add household equivalent_size and corresponding calculations

matsim/scenariogen/data/__init__.py

@@ -355,6 +355,12 @@ class Household:
     location: str
     zone: str = pd.NA
     """ A detailed zone, which can be more accurate than location. """
+    equivalent_size: float = pd.NA
+    """ Weighted household size, considering the number of persons > 14 years. 
+    May be used to calculate equivalized income for each person.
+
+    See https://de.wikipedia.org/wiki/%C3%84quivalenzeinkommen      
+     """
     income: float = pd.NA
     geom: object = pd.NA
 

matsim/scenariogen/data/formats/srv.py

@@ -6,6 +6,7 @@
 import pandas as pd
 
 from .. import *
+from ..preparation import equivalent_household_size
 
 # Has households, persons and trips
 INPUT_FILES = 3
@@ -79,6 +80,8 @@ def convert(data: tuple, regio=None):
             continue
 
         hh_id = str(int(h.HHNR))
+        hh_persons = ps[ps.hh_id == hh_id]
+
         hhs.append(
             Household(
                 hh_id,
@@ -89,11 +92,12 @@ def convert(data: tuple, regio=None):
                 pint(h.V_ANZ_MOT125 + h.V_ANZ_MOPMOT + h.V_ANZ_SONST),
                 SrV2018.parking_position(h.V_STELLPL1),
                 SrV2018.economic_status(h.E_OEK_STATUS if "E_OEK_STATUS" in hh.keys() else -1, h.V_EINK,
-                                        ps[ps.hh_id == hh_id]),
+                                        hh_persons),
                 SrV2018.household_type(h.E_HHTYP),
                 SrV2018.region_type(h, regio, random_state),
                 h.ST_CODE_NAME,
                 zone=SrV2018.parse_zone(h),
+                equivalent_size=equivalent_household_size(hh_persons),
                 income=SrV2018.income(h.V_EINK),
             )
         )
@@ -205,10 +209,7 @@ def economic_status(status, eink, persons):
         if eink == 1 or eink == 2:
             return EconomicStatus.VERY_LOW
 
-        children = (persons.age < 14).sum()
-        rest = len(persons) - children - 1
-
-        w = 0.3 * children + 1 + 0.5 * rest
+        w = equivalent_household_size(persons)
 
         if eink == 3:
             if w < 1.3:
@@ -418,26 +419,30 @@ def region_type(d, regio, random_state):
 
     @staticmethod
     def income(x):
+        # Original groups are
+        # 0, 500, 900, 1500, 2000, 2600, 3000, 3600, 4600, 5600
+
+        # Return the mean between each grop
         if x == 1:
-            return 0
+            return 250
         elif x == 2:
-            return 500
+            return 700
         elif x == 3:
-            return 900
+            return 1200
         elif x == 4:
-            return 1500
+            return 1750
         elif x == 5:
-            return 2000
+            return 2300
         elif x == 6:
-            return 2600
+            return 2800
         elif x == 7:
-            return 3000
+            return 3300
         elif x == 8:
-            return 3600
+            return 4100
         elif x == 9:
-            return 4600
+            return 5100
         elif x == 10:
-            return 5600
+            return 6100
 
         return -1
 

matsim/scenariogen/data/preparation.py

@@ -16,15 +16,6 @@ def prepare_persons(hh, pp, tt, augment=5, max_hh_size=5, core_weekday=False, re
     """
     df = pp.join(hh, on="hh_id", lsuffix="hh_")
 
-    # Replace unknown income group
-    fill(df, "income", -1)
-
-    # Replace unknown economic status
-    df["economic_status"] = df.apply(
-        lambda x: income_to_economic_status(x.income, df[df.hh_id == x.hh_id])
-        if x.economic_status == EconomicStatus.UNKNOWN else x.economic_status, axis=1
-    )
-
     # Augment data using p_weight
     if augment > 1:
         df = augment_persons(df, augment)
@@ -143,12 +134,35 @@ def augment_persons(pp, factor=1, permute_age=0.5):
     # Filter invalid options
     return duplicated[check_age_employment(None, duplicated)]
 
+def equivalent_household_size(persons):
+    """ Calculate equivalent household size, see https://stat.fi/meta/kas/ekvivalentti_tu_en.html
+
+    :param persons: persons of one household
+    """
+    children = (persons.age < 14).sum()
+    rest = len(persons) - children - 1
+
+    w = 0.3 * children + 1 + 0.5 * rest
+
+    return w
 
-def income_to_economic_status(income, persons):
+def compute_economic_status(df):
+    """ Compute missing economic status value.
+
+    :param df: joined table of persons and households
+    """
+
+    # Replace unknown economic status
+    df["economic_status"] = df.apply(
+        lambda x: income_to_economic_status(x.income, x.equivalent_size)
+        if x.economic_status == EconomicStatus.UNKNOWN else x.economic_status, axis=1
+    )
+
+def income_to_economic_status(income, w):
     """ Convert income to economic status
 
      :param income: income in Euro
-     :param persons: persons table
+     :param w: equivalent_size
      """
 
     if income < 0:
@@ -157,11 +171,6 @@ def income_to_economic_status(income, persons):
     # Calculated according to Srv 2018
     # https://tu-dresden.de/bu/verkehr/ivs/srv/ressourcen/dateien/SrV2018_Tabellenbericht_Oberzentren_500TEW-_flach.pdf?lang=de
 
-    children = (persons.age < 14).sum()
-    rest = len(persons) - children - 1
-
-    w = 0.3 * children + 1 + 0.5 * rest
-
     if income < 1500:
         if w < 1.3:
             return EconomicStatus.LOW