PyCafe - Streamlit - 2-Step Floating Catchment Area Analysis

app.py
requirements.txt
app.py
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import streamlit as st
import pandas as pd
import geopandas as gpd


def get_distance(row):
    distance = (row["dx"] - row["sx"])**2 + (row["dy"] - row["sy"])**2
    distance = distance**0.5
    return distance


## Title and description
st.title('2SFCA')

st.markdown('This app calculates the 2-Step Floating Catchment Area (2SFCA) accessibility score for a given supply and demand dataset.')


## Upload data and preprocess the data

cols = st.columns(2)
supply = cols[0].file_uploader('Upload supply data', type=['csv', 'xlsx','zip', 'geojson', 'gpkg'])
# upload target data
demand = cols[1].file_uploader('Upload demand data', type=['csv', 'xlsx', 'zip', 'geojson','gpkg'])


if supply and demand:
    # read supply data
    if supply.name.endswith('.csv'):
        supply_data = pd.read_csv(supply)
    elif supply.name.endswith('.xlsx'):
        supply_data = pd.read_excel(supply)
    elif supply.name.endswith('.zip'):
        supply_data = gpd.read_file(f'zip://{supply.name}')
    elif supply.name.endswith('.geojson'):
        supply_data = gpd.read_file(supply)
    elif supply.name.endswith('.gpkg'):
        supply_data = gpd.read_file(supply)


    # read demand data
    if demand.name.endswith('.csv'):
        demand_data = pd.read_csv(demand)
    elif demand.name.endswith('.xlsx'):
        demand_data = pd.read_excel(demand)
    elif demand.name.endswith('.zip'):
        demand_data = gpd.read_file(f'zip://{demand.name}')
    elif demand.name.endswith('.geojson'):
        demand_data = gpd.read_file(demand)
    elif demand.name.endswith('.gpkg'):
        demand_data = gpd.read_file(demand)


## preview the data
    cols[0].write(supply_data.head())
    cols[1].write(demand_data.head())
    # plot supply data
    supply_data["lat"] = supply_data.geometry.y
    supply_data["lon"] = supply_data.geometry.x
    demand_data["lat"] = demand_data.geometry.y
    demand_data["lon"] = demand_data.geometry.x
    import matplotlib.pyplot as plt
    fig0 , ax0 = plt.subplots(figsize=(8, 6))
    supply_data.plot(ax=ax0, color='red')
    cols[0].pyplot(fig0)

    fig1 , ax1 = plt.subplots(figsize=(8, 6))
    demand_data.plot(ax=ax1, color='blue')
    cols[1].pyplot(fig1)

## 2sfca parameters
    s_va = cols[0].selectbox(
    "Supply Variable",
    supply_data.columns)
    d_va = cols[1].selectbox(
    "Demand Variable",
    demand_data.columns)

    radius = cols[0].slider('Select Service radius', min_value=0, max_value=100000, value=32180, step=1000)
    buffer_radius = supply_data.buffer(radius)
    fig_b , ax_b = plt.subplots(figsize=(8, 6))
    buffer_radius.plot(ax=ax_b)
    cols[1].pyplot(fig_b)
    # buffer_radius.plot()

## Run 2sfca
    if st.button('Run'):

# calculate distance matrix
        supply_data["sx"],supply_data["sy"] = supply_data.geometry.x,supply_data.geometry.y
        supply_data["sid"] = supply_data.index
        demand_data["dx"],demand_data["dy"] = demand_data.geometry.x,demand_data.geometry.y
        demand_data["did"] = demand_data.index
        joint_data = demand_data.merge(supply_data,how="cross")
        joint_data["distance"] = joint_data.apply(get_distance,axis=1)
        distance_matrix = joint_data.pivot_table(index="did",columns="sid",values="distance")
        d_matrix = distance_matrix.values

## calculate access score
        import aceso
        model = aceso.TwoStepFCA(radius=radius)
        demand_data['access_score'] = model.calculate_accessibility_scores(
            distance_matrix=d_matrix,
            demand_array=demand_data[d_va].values,
            supply_array = supply_data[s_va].values
        )

#  plot access score
        st.write(demand_data.head())
        # plot access score
        fig_c = plt.figure()
        ax = plt.gca()
        demand_data.plot(column='access_score', legend=True, ax=ax)
        st.pyplot(fig_c)
 # download access score
        demand_data.to_csv('access_score.csv', index=False)
        st.download_button(label='Download access score', data='access_score.csv', file_name='access_score.csv', mime='text/csv')
Py.Cafe

2sfca-analysis

2-Step Floating Catchment Area Analysis
