Merge pull request #43 from TomorrowsCities/redefine_population_displacement

tomorrowcities/backend/engine.py

@@ -747,7 +747,12 @@ def compute(gdf_landuse, gdf_buildings, df_household, df_individual,gdf_intensit
     return bld_hazard
 
 
-def calculate_metrics(gdf_buildings, df_household, df_individual, infra, hazard_type, policies=[],capacity=1.0):
+def calculate_metrics(gdf_buildings, df_household, df_individual, infra, hazard_type, population_displacement_consensus, policies=[],capacity=1.0):
+    # Very handy temporary attributes showin if an individual is associated with a facility 
+    # and lost access to facility
+    df_individual['has_facility'] = df_individual['indivfacid'].apply(lambda x: x > -1)
+    df_individual['lost_facility_access'] = df_individual.apply(lambda x: x['has_facility']  and not x['facility_access'], axis=1)
+
     # only use necessary columns
     bld_hazard = gdf_buildings[['bldid','ds','expstr','occupancy','storeys',
                                 'code_level','material','nhouse','residents','hospital_access','has_power','casualty']]
@@ -783,7 +788,7 @@ def calculate_metrics(gdf_buildings, df_household, df_individual, infra, hazard_
         .merge(df_workers[['individ','ds','has_power']].rename(columns={'ds':'ds_workplace','has_power':'workplace_power'}),on='individ', how='left')\
         .merge(df_students[['individ','ds','has_power']].rename(columns={'ds':'ds_school','has_power':'school_power'}), on='individ', how='left')\
         .merge(df_indiv_household[['individ','ds','hospital_access','hospital_has_power']].rename(columns={'ds':'ds_household'}), on='individ',how='left')\
-        .merge(df_household[['hhid','bldid']],on='hhid',how='left')
+        .merge(df_household[['hhid','bldid','has_power']].rename(columns={'has_power':'household_power'}),on='hhid',how='left')
 
     DS_NO = 0
     DS_SLIGHT = 1
@@ -816,7 +821,7 @@ def calculate_metrics(gdf_buildings, df_household, df_individual, infra, hazard_
     if 'power' in infra:
         metric1_index = (metric1_index) | (df_workers['has_power'] == False)
     if 'road' in infra:
-        metric1_index = (metric1_index) | (df_workers['facility_access'] == False)
+        metric1_index = (metric1_index) | (df_workers['lost_facility_access'] == True)
     df_workers_per_building = df_workers[metric1_index][['individ','hhid','ds']].merge(
         df_household[['hhid','bldid']],on='hhid',how='left').groupby(
             'bldid',as_index=False).agg({'individ':'count'})
@@ -832,7 +837,7 @@ def calculate_metrics(gdf_buildings, df_household, df_individual, infra, hazard_
     if 'power' in infra:
         metric2_index = (metric2_index) | (df_students['has_power'] == False)
     if 'road' in infra:
-        metric2_index = (metric2_index) | (df_students['facility_access'] == False)
+        metric2_index = (metric2_index) | (df_students['lost_facility_access'] == True)
     df_students_per_building = df_students[metric2_index][['individ','hhid','ds']].merge(
         df_household[['hhid','bldid']],on='hhid',how='left').groupby(
             'bldid',as_index=False).agg({'individ':'count'})
@@ -895,17 +900,20 @@ def calculate_metrics(gdf_buildings, df_household, df_individual, infra, hazard_
 
     # metric 7 the number of displaced individuals in each building
     # more info: an individual is displaced if at least of the conditions below hold
-    metric7_index = (df_displaced_indiv['ds_household'] > thresholds['metric6']) |\
-                    (df_displaced_indiv['ds_school'] > thresholds['metric2']) |\
-                    (df_displaced_indiv['ds_workplace'] > thresholds['metric1']) |\
-                    (df_displaced_indiv['ds_hospital'] > thresholds['metric4']) 
+    print('population_displacement_consensus', population_displacement_consensus)
+    # direct damage
+    metric7_index = (df_displaced_indiv[['ds_household','ds_school','ds_workplace','ds_hospital']] \
+        > [thresholds['metric6'],thresholds['metric2'],thresholds['metric1'],thresholds['metric4']]).sum(axis=1) >= population_displacement_consensus
+    # inaccebility
     if 'road' in infra:
-        metric7_index = (metric7_index) | (df_displaced_indiv['hospital_access'] == False)
-        metric7_index = (metric7_index) | (df_displaced_indiv['facility_access'] == False)
+        inaccesibility_index = (df_displaced_indiv[['hospital_access','lost_facility_access']] \
+            == [False,True]).sum(axis=1) >= population_displacement_consensus
+        metric7_index = (metric7_index) | (inaccesibility_index)
+    # power loss
     if 'power' in infra:
-        metric7_index = (metric7_index) | (df_displaced_indiv['hospital_has_power'] == False)
-        metric7_index = (metric7_index) | (df_displaced_indiv['workplace_power'] == False)
-        metric7_index = (metric7_index) | (df_displaced_indiv['school_power'] == False)
+        power_loss_index = (df_displaced_indiv[['household_power','hospital_has_power','workplace_power','school_power']] \
+            == [False,False,False,False]).sum(axis=1) >= population_displacement_consensus
+        metric7_index = (metric7_index) | (power_loss_index)
     df_disp_per_bld = df_displaced_indiv[metric7_index]\
                                             .groupby('bldid',as_index=False)\
                                             .agg({'individ':'count'})

tomorrowcities/pages/engine.py

@@ -22,7 +22,8 @@ import logging, sys
 import pickle
 import datetime
 from .settings import storage, landslide_max_trials, revive_storage
-from .settings import threshold_flood, threshold_flood_distance, threshold_road_water_height, threshold_culvert_water_height, preserve_edge_directions
+from .settings import threshold_flood, threshold_flood_distance, threshold_road_water_height, threshold_culvert_water_height, preserve_edge_directions,\
+                      population_displacement_consensus
 from ..backend.engine import compute, compute_power_infra, compute_road_infra, calculate_metrics, generate_exposure
 from ..backend.utils import building_preprocess, identity_preprocess, ParameterFile
 from .utilities import S3FileBrowser, extension_list, extension_list_w_dots
@@ -1229,7 +1230,8 @@ def ExecutePanel():
                 # capacity = 1
             computed_metrics, df_metrics = calculate_metrics(buildings, household, 
                                                              individual, layers.value['infra'].value,
-                                                             layers.value['hazard'].value, policies=policies,capacity=1)
+                                                             layers.value['hazard'].value, population_displacement_consensus.value, 
+                                                             policies=policies,capacity=1)
             print(computed_metrics)
             for metric in df_metrics.keys():
                 buildings[metric] = list(df_metrics[metric][metric])

tomorrowcities/pages/settings.py

@@ -24,6 +24,7 @@ threshold_flood_distance = solara.reactive(10)
 threshold_road_water_height = solara.reactive(0.3) 
 threshold_culvert_water_height = solara.reactive(1.5)
 preserve_edge_directions = solara.reactive(False)
+population_displacement_consensus = solara.reactive(2)
 
 def storage_control(aws_access_key_id: str, aws_secret_access_key: str, region_name: str, bucket_name: str):
     storage.value = S3Storage(aws_access_key_id, aws_secret_access_key, region_name, bucket_name)
@@ -106,6 +107,10 @@ def Page(name: Optional[str] = None, page: int = 0, page_size=100):
     if err_message != '':
         solara.Error(err_message)
 
+    with solara.Card(title='Metric-related Parameters',
+                     subtitle='Minimum number of direct damage, inaccessibility or power lost should be observed to claim a population displacement.'):
+        solara.Select(label='population displacement consensus', values=[1,2,3,4], value=population_displacement_consensus)
+
     with solara.Card(title='Connectivity Parameters',subtitle='Parameters effecting connectivity analysis in road/power networks'):
         solara.Checkbox(label='Preserve directions in graph edges', value=preserve_edge_directions)