Added implementation capacity score

tomorrowcities/backend/engine.py

@@ -334,9 +334,9 @@ def compute(gdf_landuse, gdf_buildings, df_household, df_individual,gdf_intensit
     return bld_hazard
 
 
-def calculate_metrics(gdf_buildings, df_household, df_individual, hazard_type, policies=[]):
+def calculate_metrics(gdf_buildings, df_household, df_individual, hazard_type, policies=[],capacity=1.0):
     # only use necessary columns
-    bld_hazard = gdf_buildings[['bldid','ds','expstr']]
+    bld_hazard = gdf_buildings[['bldid','ds','expstr','nhouse','residents']]
     bld_hazard[['material','code_level','storeys','occupancy']] =  \
         bld_hazard['expstr'].str.split('+',expand=True).copy()
     #bld_hazard['occupancy'] = bld_hazard['occupancy'].astype('category')
@@ -400,8 +400,10 @@ def calculate_metrics(gdf_buildings, df_household, df_individual, hazard_type, p
         df_household[['hhid','bldid']],on='hhid',how='left').groupby(
             'bldid',as_index=False).agg({'individ':'count'})
 
-    df_metric1 = bld_hazard.merge(df_workers_per_building,how='left',left_on='bldid',right_on = 'bldid')[['bldid','individ']]
+    df_metric1 = bld_hazard.merge(df_workers_per_building,how='left',left_on='bldid',right_on = 'bldid')[['bldid','residents','individ']]
     df_metric1.rename(columns={'individ':'metric1'}, inplace=True)
+    df_metric1['metric1'] = (df_metric1['metric1'].fillna(0) * capacity).astype(int)
+    df_metric1['metric1'] = df_metric1[['residents','metric1']].min(axis=1)
     df_metric1['metric1'] = df_metric1['metric1'].fillna(0).astype(int)
 
     # metric 2 number of students in each building with no access to schools
@@ -409,40 +411,50 @@ def calculate_metrics(gdf_buildings, df_household, df_individual, hazard_type, p
         df_household[['hhid','bldid']],on='hhid',how='left').groupby(
             'bldid',as_index=False).agg({'individ':'count'})
 
-    df_metric2 = bld_hazard.merge(df_students_per_building,how='left',left_on='bldid',right_on = 'bldid')[['bldid','individ']]
+    df_metric2 = bld_hazard.merge(df_students_per_building,how='left',left_on='bldid',right_on = 'bldid')[['bldid','residents','individ']]
     df_metric2.rename(columns={'individ':'metric2'}, inplace=True)
+    df_metric2['metric2'] = (df_metric2['metric2'].fillna(0) * capacity).astype(int)
+    df_metric2['metric2'] = df_metric2[['residents','metric2']].min(axis=1)
     df_metric2['metric2'] = df_metric2['metric2'].fillna(0).astype(int)
 
     # metric 3 number of households in each building with no access to hospitals
     df_hospitals_per_household = df_hospitals[df_hospitals['ds'] > thresholds['metric3']].groupby(
         'bldid',as_index=False).agg({'hhid':'count'})
 
-    df_metric3 = bld_hazard.merge(df_hospitals_per_household,how='left',left_on='bldid',right_on='bldid')[['bldid','hhid']]
+    df_metric3 = bld_hazard.merge(df_hospitals_per_household,how='left',left_on='bldid',right_on='bldid')[['bldid','nhouse','hhid']]
     df_metric3.rename(columns={'hhid':'metric3'}, inplace=True)
+    df_metric3['metric3'] = (df_metric3['metric3'].fillna(0) * capacity).astype(int)
+    df_metric3['metric3'] = df_metric3[['nhouse','metric3']].min(axis=1)
     df_metric3['metric3'] = df_metric3['metric3'].fillna(0).astype(int)
 
     # metric 4 number of individuals in each building with no access to hospitals
     df_hospitals_per_individual = df_hospitals[df_hospitals['ds'] > thresholds['metric4']].groupby(
         'bldid',as_index=False).agg({'nind':'sum'})
 
-    df_metric4 = bld_hazard.merge(df_hospitals_per_individual,how='left',left_on='bldid',right_on='bldid')[['bldid','nind']]
+    df_metric4 = bld_hazard.merge(df_hospitals_per_individual,how='left',left_on='bldid',right_on='bldid')[['bldid','residents','nind']]
     df_metric4.rename(columns={'nind':'metric4'}, inplace=True)
+    df_metric4['metric4'] = (df_metric4['metric4'].fillna(0) * capacity).astype(int)
+    df_metric4['metric4'] = df_metric4[['residents','metric4']].min(axis=1)
     df_metric4['metric4'] = df_metric4['metric4'].fillna(0).astype(int)
 
     # metric 5 number of damaged households in each building
     df_homeless_households = df_household_bld[df_household_bld['ds'] > thresholds['metric5']].groupby(
         'bldid',as_index=False).agg({'hhid':'count'})
 
-    df_metric5 = bld_hazard.merge(df_homeless_households,how='left',left_on='bldid',right_on='bldid')[['bldid','hhid']]
+    df_metric5 = bld_hazard.merge(df_homeless_households,how='left',left_on='bldid',right_on='bldid')[['bldid','nhouse','hhid']]
     df_metric5.rename(columns={'hhid':'metric5'}, inplace=True)
+    df_metric5['metric5'] = (df_metric5['metric5'].fillna(0) * capacity).astype(int)
+    df_metric5['metric5'] = df_metric5[['nhouse','metric5']].min(axis=1)
     df_metric5['metric5'] = df_metric5['metric5'].fillna(0).astype(int)
 
     # metric 6 number of homeless individuals in each building
     df_homeless_individuals = df_household_bld[df_household_bld['ds'] > thresholds['metric6']].groupby(
         'bldid',as_index=False).agg({'nind':'sum'})
 
-    df_metric6 = bld_hazard.merge(df_homeless_individuals,how='left',left_on='bldid',right_on='bldid')[['bldid','nind']]
+    df_metric6 = bld_hazard.merge(df_homeless_individuals,how='left',left_on='bldid',right_on='bldid')[['bldid','residents','nind']]
     df_metric6.rename(columns={'nind':'metric6'}, inplace=True)
+    df_metric6['metric6'] = (df_metric6['metric6'].fillna(0) * capacity).astype(int)
+    df_metric6['metric6'] = df_metric6[['residents','metric6']].min(axis=1)
     df_metric6['metric6'] = df_metric6['metric6'].fillna(0).astype(int)
 
     # metric 7 the number of displaced individuals in each building
@@ -454,8 +466,10 @@ def calculate_metrics(gdf_buildings, df_household, df_individual, hazard_type, p
                                             .groupby('bldid',as_index=False)\
                                             .agg({'individ':'count'})
 
-    df_metric7 = bld_hazard.merge(df_disp_per_bld,how='left',left_on='bldid',right_on='bldid')[['bldid','individ']]
+    df_metric7 = bld_hazard.merge(df_disp_per_bld,how='left',left_on='bldid',right_on='bldid')[['bldid','residents','individ']]
     df_metric7.rename(columns={'individ':'metric7'}, inplace=True)
+    df_metric7['metric7'] = (df_metric7['metric7'].fillna(0) * capacity).astype(int)
+    df_metric7['metric7'] = df_metric7[['residents','metric7']].min(axis=1)
     df_metric7['metric7'] = df_metric7['metric7'].fillna(0).astype(int)
 
 

tomorrowcities/content/articles/policies.md

@@ -46,13 +46,13 @@ the damage curve value for special facilities by increasing the height of the st
 ### Policy 5: Shelter Law - All low-income and informal settlements should have physical and free access to community centres and shelters
 Expected outcome: Social Vulnerability reduction - reducing the effects of displacement and other issues such as food insecurity. 
 Concerns: Implementation/Enforcement – evidence that some shelters remain unfunded or little effective despite their physical existence 
-In implementation, we filter residential buildings in low income regions, and slight increase the height levels.
+In implementation, we filter residential buildings in low income regions, and slightly increase the height levels.
 
 ### Policy 6: Funding community-based networks in low-income areas (holistic approaches)
 Expected outcome: Social Vulnerability reduction - meeting diverse emergency needs
 Concerns: Implementation/Enforcement – funds could not be available as soon as hazards hit, decreasing their effectivity. Not certain how groups would allocate funding. 
 Implementation: filter non 
-In implementation, we filter commercial buildings in low income regions, and slight increase the height levels.
+In implementation, we filter commercial buildings in low income regions, and slightly increase the height levels.
 
 ### Policy 7: Urban farming programs 
 Expected outcome: Exposure Reduction - reserve of % of land to urban farming (by residents) in hazard-prone areas. Social Vulnerability reduction due to improved food security. 

tomorrowcities/content/articles/welcome.md

@@ -15,6 +15,8 @@ category:
 TCDSE is a web application designed to conduct computational tasks to generate information needed for decision mechanisms in designing future cities. The web application, which will be referred as TCDSE for short, contains a computational engine capable of executing several hazard scenarios on different exposure datasets and infrastructures. 
 
 ## What is New?
+Implementation Capacity Score is added. If medium or low is selected, then building-level metrics is increased by 25% and 50%, respectively. If high is selected, there is no change in the metrics.
+
 ### New policies
 * Eight new policies are added. See their definitions [here](/docs/policies)
 * New info related to flood damage assessment is added [here](/docs/flood)

tomorrowcities/pages/engine.py

@@ -122,6 +122,7 @@ layers = solara.reactive({
         '9': {'id':9, 'label': 'P9', 'description': 'Waste collection and rivers cleaning program ', 'applied': solara.reactive(False)},
         '10': {'id':10, 'label': 'P10', 'description': 'Enforcement of environmental protection zones', 'applied': solara.reactive(False)},
     },
+    'implementation_capacity_score': solara.reactive("high"),
     'metrics': {
         "metric1": {"desc": "Number of workers unemployed", "value": 0, "max_value": 100},
         "metric2": {"desc": "Number of children with no access to education", "value": 0, "max_value": 100},
@@ -487,7 +488,16 @@ def ExecutePanel():
                 fragility if hazard == "earthquake" else vulnerability, 
                 hazard,policies=policies)
             buildings['ds'] = list(df_bld_hazard['ds'])
-            computed_metrics, df_metrics = calculate_metrics(buildings, household, individual, hazard, policies=policies)
+
+            implementation_capacity_score = layers.value['implementation_capacity_score'].value
+            if implementation_capacity_score == 'medium':
+                capacity = 1.25
+            elif implementation_capacity_score == 'low':
+                capacity = 1.50
+            else:
+                capacity = 1
+            computed_metrics, df_metrics = calculate_metrics(buildings, household, 
+                                                             individual, hazard, policies=policies,capacity=capacity)
         
             print(computed_metrics)
             for metric in df_metrics.keys():
@@ -521,11 +531,19 @@ def ExecutePanel():
     # Execute the thread only when the depencency is changed
     result = solara.use_thread(execute_engine, dependencies=[execute_counter])
 
-    with solara.Row(justify="center"):
-        solara.ToggleButtonsMultiple(value=infra, on_value=set_infra, values=["building","power"])
-    with solara.Row(justify="center"):
-        solara.ToggleButtonsSingle(value=hazard, on_value=set_hazard, values=["earthquake","flood"])
-
+    with solara.GridFixed(columns=2):
+        solara.Text("Infrastructure Type")
+        with solara.Row(justify="right"):
+            solara.ToggleButtonsMultiple(value=infra, on_value=set_infra, values=["building","power"])
+        solara.Text("Hazard")
+        with solara.Row(justify="right"):
+            solara.ToggleButtonsSingle(value=hazard, on_value=set_hazard, values=["earthquake","flood"])
+        with solara.Tooltip("Building-level metrics will be increased by 25% and 50% for medium and low"):
+            solara.Text("Implementation Capacity Score")
+        with solara.Row(justify="right"):
+            solara.ToggleButtonsSingle(value=layers.value['implementation_capacity_score'].value, 
+                                values=['low','medium','high'],
+                                on_value=layers.value['implementation_capacity_score'].set)
     PolicyPanel()
     solara.ProgressLinear(value=False)
     solara.Button("Calculate", on_click=on_click, outlined=True,