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thornoe · Jan 22, 2025 · 10cb892 · 10cb892
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diff --git a/gis/output/all_VP_shore length_stats.tex b/gis/output/all_VP_shore length_stats.tex
@@ -3,9 +3,9 @@
  & n & mean & std & min & 25\% & 50\% & 75\% & max \\
 \midrule
 Coastline & 108 & 64.05 & 57.70 & 0.39 & 18.28 & 47.00 & 88.63 & 288.35 \\
-Shore length of lakes in VP3 & 90 & 37.14 & 62.58 & 0.61 & 4.18 & 11.07 & 49.02 & 436.75 \\
-Shore length of streams in VP3 & 101 & 367.84 & 580.51 & 2.19 & 40.21 & 160.83 & 377.16 & 3664.68 \\
-Shore length of all water bodies in VP3 & 108 & 439.00 & 632.11 & 5.74 & 67.82 & 212.27 & 530.82 & 4112.75 \\
+Lake shores in RBMP3 & 90 & 37.14 & 62.58 & 0.61 & 4.18 & 11.07 & 49.02 & 436.75 \\
+Stream shores in RBMP3 & 101 & 367.84 & 580.51 & 2.19 & 40.21 & 160.83 & 377.16 & 3664.68 \\
+All shores in RBMP3 & 108 & 439.00 & 632.11 & 5.74 & 67.82 & 212.27 & 530.82 & 4112.75 \\
 Proportion of arable land & 108 & 0.64 & 0.16 & 0.10 & 0.58 & 0.66 & 0.72 & 1.00 \\
 \bottomrule
 \end{tabular}
diff --git a/gis/output/all_demographics_N.pdf b/gis/output/all_demographics_N.pdf
diff --git a/gis/output/all_demographics_age.pdf b/gis/output/all_demographics_age.pdf
diff --git a/gis/output/all_demographics_y.pdf b/gis/output/all_demographics_y.pdf
diff --git a/gis/sandbox.py b/gis/sandbox.py
@@ -20,8 +20,11 @@
 # Import Operation System (os)
 import os
 
-# Import Pandas and ticker.FuncFormatter
+# Import pyplot, NumPy, Pandas, seaborn, and matplotlib.ticker.FuncFormatter
+import matplotlib.pyplot as plt
+import numpy as np
 import pandas as pd
+import seaborn as sns
 from matplotlib.ticker import FuncFormatter
 
 ########################################################################################
@@ -436,9 +439,9 @@ def y_f(x, pos):
 )
 SL.columns = [
     "Coastline",
-    "Shore length of lakes in VP3",
-    "Shore length of streams in VP3",
-    "Shore length of all water bodies in VP3",
+    "Lake shores in RBMP3",
+    "Stream shores in RBMP3",
+    "All shores in RBMP3",
 ]
 PAL = pd.read_excel(
     "data\\" + data["shared"][2], index_col=0
@@ -461,7 +464,7 @@ def y_f(x, pos):
 # Box plot for mean real income
 plt.figure()
 sns.boxplot(x="t", y="y", data=Dem, palette=["#CCBB44"])
-plt.title("Distribution of mean real household income over catchment areas")
+# plt.title("Distribution of mean real household income over catchment areas")
 plt.xlabel("")  #  omit x-axis label
 plt.ylabel("Mean real household income (100,000 DKK, 2018 prices)")
 plt.xticks(rotation=90)
@@ -473,7 +476,7 @@ def y_f(x, pos):
 Dem["N"] = Dem["N"] / 100000  #  convert number of households to 100,000
 plt.figure()
 sns.boxplot(x="t", y="N", data=Dem, palette=["#AA3377"])
-plt.title("Distribution of households over catchment areas")
+# plt.title("Distribution of number of households over catchment areas")
 plt.xlabel("")  #  omit x-axis label
 plt.ylabel("Number of households (100,000)")
 plt.xticks(rotation=90)
@@ -484,7 +487,7 @@ def y_f(x, pos):
 # Box plot for mean age
 plt.figure()
 sns.boxplot(x="t", y="age", data=Dem, palette=["#EE6677"])
-plt.title("Distribution of mean age over catchment areas")
+# plt.title("Distribution of mean age over catchment areas")
 plt.xlabel("")  #  omit x-axis label
 plt.ylabel("Mean age")
 plt.yticks([35, 40, 45, 50, 55])  # set specific tick positions on y-axis
@@ -495,4 +498,3 @@ def y_f(x, pos):
 plt.close()  #  close plot to free up memory
 n = Dem.groupby("t")["D age"].sum()  #  number of catchment areas w. mean age > 45 years
 pd.DataFrame({"n": n, "s (%)": 100 * n / 108})  #  number and share: mean age > 45 years
-pd.DataFrame({"n": n, "s (%)": 100 * n / 108})  #  number and share: mean age > 45 years