Added new policies

tomorrowcities/backend/engine.py

@@ -127,6 +127,8 @@ def compute_power_infra(nodes,edges,intensity,fragility):
 
 def compute(gdf_landuse, gdf_buildings, df_household, df_individual,gdf_intensity, df_hazard, hazard_type, policies=[]):
 
+    np.random.seed(seed=0)
+
     column_names = {'zoneID':'zoneid','bldID':'bldid','nHouse':'nhouse',
                     'specialFac':'specialfac','expStr':'expstr','repValue':'repvalue',
                     'xCoord':'xcoord','yCoord':'ycoord','hhID':'hhid','nInd':'nind',
@@ -177,6 +179,8 @@ def compute(gdf_landuse, gdf_buildings, df_household, df_individual,gdf_intensit
 
     gdf_building_intensity = gdf_building_intensity.merge(gdf_landuse[['zoneid','avgincome']],on='zoneid',how='left')
 
+    gdf_building_intensity['rnd'] = np.random.random((len(gdf_building_intensity),1))
+
     # TODO: Check if the logic makes sense
     if hazard_type == HAZARD_FLOOD:
         away_from_flood = gdf_building_intensity['distance'] > threshold_flood_distance
@@ -186,17 +190,109 @@ def compute(gdf_landuse, gdf_buildings, df_household, df_individual,gdf_intensit
     gdf_building_intensity[['material','code_level','storeys','occupancy']] =  \
         gdf_building_intensity['expstr'].str.split('+',expand=True)
     gdf_building_intensity['height'] = gdf_building_intensity['storeys'].str.extract(r'([0-9]+)s').astype('int')
+
+
+    if 1 in policies:
+        # First, mid-code -> high-code
+        # then, low-code -> mid-code
+        gdf_building_intensity.loc[(gdf_building_intensity['occupancy'] == 'Res') & (gdf_building_intensity['code_level'] == 'MC'), 'code_level'] = 'HC'
+        gdf_building_intensity.loc[(gdf_building_intensity['occupancy'] == 'Res') & (gdf_building_intensity['code_level'] == 'LC'), 'code_level'] = 'MC'
+        if hazard_type == HAZARD_FLOOD or hazard_type == HAZARD_DEBRIS:
+            max_height = gdf_building_intensity['height'].max()
+            idx = gdf_building_intensity['occupancy'] == 'Res'
+            gdf_building_intensity.loc[idx, 'height'] = gdf_building_intensity[idx]['height'].apply(lambda h: min(max_height, h+2))
+            gdf_building_intensity['storeys'] = gdf_building_intensity['height'].apply(lambda h: str(h)+'s')
+
+    if 2 in policies:
+        # First, mid-code -> high-code
+        # then, low-code -> mid-code
+        gdf_building_intensity.loc[((gdf_building_intensity['avgincome'] == 'lowIncomeA') | (gdf_building_intensity['avgincome'] == 'lowIncomeA')) & (gdf_building_intensity['code_level'] == 'MC'), 'code_level'] = 'HC'
+        gdf_building_intensity.loc[((gdf_building_intensity['avgincome'] == 'lowIncomeA') | (gdf_building_intensity['avgincome'] == 'lowIncomeA')) & (gdf_building_intensity['code_level'] == 'LC'), 'code_level'] = 'MC'
+        if hazard_type == HAZARD_FLOOD or hazard_type == HAZARD_DEBRIS:
+            max_height = gdf_building_intensity['height'].max()
+            idx = (gdf_building_intensity['avgincome'] == 'lowIncomeA') | (gdf_building_intensity['avgincome'] == 'lowIncomeA')
+            gdf_building_intensity.loc[idx, 'height'] = gdf_building_intensity[idx]['height'].apply(lambda h: min(max_height, h+4))
+            gdf_building_intensity['storeys'] = gdf_building_intensity['height'].apply(lambda h: str(h)+'s')
+
+    if 3 in policies:
+        if hazard_type == HAZARD_FLOOD or hazard_type == HAZARD_DEBRIS:
+            max_height = gdf_building_intensity['height'].max()
+            gdf_building_intensity.loc[:, 'height'] = gdf_building_intensity['height'].apply(lambda h: min(max_height, h+6))
+            gdf_building_intensity['storeys'] = gdf_building_intensity['height'].apply(lambda h: str(h)+'s')
+
+    if 4 in policies:
+        if hazard_type == HAZARD_FLOOD or hazard_type == HAZARD_DEBRIS:
+            max_height = gdf_building_intensity['height'].max()
+            idx = gdf_building_intensity['specialfac'] != 0
+            gdf_building_intensity.loc[idx, 'height'] = gdf_building_intensity[idx]['height'].apply(lambda h: min(max_height, h+4))
+            gdf_building_intensity['storeys'] = gdf_building_intensity['height'].apply(lambda h: str(h)+'s')
+
+    if 5 in policies:
+        # First, mid-code -> high-code
+        # then, low-code -> mid-code
+        idx = ((gdf_building_intensity['avgincome'] == 'lowIncomeA') |\
+                (gdf_building_intensity['avgincome'] == 'lowIncomeA') &\
+                (gdf_building_intensity['specialfac'] == 0))
+        gdf_building_intensity.loc[idx & (gdf_building_intensity['code_level'] == 'MC'), 'code_level'] = 'HC'
+        gdf_building_intensity.loc[idx & (gdf_building_intensity['code_level'] == 'LC'), 'code_level'] = 'MC'
+        if hazard_type == HAZARD_FLOOD or hazard_type == HAZARD_DEBRIS:
+            max_height = gdf_building_intensity['height'].max()
+            gdf_building_intensity.loc[idx, 'height'] = gdf_building_intensity[idx]['height'].apply(lambda h: min(max_height, h+1))
+            gdf_building_intensity['storeys'] = gdf_building_intensity['height'].apply(lambda h: str(h)+'s')
+
+    if 6 in policies:
+        # First, mid-code -> high-code
+        # then, low-code -> mid-code
+        idx = ((gdf_building_intensity['avgincome'] == 'lowIncomeA') |\
+                (gdf_building_intensity['avgincome'] == 'lowIncomeA') &\
+                (gdf_building_intensity['specialfac'] != 0))
+        gdf_building_intensity.loc[idx & (gdf_building_intensity['code_level'] == 'MC'), 'code_level'] = 'HC'
+        gdf_building_intensity.loc[idx & (gdf_building_intensity['code_level'] == 'LC'), 'code_level'] = 'MC'
+        if hazard_type == HAZARD_FLOOD or hazard_type == HAZARD_DEBRIS:
+            max_height = gdf_building_intensity['height'].max()
+            gdf_building_intensity.loc[idx, 'height'] = gdf_building_intensity[idx]['height'].apply(lambda h: min(max_height, h+1))
+            gdf_building_intensity['storeys'] = gdf_building_intensity['height'].apply(lambda h: str(h)+'s')
+
+    if 7 in policies:
+        if hazard_type == HAZARD_FLOOD or hazard_type == HAZARD_DEBRIS:
+            idx = (gdf_building_intensity['rnd'] < 0.70) & (gdf_building_intensity['occupancy'] == 'Res')
+            gdf_building_intensity.loc[idx,'occupancy'] = 'Agri'
+
+    if 8 in policies:
+        if hazard_type == HAZARD_FLOOD or hazard_type == HAZARD_DEBRIS:
+            max_height = gdf_building_intensity['height'].max()
+            idx = gdf_building_intensity['specialfac'] == 0
+            gdf_building_intensity.loc[idx, 'height'] = gdf_building_intensity[idx]['height'].apply(lambda h: min(max_height, h+4))
+            gdf_building_intensity['storeys'] = gdf_building_intensity['height'].apply(lambda h: str(h)+'s')
+
+    if 9 in policies:
+        if hazard_type == HAZARD_FLOOD or hazard_type == HAZARD_DEBRIS:
+            max_height = gdf_building_intensity['height'].max()
+            gdf_building_intensity.loc[:, 'height'] = gdf_building_intensity['height'].apply(lambda h: min(max_height, h+1))
+            gdf_building_intensity['storeys'] = gdf_building_intensity['height'].apply(lambda h: str(h)+'s')
+
+    if 10 in policies:
+        if hazard_type == HAZARD_FLOOD or hazard_type == HAZARD_DEBRIS:
+            idx = gdf_building_intensity['rnd'] < 0.80
+            gdf_building_intensity.loc[idx,'occupancy'] = 'Agri'
+
     lr = (gdf_building_intensity['height'] <= 4)
     mr = (gdf_building_intensity['height'] >= 5) & (gdf_building_intensity['height'] <= 8)
     hr = (gdf_building_intensity['height'] >= 9)
     gdf_building_intensity.loc[lr, 'height_level'] = 'LR'
     gdf_building_intensity.loc[mr, 'height_level'] = 'MR'
     gdf_building_intensity.loc[hr, 'height_level'] = 'HR'
+
     # Earthquake uses simplified taxonomy
     gdf_building_intensity['vulnstreq'] = \
         gdf_building_intensity[['material','code_level','height_level']] \
             .agg('+'.join,axis=1)
-    # 
+    
+    gdf_building_intensity['expstr'] = \
+        gdf_building_intensity[['material','code_level','storeys','occupancy']] \
+            .agg('+'.join,axis=1)
+    
+     
     if hazard_type == HAZARD_EARTHQUAKE:
         bld_eq = gdf_building_intensity.merge(df_hazard, left_on='vulnstreq',right_on='expstr', how='left')
         nulls = bld_eq['muds1_g'].isna()
@@ -213,12 +309,6 @@ def compute(gdf_landuse, gdf_buildings, df_household, df_individual,gdf_intensit
             bld_eq[f'ds_{i}'] = np.abs(bld_eq[f'prob_ds{i-1}'] - bld_eq[f'prob_ds{i}'])
         df_ds = bld_eq[['ds_1','ds_2','ds_3','ds_4','ds_5']]
         bld_eq['eq_ds'] = df_ds.idxmax(axis='columns').str.extract(r'ds_([0-9]+)').astype('int') - 1
-
-        if 1 in policies:
-            bld_eq.loc[bld_eq['occupancy'] == 'Res', 'eq_ds'] = 0
-        if 2 in policies:
-            bld_eq.loc[bld_eq['avgincome'] == 'lowIncomeA', 'eq_ds'] = 0
-            bld_eq.loc[bld_eq['avgincome'] == 'lowIncomeB', 'eq_ds'] = 0
         # Create a simplified building-hazard relation
         bld_hazard = bld_eq[['bldid','eq_ds']]
         bld_hazard = bld_hazard.rename(columns={'eq_ds':'ds'})
@@ -235,11 +325,6 @@ def compute(gdf_landuse, gdf_buildings, df_household, df_individual,gdf_intensit
         bld_flood['fl_prob'] = np.diag(flood_mapping(xnew))
         bld_flood['fl_ds'] = 0
         bld_flood.loc[bld_flood['fl_prob'] > threshold_flood,'fl_ds'] = 1
-        if 1 in policies:
-            bld_flood.loc[bld_flood['occupancy'] == 'Res', 'fl_ds'] = 0
-        if 2 in policies:
-            bld_flood.loc[bld_flood['avgincome'] == 'lowIncomeA', 'fl_ds'] = 0
-            bld_flood.loc[bld_flood['avgincome'] == 'lowIncomeB', 'fl_ds'] = 0
         # Create a simplified building-hazard relation
         bld_hazard = bld_flood[['bldid','fl_ds']]
         bld_hazard = bld_hazard.rename(columns={'fl_ds':'ds'})

tomorrowcities/content/articles/flood.md

@@ -0,0 +1,31 @@
+---
+author: huseyin.kaya
+title: Flood Vulnerability Analysus
+description: Methodologies in flood damage assessment
+image: https://images.unsplash.com/photo-1429041966141-44d228a42775?ixid=MXwxMjA3fDB8MHxwaG90by1wYWdlfHx8fGVufDB8fHw%3D&ixlib=rb-1.2.1&auto=format&fit=crop&w=2500&q=80
+thumbnail: https://images.unsplash.com/photo-1429041966141-44d228a42775?ixid=MXwxMjA3fDB8MHxwaG90by1wYWdlfHx8fGVufDB8fHw%3D&ixlib=rb-1.2.1&auto=format&fit=crop&w=350&q=80
+alt: "Flood vulnerability analysis"
+createdAt: 2023-10-10
+duration: 6 min read
+category:
+  - general
+---
+
+The core component used in flood vulnerability analysis is to generate a mapping between water levels and relative damage on the structure. The shape of the curve depends on the typology of the structure, material type, height, code levels and many other features.
+
+The Joint Research Centre (JRC) led by the European Commission provides technical reports and guidelines to generate damage curves [1]. It is also always possible to make minor modification for special cases such as single storey adobe buildings in Africa [2]. 
+
+We provide a user interface to visualize and generate damages curves in [3].
+
+## Flood Vulnerability Calcul
+
+## Remarks
+* In both JRC [1] and modified version by Englhardt et.al [2],  the generated curves
+do not depend on code levels of the structures. So any policy affecting code levels in flood damage assessment should find another way to change the damage states. See [policies](/docs/policies) for more information.
+
+
+## References
+
+* [1] Global flood depth-damage functions: Methodology and the database with guidelines, [link](https://publications.jrc.ec.europa.eu/repository/handle/JRC105688)
+* [2] Englhardt, Johanna, et al. "Enhancement of large-scale flood risk assessments using building-material-based vulnerability curves for an object-based approach in urban and rural areas." Natural Hazards and Earth System Sciences 19.8 (2019): 1703-1722.  [link](https://nhess.copernicus.org/articles/19/1703/2019/nhess-19-1703-2019.pdf)
+* [3] [Flood Vulnerability Generator](https://huggingface.co/spaces/hkayabilisim/flood_vulnerability_generator)

tomorrowcities/content/articles/policies.md

@@ -14,9 +14,65 @@ category:
 ### Policy 1: Land and tenure security program
 When this policy applied, the code levels of the residential buildings are set to higher standards 
 which effectively reduces the damage states during hazard simulations. Since this policy only changes
-the code levels, the computing engine is required to run again to see its effect.
+the code levels, the computing engine is required to run again whevener this policy is applied.
+
+There could be cases where the damage curves used to assess the vulnerability do not depend on code levels.
+Flood damage curves, for instance, fall into this category. In such a case, we opt to increase the
+height of buildings by addingt two storeys to work witk more suppressed damage curves.
+Please note that, the scope of this change is only a temporary one and is bound only to flood damage calculation. 
+
 
 ### Policy 2: State-led upgrading/retrofitting of low-income/informal housing
 When applied, this policy increase the code level of the buildings located in low income A and B zones.
 This effectively reduces the damage states, and hence several impact metrics, starting from building-level
-metrics.
+metrics. 
+
+Similar to policy 1, if the code level is not used in damage assessment other features are used 
+to increase the stability of the structure. For instance, for flood calculations, the height of the structures
+are increased by 2 storeys.
+
+### Policy 3: Robust investment in WASH (water, sanitation and hygiene) and flood-control infrastructure 
+When applied the expected outcome is exposure and Social Vulnerability reduction - decreased diseases, 
+casualties and other losses and damages in the event of floods due to better water and sewage management. 
+
+Since the effect of this policy is very dramatic, 6 storeys are added.
+
+### Policy 4: Investments in road networks and public spaces through conventional paving
+Expected outcome: Exposure reduction - better mobility and more escape routes during hazard events. 
+Since the transportation network analysis is not done yet, we choose to improve the special
+facilities to mimic the stability of road networks.  In other works for flood hazard type, we decrese 
+the damage curve value for special facilities by increasing the height of the structures.
+
+### 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.
+
+### 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.
+
+### 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. 
+Concerns: Equity – who would benefit from policy in practice and have access to produce
+
+In implementating this policy, we change the occupancy type of 30% of the residulual buildings
+into agriculture. So the damage curves for the agricultural areas is much lower than residental types.
+
+### Policy 8: Emergency cash transfers to vulnerable households
+Expected outcome: Social Vulnerability reduction - strengthening of economic capacity. 
+Concerns: Implementation/Enforcement – funds could not be available as soon as hazards hit, decreasing their effectivity. Equity – ‘invisible residents’ (e.g., migrants) would lose benefit. 
+Implementation: damage curves of non-special buildings are suppressed.
+
+### Policy 9: Waste collection and rivers cleaning program 
+Expected outcome: Social Vulnerability reduction – reduced risk of diseases and other negative effects of waste mixed with water runoff
+Concerns: Implementation/Enforcement – policy could take a long time to generate meaningful results. Not clear if effective in the immediate aftermath of a flood
+Implementation: damage curves of every structure in the visioning scenarios are moderately suppressed.
+
+## Policy 10: Enforcement of environmental protection zones
+Expected outcome: Reduced exposure in hazard-prone or environmentally sensitive areas
+Concerns: Equity – increased land prices or displacement of informal settlers in environmentally sensitive areas
+Implementation: some random subset of buildings are assigned as agriculture mimiching to create protected zones.
+

tomorrowcities/pages/engine.py

@@ -111,8 +111,16 @@ layers = solara.reactive({
     'render_count': solara.reactive(0),
     'bounds': solara.reactive(None),
     'policies': {
-        '1': {'id':1, 'label': 'Policy 1', 'description': 'Land and tenure security program', 'applied': solara.reactive(False)},
-        '2': {'id':2, 'label': 'Policy 2', 'description': 'State-led upgrading/retrofitting of low-income/informal housing', 'applied': solara.reactive(False)},
+        '1': {'id':1, 'label': 'P1', 'description': 'Land and tenure security program', 'applied': solara.reactive(False)},
+        '2': {'id':2, 'label': 'P2', 'description': 'State-led upgrading/retrofitting of low-income/informal housing', 'applied': solara.reactive(False)},
+        '3': {'id':3, 'label': 'P3', 'description': 'Robust investment in WASH (water, sanitation and hygiene) and flood-control infrastructure', 'applied': solara.reactive(False)},
+        '4': {'id':4, 'label': 'P4', 'description': 'Investments in road networks and public spaces through conventional paving', 'applied': solara.reactive(False)},
+        '5': {'id':5, 'label': 'P5', 'description': 'Shelter Law - All low-income and informal settlements should have physical and free access to community centres and shelters', 'applied': solara.reactive(False)},
+        '6': {'id':6, 'label': 'P6', 'description': 'Funding community-based networks in low-income areas (holistic approaches)', 'applied': solara.reactive(False)},
+        '7': {'id':7, 'label': 'P7', 'description': 'Urban farming programs', 'applied': solara.reactive(False)},
+        '8': {'id':8, 'label': 'P8', 'description': 'Emergency cash transfers to vulnerable households', 'applied': solara.reactive(False)},
+        '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)},
     },
     'metrics': {
         "metric1": {"desc": "Number of workers unemployed", "value": 0, "max_value": 100},
@@ -536,7 +544,7 @@ def ExecutePanel():
 
 @solara.component
 def PolicyPanel():
-    with solara.Row():
+    with solara.GridFixed(columns=6):
         for policy_key, policy in layers.value['policies'].items():
             with solara.Tooltip(tooltip=policy['description']):
                 solara.Checkbox(label=policy['label'],