FL-disaster-vulnerabilities

import folium
from folium import FeatureGroup, LayerControl
from folium.plugins import HeatMap, MiniMap
from branca.colormap import LinearColormap
from folium.features import GeoJsonTooltip
import pandas as pd
import geopandas as gpd

# google sheet by David E 
document_id = '1EIHK3lGBfIWVhOKcW5QJjjHdhrMeiitwzt3Oh6kugJ4'
tab_name = 'data_1-FL'
url = f'https://docs.google.com/spreadsheets/d/{document_id}/gviz/tq?tqx=out:csv&sheet={tab_name}'

df = pd.read_csv(url)

df.head()

# geojson from arcgis https://www.arcgis.com/home/item.html?id=3c164274a80748dda926a046525da610
# url = 'https://services.arcgis.com/P3ePLMYs2RVChkJx/arcgis/rest/services/USA_Counties_Generalized_Boundaries/FeatureServer/0/query?outFields=*&where=1%3D1&f=geojson' # exceededTransferLimit	true, refining query
url = 'https://services.arcgis.com/P3ePLMYs2RVChkJx/arcgis/rest/services/USA_Counties_Generalized_Boundaries/FeatureServer/0/query?outFields=*&where=STATE_ABBR%3D%27FL%27&f=geojson'

# Read the GeoJSON data into a GeoDataFrame
counties = gpd.read_file(url)

# Print or inspect the counties data
counties.head()

# Define the features you want to visualize and their corresponding column names
feature_list = [
   
    {
        'name': 'Energy Burden',
        'column': 'energy_burden_percentile',
        'enabled': False 
    },
    {
        'name': 'Expected Agricultural Loss due to Natural Hazards Heatmap',
        'column': 'expected_agricultural_loss_rate_natural_hazards_risk_index_percentile',
        'enabled': False 
    },
    {
        'name': 'Expected Building Loss due to Natural Hazards Heatmap',
        'column': 'expected_building_loss_rate_natural_hazards_risk_index_percentile',
        'enabled': False  
    },
    {
        'name': 'Expected Population Loss due to Natural Hazards Heatmap',
        'column': 'expected_population_loss_rate_natural_hazards_risk_index_percentile',
        'enabled': False 
    },
    {
        'name': 'Risk of Fire in 30 years Heatmap',
        'column': 'share_of_properties_at_risk_of_fire_in_30_years_percentile',
        'enabled': False  
    },
    {
        'name': 'Risk of Flood in 30 years Heatmap',
        'column': 'share_of_properties_at_risk_of_flood_in_30_years_percentile',
        'enabled': False  
    }
]

# Prepare required columns
required_columns = {'Lat', 'Lon', 'geoid'} | {f['column'] for f in feature_list}
required_columns = [col['column'] if isinstance(col, dict) else col for col in required_columns]

# clean and reduce data
df_clean = df.dropna(subset=required_columns)
df_clean_reduced = df_clean[required_columns]
# df_clean_reduced.head()

# Create base map
m = folium.Map(location=[df_clean_reduced['Lat'].mean(), df_clean_reduced['Lon'].mean()], zoom_start=7)

# Create shared color scale for percentile data
shared_color_scale = LinearColormap(
    colors=['green', 'yellow', 'red'],
    vmin=0,
    vmax=100,
    caption="Percentile"
)

# Function to create grid
def create_grid(df, feature_column, grid_size=0.1):
    df['lat_bin'] = np.floor(df['Lat'] / grid_size) * grid_size
    df['lon_bin'] = np.floor(df['Lon'] / grid_size) * grid_size
    grid = df.groupby(['lat_bin', 'lon_bin']).agg({
        feature_column: 'mean',
        'geoid': lambda x: x.iloc[0]  # Take the first geoid in the group
    }).reset_index()
    return grid

# Function to get county name from geoid
def get_county_name(geoid):
    county = counties[counties['FIPS'] == str(geoid)[:5]]
    return county['NAME'].iloc[0] if not county.empty else "Unknown County"

# Iterate over features
for feature in feature_list:
    # Create grid
    grid = create_grid(df_clean_reduced, feature['column'])
    
    # Create feature group
    feature_group = folium.FeatureGroup(name=feature['name'], show=feature['enabled'])
    
    # Add grid cells to feature group
    for _, row in grid.iterrows():
        value = row[feature['column']]
        county_name = get_county_name(row['geoid'])
        
        popup_text = f"""
        <b>{feature['name']}</b><br>
        County: {county_name}<br>
        Percentile: {value:.2f}<br>
        Geoid: {row['geoid']}
        """
        
        folium.CircleMarker(
            location=[row['lat_bin'], row['lon_bin']],
            radius=10,
            popup=folium.Popup(popup_text, max_width=300),
            tooltip=f"{feature['name']} Percentile: {value:.2f}",
            color=shared_color_scale(value),
            fill=True,
            fillColor=shared_color_scale(value),
            fillOpacity=0.7,
            weight=0
        ).add_to(feature_group)
    
    feature_group.add_to(m)

# Add county boundaries
folium.GeoJson(
    counties,
    name='County Boundaries',
    style_function=lambda feature: {
        'fillColor': 'transparent',
        'color': 'black',
        'weight': 1,
        'fillOpacity': 0.7,
    },
    tooltip=folium.GeoJsonTooltip(
        fields=['NAME'],
        aliases=['County:'],
        localize=True
    )
).add_to(m)

# Add shared color scale legend to map
shared_color_scale.add_to(m)

# Add layer control
folium.LayerControl().add_to(m)

# Add custom JavaScript to toggle legend visibility
legend_toggle_js = """
<script>
document.addEventListener('DOMContentLoaded', function() {
    var legendElement = document.querySelector('.leaflet-bottom.leaflet-right');
    var layerControl = document.querySelector('.leaflet-control-layers-expanded');
    
    function updateLegendVisibility() {
        var checkboxes = layerControl.querySelectorAll('input[type="checkbox"]');
        var anyLayerVisible = Array.from(checkboxes).some(cb => cb.checked);
        legendElement.style.display = anyLayerVisible ? 'block' : 'none';
    }
    
    // Initial check
    updateLegendVisibility();
    
    // Add event listeners to checkboxes
    layerControl.addEventListener('change', updateLegendVisibility);
});
</script>
"""

# Add the custom JavaScript to the map
m.get_root().html.add_child(folium.Element(legend_toggle_js))

m