feat: add municipality info

CHANGELOG.md

@@ -2,6 +2,7 @@
 
 **Under development**
 
+- feat: add municipality information to households and activities
 - chore: update to `eqasim-java` commit `ece4932`
 - feat: vehicles and vehicle types are now always generated
 - feat: read vehicles data from zip files

synthesis/output.py

@@ -62,23 +62,6 @@ def execute(context):
     output_prefix = context.config("output_prefix")
     output_formats = context.config("output_formats")
 
-    # Prepare households
-    df_households = context.stage("synthesis.population.enriched").rename(
-        columns = { "household_income": "income" }
-    ).drop_duplicates("household_id")
-
-    df_households = df_households[[
-        "household_id",
-        "car_availability", "bike_availability",
-        "number_of_vehicles", "number_of_bikes",
-        "income",
-        "census_household_id"
-    ]]
-    if "csv" in output_formats:
-        df_households.to_csv("%s/%shouseholds.csv" % (output_path, output_prefix), sep = ";", index = None, lineterminator = "\n")
-    if "parquet" in output_formats:
-        df_households.to_parquet("%s/%shouseholds.parquet" % (output_path, output_prefix))
-
     # Prepare persons
     df_persons = context.stage("synthesis.population.enriched").rename(
         columns = { "has_license": "has_driving_license" }
@@ -106,9 +89,29 @@ def execute(context):
     df_activities["preceding_trip_index"] = df_activities["following_trip_index"].shift(1)
     df_activities.loc[df_activities["is_first"], "preceding_trip_index"] = -1
     df_activities["preceding_trip_index"] = df_activities["preceding_trip_index"].astype(int)
+    # Prepare spatial data sets
+    df_locations = context.stage("synthesis.population.spatial.locations")[[
+        "person_id",  "iris_id", "commune_id","departement_id","region_id","activity_index", "geometry"
+    ]]
 
+    df_activities = pd.merge(df_activities, df_locations[[
+        "person_id", "iris_id", "commune_id","departement_id","region_id","activity_index", "geometry"
+    ]], how = "left", on = ["person_id", "activity_index"])
+
+    # Prepare spatial activities
+    df_spatial = gpd.GeoDataFrame(df_activities[[
+            "person_id", "household_id", "activity_index",
+            "iris_id", "commune_id","departement_id","region_id",
+            "preceding_trip_index", "following_trip_index",
+            "purpose", "start_time", "end_time",
+            "is_first", "is_last", "geometry"
+        ]], crs = df_locations.crs)
+    df_spatial = df_spatial.astype({'purpose': 'str', "departement_id": 'str'})
+
+    # Write activities
     df_activities = df_activities[[
         "person_id", "household_id", "activity_index",
+        "iris_id", "commune_id","departement_id","region_id",
         "preceding_trip_index", "following_trip_index",
         "purpose", "start_time", "end_time",
         "is_first", "is_last"
@@ -119,6 +122,25 @@ def execute(context):
     if "parquet" in output_formats:
         df_activities.to_parquet("%s/%sactivities.parquet" % (output_path, output_prefix))
 
+    # Prepare households
+    df_households = context.stage("synthesis.population.enriched").rename(
+        columns = { "household_income": "income" }
+    ).drop_duplicates("household_id")
+
+    df_households = pd.merge(df_households,df_activities[df_activities["purpose"] == "home"][["household_id",
+        "iris_id", "commune_id","departement_id","region_id"]].drop_duplicates("household_id"),how="left")
+    df_households = df_households[[
+        "household_id","iris_id", "commune_id", "departement_id","region_id",
+        "car_availability", "bike_availability",
+        "number_of_vehicles", "number_of_bikes",
+        "income",
+        "census_household_id"
+    ]]
+    if "csv" in output_formats:
+        df_households.to_csv("%s/%shouseholds.csv" % (output_path, output_prefix), sep = ";", index = None, lineterminator = "\n")
+    if "parquet" in output_formats:
+        df_households.to_parquet("%s/%shouseholds.parquet" % (output_path, output_prefix))
+
     # Prepare trips
     df_trips = context.stage("synthesis.population.trips").rename(
         columns = {
@@ -170,18 +192,7 @@ def execute(context):
         df_vehicle_types.to_parquet("%s/%svehicle_types.parquet" % (output_path, output_prefix))
         df_vehicles.to_parquet("%s/%svehicles.parquet" % (output_path, output_prefix))
 
-    # Prepare spatial data sets
-    df_locations = context.stage("synthesis.population.spatial.locations")[[
-        "person_id", "activity_index", "geometry"
-    ]]
-
-    df_activities = pd.merge(df_activities, df_locations[[
-        "person_id", "activity_index", "geometry"
-    ]], how = "left", on = ["person_id", "activity_index"])
 
-    # Write spatial activities
-    df_spatial = gpd.GeoDataFrame(df_activities, crs = df_locations.crs)
-    df_spatial["purpose"] = df_spatial["purpose"].astype(str)
     if "gpkg" in output_formats:
         path = "%s/%sactivities.gpkg" % (output_path, output_prefix)
         df_spatial.to_file(path, driver = "GPKG")
@@ -194,7 +205,7 @@ def execute(context):
     df_spatial_homes = df_spatial[
         df_spatial["purpose"] == "home"
     ].drop_duplicates("household_id")[[
-        "household_id", "geometry"
+        "household_id","iris_id", "commune_id","departement_id","region_id", "geometry"
     ]]
     if "gpkg" in output_formats:
         path = "%s/%shomes.gpkg" % (output_path, output_prefix)

synthesis/population/spatial/locations.py

@@ -9,6 +9,7 @@ def configure(context):
 
     context.stage("synthesis.population.activities")
     context.stage("synthesis.population.sampled")
+    context.stage("data.spatial.iris")
 
 def execute(context):
     df_home = context.stage("synthesis.population.spatial.home.locations")
@@ -57,4 +58,10 @@ def execute(context):
     assert not df_locations["geometry"].isna().any()
     df_locations = gpd.GeoDataFrame(df_locations, crs = df_home.crs)
 
+    # add municipalities
+    df_iris = context.stage("data.spatial.iris")
+    df_iris = gpd.GeoDataFrame(df_iris, crs = df_home.crs)
+
+    df_locations = gpd.sjoin(df_locations,df_iris,how="left")
+
     return df_locations

tests/test_determinism.py

@@ -68,18 +68,18 @@ def _test_determinism(index, data_path, tmpdir):
     synpp.run(stages, config, working_directory = cache_path)
 
     REFERENCE_CSV_HASHES = {
-        "ile_de_france_activities.csv":     "e520003e1876a9542ff1a955a6efcfdc",
-        "ile_de_france_households.csv":     "709ce7ded8a2487e6691d4fb3374754b",
+        "ile_de_france_activities.csv":     "53c44fb4026d2037729ee8ff1c8fb93f",
+        "ile_de_france_households.csv":     "ca2a29ef13467326f937638f1ff8be1a",
         "ile_de_france_persons.csv":        "ddbe9b418c915b14e888b54efbdf9b1e",
         "ile_de_france_trips.csv":          "6c5f3427e41e683da768eeb53796a806",
         "ile_de_france_vehicle_types.csv":  "00bee1ea6d7bc9af43ae6c7101dd75da",
         "ile_de_france_vehicles.csv":       "3567b0f29e51d521b13d91c82c77cecb",
     }
 
     REFERENCE_GPKG_HASHES = {
-        "ile_de_france_activities.gpkg":    "9cf9a5fd8927c709927f7a940f86efbf",
+        "ile_de_france_activities.gpkg":    "884eec1fd0c29904284eb4362ff89be1",
         "ile_de_france_commutes.gpkg":      "5a4180390a69349cc655c07c5671e8d3",
-        "ile_de_france_homes.gpkg":         "033d1aa7a5350579cbd5e8213b9736f2",
+        "ile_de_france_homes.gpkg":         "a85e973f0e2f51031cd60170d351845e",
         "ile_de_france_trips.gpkg":         "d0aec4033cfc184bf1b91ae13a537ef8",
     }