,33.3,60.5
+AT22,2022,114.0,9.9,32.2,77.5
+AT31,2022,127.0,6.6,32.4,129.2
+AT32,2022,149.0,2.8,33.2,80.1
+AT33,2022,127.0,2.8,31.9,61.4
+AT34,2022,147.0,3.0,29.9,159.6
+BE10,2022,194.0,23.8,53.5,7660.0
+BE21,2022,141.0,8.0,47.1,678.8
+BE22,2022,96.0,7.7,43.7,373.8
+BE23,2022,113.0,5.0,49.6,523.4
+BE24,2022,134.0,12.9,51.6,558.7
+BE25,2022,116.0,5.6,39.8,386.2
+BE31,2022,145.0,8.2,60.7,375.8
+BE32,2022,73.0,20.5,34.2,357.3
+BE33,2022,86.0,14.6,41.5,291.6
+BE34,2022,74.0,4.7,42.1,66.4
+BE35,2022,79.0,16.2,43.3,137.8
+BG31,2022,41.0,23.8,18.8,35.9
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