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Initial commit with dash app files and data

app.py

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+import os
+import pandas as pd
+import numpy as np
+import geopandas as gpd
+import plotly.express as px
+from dash import Dash, dcc, html, Input, Output, callback
+
+external_stylesheets = ['https://codepen.io/chriddyp/pen/bWLwgP.css']
+
+app = Dash(__name__, external_stylesheets=external_stylesheets)
+server = app.server  # Required for deployment
+
+# Load data using relative paths
+flood_risk = gpd.read_file("data/final_flood_risk_scores.geojson")
+flood_risk['Census Tract'] = flood_risk['Census Tract'].astype(int)
+
+ami = pd.read_csv("data/acs_2023_ami_cleaned.csv")
+age = pd.read_csv("data/acs_2023_age_cleaned.csv")
+tenure = pd.read_csv("data/acs_2023_tenure_cleaned.csv")
+race = pd.read_csv("data/race_percentages.csv")
+
+# Adjust race dataframe for display
+race[['NH White','NH Black or African American','NH Asian','Hispanic or Latino','NH Other']] = race[['NH White','NH Black or African American','NH Asian','Hispanic or Latino','NH Other']] * 100
+race = race.rename(columns={'NH White': 'White', 'NH Black or African American': 'Black or African American','NH Asian' : 'Asian', 'NH Other': 'Other'})
+race = race.set_index("geoid")
+race.rename(index={'total': '111'}, inplace=True)
+race.index = race.index.astype(int)
+race.rename(index={111: 'San Francisco'}, inplace=True)
+
+
+def normalize_scores(series):
+    """Takes a series and normalizes values to be on a 0-100 range"""
+    max_val = series.max()
+    min_val = series.min()
+    scores = ((series - min_val) * 100) / (max_val - min_val)
+    return scores.round(1)
+
+
+def race_plot(tract_id: str):
+    rows = race.loc[['San Francisco', tract_id], race.columns[0:5]]
+    race_fig = px.bar(
+        rows.T,
+        y=rows.index,
+        x=rows.columns,
+        barmode="group",
+        labels={'geoid': 'Geographic Area', 'index': 'Race or Ethnicity', 'value': 'Percent of Total Population'},
+        color_discrete_sequence=['lightgray', 'black']
+    )
+    race_fig.update_layout(xaxis={'categoryorder': 'total descending'})
+    race_fig.update_traces(
+        hovertemplate="%{x}<br><b>%{y:,.0f}%<br></b><extra></extra>"
+    )
+    race_fig.update_layout({'plot_bgcolor': 'rgba(0,0,0,0)'})
+    return race_fig
+
+
+def baseline_race_plot():
+    rows = race.loc[['San Francisco'], race.columns[0:5]]
+    race_fig = px.bar(
+        rows.T,
+        y=rows.index,
+        x=rows.columns,
+        barmode="group",
+        labels={'geoid': 'Geographic Area', 'index': 'Race or Ethnicity', 'value': 'Percent of Total Population'},
+        color_discrete_sequence=['lightgray', 'black']
+    )
+    race_fig.update_layout(xaxis={'categoryorder': 'total descending'})
+    race_fig.update_traces(
+        hovertemplate="%{x}<br><b>%{y:,.0f}%<br></b><extra></extra>"
+    )
+    race_fig.update_layout({'plot_bgcolor': 'rgba(0,0,0,0)'})
+    return race_fig
+
+
+def plot_data(df: pd.DataFrame, tract_id: str, column: str, x_label: str):
+    df = df.copy()
+    df['select'] = 0
+    df['opacity'] = 0.5
+    df.loc[df['geoid'] == tract_id, 'select'] = 1
+    df.loc[df['geoid'] == tract_id, 'opacity'] = 1
+    fig = px.strip(
+        df,
+        x=column,
+        color='select',
+        stripmode='overlay',
+        color_discrete_map={1: 'black', 0: 'darkgray'},
+        labels={column: x_label},
+        hover_name='geoid',
+        hover_data={column: True, 'select': False}
+    )
+    fig.update_layout(showlegend=False)
+    fig.update_layout({'plot_bgcolor': 'rgba(0,0,0,0)'})
+    fig.update_layout(margin=dict(l=0, r=0, t=0, b=0))
+    fig.update_layout(height=250)
+    fig.update_traces({'marker': {'size': 15}})
+    fig.update_xaxes(gridcolor='#f2f2f2')
+    fig.update_traces(
+        hovertemplate=f"Census Tract: {tract_id}<br>{x_label}: <b>%{{x:,.0f}}<br></b><extra></extra>"
+    )
+    fig.data[0].marker.opacity = 0.2
+    fig.data[1].marker.opacity = 1.0
+    return fig
+
+
+def baseline_plot(df: pd.DataFrame, column: str, x_label: str):
+    df = df.copy()
+    df['select'] = 0
+    df['opacity'] = 0.5
+    fig = px.strip(
+        df,
+        x=column,
+        color_discrete_sequence=['darkgray'],
+        stripmode='overlay',
+        labels={column: x_label},
+        hover_name='geoid',
+        hover_data={column: True, 'select': False},
+        custom_data=['geoid']
+    )
+    fig.update_layout(showlegend=False)
+    fig.update_layout({'plot_bgcolor': 'rgba(0,0,0,0)'})
+    fig.update_layout(margin=dict(l=0, r=0, t=0, b=0))
+    fig.update_layout(height=250)
+    fig.update_traces({'marker': {'size': 15}})
+    fig.update_xaxes(gridcolor='#f2f2f2')
+    fig.update_traces(marker=dict(opacity=0.2))
+    fig.update_traces(
+        hovertemplate="Census Tract: %{customdata[0]}<br>" + x_label + ": <b>%{x:,.0f}<br></b><extra></extra>"
+    )
+    return fig
+
+
+def update_figure(value1, value2, value3):
+    updated_flood = flood_risk.copy()
+    total_value = value1 + value2 + value3
+    factor1 = value1 / total_value
+    factor2 = value2 / total_value
+    factor3 = value3 / total_value
+    updated_flood["Weighted flood risk score"] = (
+        updated_flood["Floodplain coverage score"] * factor1 +
+        updated_flood["Drain quality score"] * factor2 +
+        updated_flood["Green infrastructure score"] * factor3
+    )
+    updated_flood["Weighted flood risk score"] = normalize_scores(updated_flood["Weighted flood risk score"])
+
+    sf_lat = 37.7749
+    sf_lon = -122.4194
+    updated_flood = updated_flood.set_index('Census Tract')
+    updated_flood = updated_flood.to_crs("EPSG:4326")
+    fig = px.choropleth_map(
+        updated_flood,
+        geojson=updated_flood.geometry,
+        locations=updated_flood.index,
+        color="Weighted flood risk score",
+        center={"lat": sf_lat, "lon": sf_lon},
+        map_style="carto-positron",
+        zoom=10,
+        color_continuous_scale='blues'
+    )
+    fig.update_layout(
+        transition_duration=200,
+        uirevision="constant",
+        clickmode='event+select',
+        width=800,
+        height=400,
+        margin={"r": 0, "t": 0, "l": 0, "b": 0},
+        coloraxis_colorbar=dict(orientation='h', len=0.8),
+        coloraxis_colorbar_title_font=dict(family='sans-serif')
+    )
+    fig.update_traces(
+        marker_line_width=0.5,
+        marker_opacity=0.75,
+        marker_line_color='white'
+    )
+    fig.update_traces(
+        selected=dict(marker=dict(opacity=1)),
+        unselected=dict(marker=dict(opacity=0.5))
+    )
+    fig.update_traces(
+        hovertemplate="Census Tract: %{location}<br>Weighted flood risk score: <b>%{z:,.0f}</b><extra></extra>"
+    )
+    return fig
+
+
+def update_graphs(clicked_data):
+    if clicked_data is not None:
+        tract_id = clicked_data["points"][0]["location"]
+        ami_fig = plot_data(ami, tract_id, "median_income", 'Median Income')
+        tenure_fig = plot_data(tenure, tract_id, "pct_renter", 'Percent Renter Households')
+        under5_fig = plot_data(age, tract_id, 'Under 5 Years', 'Percent Residents Under 5 Years')
+        over65_fig = plot_data(age, tract_id, '65 Years and Over', 'Percent Residents 65 Years and Older')
+        race_fig = race_plot(tract_id)
+        return ami_fig, tenure_fig, under5_fig, over65_fig, race_fig
+    else:
+        ami_fig = baseline_plot(ami, "median_income", "Median Income")
+        tenure_fig = baseline_plot(tenure, "pct_renter", 'Percent Renter Households')
+        under5_fig = baseline_plot(age, 'Under 5 Years', 'Percent Residents Under 5 Years')
+        over65_fig = baseline_plot(age, '65 Years and Over', 'Percent Residents 65 Years and Older')
+        race_fig = baseline_race_plot()
+        return ami_fig, tenure_fig, under5_fig, over65_fig, race_fig
+
+
+# Set initial state
+initial_ami_fig, initial_tenure_fig, initial_under5_fig, initial_over65_fig, initial_race_fig = update_graphs(None)
+initial_flood_map = update_figure(2, 2, 2)
+
+# Build the app layout
+app.layout = html.Div([
+    html.Div(
+        children="Analyzing Flood Risk in San Francisco",
+        style={'fontFamily': 'Arial', 'fontSize': '40px', "font-weight": "bold", 'textAlign': 'center', 'paddingTop': '30px'}
+    ),
+    html.Div([
+        # Sliders
+        html.Div([
+            html.Div([
+                html.H3("Select a floodplain coverage weight:", style={'fontFamily': 'Arial', 'fontSize': '18px', "font-weight": "normal", 'letterSpacing': '.05px'}),
+                dcc.Slider(0, 4, step=None, marks={0: 'low priority', 1: '', 2: 'moderate priority', 3: '', 4: 'high priority'}, value=2, id='floodplain_slider')
+            ]),
+            html.Div([
+                html.H3("Select a drain quality weight:", style={'fontFamily': 'Arial', 'fontSize': '18px', 'letterSpacing': '.05px'}),
+                dcc.Slider(0, 4, step=None, marks={0: 'low priority', 1: '', 2: 'moderate priority', 3: '', 4: 'high priority'}, value=2, id='drain_slider')
+            ]),
+            html.Div([
+                html.H3("Select a green infrastructure weight:", style={'fontFamily': 'Arial', 'fontSize': '18px', 'letterSpacing': '.05px'}),
+                dcc.Slider(0, 4, step=None, marks={0: 'low priority', 1: '', 2: 'moderate priority', 3: '', 4: 'high priority'}, value=2, id='gi_slider')
+            ]),
+        ], style={'display': 'flex', 'flexDirection': 'column', 'flex': 1, 'marginTop': '20px', 'paddingRight': '40px', 'padding': '3%', 'justifyContent': 'center', "gap": "25px"}),
+        html.Div(id='slider-output-container'),
+        # Flood risk score map
+        html.Div(dcc.Graph(id='flood-risk-graph', figure=initial_flood_map), style={'padding': '3%', 'flex': 1})
+    ], style={'display': 'flex', 'flexDirection': 'row'}),
+
+    # Census tract name display
+    html.Div([
+        html.Span("Census Tract:", style={'fontFamily': 'Arial', 'fontSize': '18px', 'marginRight': '5px'}),
+        html.Span(id='tract_id', style={'fontFamily': 'Arial', 'fontSize': '18px', 'font-weight': 'bold'})
+    ], style={'display': 'flex', 'flexDirection': 'row', 'padding': '3%', 'justify-content': 'center'}),
+
+    # AMI and tenure charts
+    html.Div([
+        html.Div(dcc.Graph(id='ami_fig', figure=initial_ami_fig), style={'padding': '3%', 'flex': 1}),
+        html.Div(dcc.Graph(id='tenure_fig', figure=initial_tenure_fig), style={'padding': '3%', 'flex': 1}),
+    ], style={'display': 'flex', 'flexDirection': 'row'}),
+
+    # Age charts
+    html.Div([
+        html.Div(dcc.Graph(id='under5_fig', figure=initial_under5_fig), style={'padding': '3%', 'flex': 1}),
+        html.Div(dcc.Graph(id='over65_fig', figure=initial_over65_fig), style={'padding': '3%', 'flex': 1}),
+    ], style={'display': 'flex', 'flexDirection': 'row'}),
+
+    # Race bar chart
+    html.Div([
+        html.Div(dcc.Graph(id='race_fig', figure=initial_race_fig), style={'padding': '3%', 'flex': 1}),
+    ], style={'display': 'flex', 'flexDirection': 'row', 'paddingLeft': '20%', 'paddingRight': '20%'}),
+])
+
+
+@callback(
+    [Output('flood-risk-graph', 'figure'),
+     Output('ami_fig', 'figure'),
+     Output('tenure_fig', 'figure'),
+     Output('under5_fig', 'figure'),
+     Output('over65_fig', 'figure'),
+     Output('race_fig', 'figure'),
+     Output('tract_id', 'children')],
+    [Input('floodplain_slider', 'value'),
+     Input('drain_slider', 'value'),
+     Input('gi_slider', 'value'),
+     Input('flood-risk-graph', 'clickData')]
+)
+def update_output(value1, value2, value3, clickData):
+    flood_risk_fig = update_figure(value1, value2, value3)
+    ami_fig, tenure_fig, under5_fig, over65_fig, race_fig = update_graphs(clickData)
+    if clickData is not None:
+        tract_id = clickData["points"][0]["location"]
+    else:
+        tract_id = 'Select a census tract on the map'
+    return flood_risk_fig, ami_fig, tenure_fig, under5_fig, over65_fig, race_fig, tract_id
+
+
+if __name__ == '__main__':
+    app.run(debug=False, host="0.0.0.0", port=7860)

data/acs_2023_age_cleaned.csv

@@ -0,0 +1,245 @@
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data/acs_2023_ami_cleaned.csv

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data/acs_2023_tenure_cleaned.csv

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data/race_percentages.csv

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requirements.txt

@@ -0,0 +1,6 @@
+dash
+pandas
+numpy
+geopandas
+plotly
+pyproj