r2sfca 1.1.3

Reconciled Two-Step Floating Catchment Area Model for spatial accessibility analysis

r2SFCA: Reconciled Two-Step Floating Catchment Area Model

A Python package for spatial accessibility analysis that reconciles 2SFCA and i2SFCA methods through distance decay parameterization and cross-entropy minimization.

Authors: Lingbo Liu ([e-mail]), Fahui Wang ([e-mail])

Overview

The r2SFCA package implements a unified framework for spatial accessibility analysis that:

• Reconciles demand-side (2SFCA) and supply-side (i2SFCA) accessibility measures
• Optimizes distance decay parameters through cross-entropy minimization
• Supports multiple decay functions with configurable parameters
• Provides comprehensive evaluation metrics and visualization tools
• Enables grid search and Adam optimization for parameter estimation

Installation

pip install r2sfca

Quick Start

import pandas as pd
from r2sfca import R2SFCA

# Load your spatial accessibility data
df = pd.read_csv('your_data.csv')

# Initialize the R2SFCA model
model = R2SFCA(
    df=df,
    demand_col='Demand',
    supply_col='Supply',
    travel_cost_col='TravelCost',
    demand_id_col='DemandID',
    supply_id_col='SupplyID',
    observed_flow_col='O_Fij',  # Optional, for validation
    decay_function='exponential'
)

# Calculate accessibility and crowdedness scores
accessibility = model.access_score(beta=2.5)
crowdedness = model.crowd_score(beta=2.5)

# Perform grid search to find optimal parameters
results = model.search_fij(
    beta_range=(0.0, 5.0, 0.1),
    # param2_range = (10, 20.0, 5.0),#optional for sigmoid and gaussian function
    # either parameter can be a fixed value
    metrics=['cross_entropy', 'correlation', 'rmse']
)

# Optimize parameters using Adam optimizer
optimization_result = model.solve_beta(
    metric='cross_entropy',
    # param2=20.0, #optional for sigmoid and gaussian function
    method='adam',
    num_epochs=400
)

Data Requirements

Your input dataframe should contain the following columns:

• Demand: Demand values (e.g., population)
• Supply: Supply values (e.g., service capacity)
• TravelCost: Travel cost/distance between demand and supply locations
• DemandID: Unique identifiers for demand locations
• SupplyID: Unique identifiers for supply locations
• O_Fij (optional): Observed flow values for model validation

Decay Functions

The package supports six distance decay functions:

1. Exponential Decay

f(d) = exp(-β * d)

2. Power Decay

f(d) = d^(-β)

3. Sigmoid Decay

f(d) = 1 / (1 + exp(steepness * (d - β)))

4. Square Root Exponential Decay

f(d) = exp(-β * sqrt(d))

5. Gaussian Decay

f(d) = exp(-β * (d/d0)²)

6. Log-Squared Decay

f(d) = exp(-β * log(d)²)

API Reference

R2SFCA Class

Constructor

R2SFCA(df, demand_col, supply_col, travel_cost_col, demand_id_col, supply_id_col, 
       observed_flow_col=None, decay_function='exponential', epsilon=1e-15)

Methods

dist_decay(beta, **kwargs)

Calculate distance decay values using the specified decay function.

Parameters:

• beta: Primary decay parameter
• **kwargs: Additional parameters (steepness for sigmoid, d0 for gaussian)

Note: Uses the travel_cost data stored in the model during initialization.

Returns: Decay values

fij(beta, **kwargs)

Calculate Fij values (demand-side accessibility) using 2SFCA method.

Parameters:

• beta: Decay parameter
• **kwargs: Additional parameters for decay function

Returns: Fij values

tij(beta, **kwargs)

Calculate Tij values (supply-side accessibility) using i2SFCA method.

Parameters:

• beta: Decay parameter
• **kwargs: Additional parameters for decay function

Returns: Tij values

search_fij(beta_range, param2_range=None, metrics=None, normalize=True)

Perform grid search over parameter ranges to find optimal values.

Parameters:

• beta_range: (start, end, step) for beta parameter
• param2_range: (start, end, step) for second parameter
• metrics: List of evaluation metrics to calculate
• normalize: Whether to normalize Fij and Tij for cross-entropy calculation

Returns: DataFrame with grid search results

solve_beta(metric='cross_entropy', param2=None, method='minimize', **kwargs)

Solve for optimal beta parameter using optimization.

Parameters:

• metric: Metric to optimize
• param2: Second parameter value
• method: Optimization method ('minimize' or 'adam')
• **kwargs: Additional optimization parameters

Returns: Dictionary with optimization results

access_score(beta, **kwargs)

Calculate accessibility scores (Ai) for each demand location.

Parameters:

• beta: Decay parameter
• **kwargs: Additional parameters for decay function

Returns: Series with accessibility scores

crowd_score(beta, **kwargs)

Calculate crowdedness scores (Cj) for each supply location.

Parameters:

• beta: Decay parameter
• **kwargs: Additional parameters for decay function

Returns: Series with crowdedness scores

Evaluation Metrics

The package provides several evaluation metrics:

• Cross Entropy: Measures the difference between Fij and Tij distributions
• Correlation: Pearson correlation coefficient between Fij and Tij
• RMSE: Root Mean Square Error
• MSE: Mean Square Error
• MAE: Mean Absolute Error
• Fij-Flow Correlation: Correlation between estimated Fij and observed flows
• Tij-Flow Correlation: Correlation between estimated Tij and observed flows

Visualization

Grid Search Results

from r2sfca.utils import plot_grid_search_results

# Plot grid search results
fig = plot_grid_search_results(
    results_df=results,
    x_col='beta',
    y_cols=['cross_entropy', 'correlation'],
    title='Grid Search Results',
    save_path='grid_search.png'
)

Model Comparison

from r2sfca.utils import plot_model_comparison

# Compare multiple models
fig = plot_model_comparison(
    results_dfs=[results1, results2, results3],
    labels=['Gaussian', 'Exponential', 'Power'],
    y_col='fij_flow_correlation',
    title='Model Comparison',
    save_path='model_comparison.png'
)

Summary Table

from r2sfca.utils import create_summary_table

# Create summary table
summary = create_summary_table(
    results_dfs=[results1, results2, results3],
    labels=['Gaussian', 'Exponential', 'Power'],
    metric='cross_entropy',
    minimize=True
)

Examples

Example 1: Basic Usage

import pandas as pd
import numpy as np
from r2sfca import R2SFCA

# Create sample data
np.random.seed(42)
n_demand = 100
n_supply = 50

data = []
for i in range(n_demand):
    for j in range(n_supply):
        data.append({
            'DemandID': i,
            'SupplyID': j,
            'Demand': np.random.poisson(1000),
            'Supply': np.random.poisson(100),
            'TravelCost': np.random.exponential(10),
            'O_Fij': np.random.poisson(50)
        })

df = pd.DataFrame(data)

# Initialize model
model = R2SFCA(
    df=df,
    demand_col='Demand',
    supply_col='Supply',
    travel_cost_col='TravelCost',
    demand_id_col='DemandID',
    supply_id_col='SupplyID',
    observed_flow_col='O_Fij',
    decay_function='gaussian'
)

# Calculate accessibility scores
accessibility = model.access_score(beta=2.0)
print(f"Accessibility scores: {accessibility.describe()}")

Example 2: Parameter Optimization

# Grid search
results = model.search_fij(
    beta_range=(0.0, 5.0, 0.2),
    metrics=['cross_entropy', 'correlation', 'rmse']
)

# Find optimal beta
optimal_idx = results['cross_entropy'].idxmin()
optimal_beta = results.loc[optimal_idx, 'beta']
print(f"Optimal beta: {optimal_beta}")

# Adam optimization
optimization_result = model.solve_beta(
    metric='cross_entropy',
    method='adam',
    num_epochs=200
)
print(f"Optimized beta: {optimization_result['optimal_beta']}")

Example 3: Model Comparison

# Compare different decay functions
decay_functions = ['exponential', 'power', 'gaussian', 'sigmoid']
results_list = []
labels = []

for decay_func in decay_functions:
    model_temp = R2SFCA(
        df=df,
        demand_col='Demand',
        supply_col='Supply',
        travel_cost_col='TravelCost',
        demand_id_col='DemandID',
        supply_id_col='SupplyID',
        observed_flow_col='O_Fij',
        decay_function=decay_func
    )
    
    results = model_temp.search_fij(
        beta_range=(0.0, 3.0, 0.1),
        metrics=['cross_entropy', 'fij_flow_correlation']
    )
    
    results_list.append(results)
    labels.append(decay_func.title())

# Plot comparison
from r2sfca.utils import plot_model_comparison

fig = plot_model_comparison(
    results_dfs=results_list,
    labels=labels,
    y_col='fij_flow_correlation',
    title='Decay Function Comparison'
)

Advanced Usage

Custom Decay Function Parameters

# Gaussian decay with custom d0 parameter
model = R2SFCA(df, decay_function='gaussian')
fij = model.fij(beta=2.0, d0=30.0)  # Custom d0 value

# Sigmoid decay with custom steepness
model = R2SFCA(df, decay_function='sigmoid')
fij = model.fij(beta=1.5, steepness=5.0)  # Custom steepness

Multiple Parameter Optimization

# Grid search with second parameter
results = model.search_fij(
    beta_range=(0.0, 3.0, 0.1),
    param2_range=(1.0, 10.0, 0.5),  # For sigmoid steepness
    metrics=['cross_entropy', 'correlation']
)

Custom Evaluation Metrics

# Use custom metrics
results = model.search_fij(
    beta_range=(0.0, 3.0, 0.1),
    metrics=['cross_entropy', 'rmse', 'mae', 'fij_flow_correlation']
)

Performance Considerations

• For large datasets, consider using the Adam optimizer instead of grid search
• The package uses vectorized operations for efficiency
• Memory usage scales with the number of demand-supply pairs
• Consider sampling for very large datasets during parameter optimization

Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

License

This project is licensed under the MIT License - see the LICENSE file for details.

Citation

If you use this package in your research, please cite:

@article{r2sfca2025,
   author = {Liu, Lingbo and Wang, Fahui},
   title = {Reconciling 2SFCA and i2SFCA via distance decay parameterization},
   journal = {International Journal of Geographical Information Science},
   pages = {1-18},
   note = {doi: 10.1080/13658816.2025.2562255},
   ISSN = {1365-8816},
   DOI = {10.1080/13658816.2025.2562255},
   url = {https://doi.org/10.1080/13658816.2025.2562255},
   year = {2025},
   type = {Journal Article}
}

Support

For questions and support, please open an issue on GitHub or contact the development team.