access-lib 0.1.0

Multimodal E2SFCA healthcare accessibility analysis with specialisation decomposition

access_lib

Multimodal E2SFCA Healthcare Accessibility Analysis

A Python library for evaluating spatial accessibility of healthcare facilities using the Enhanced Two-Step Floating Catchment Area (E2SFCA) method with multimodal transport decomposition, medical specialisation layers, and M/M/1 queue correction.

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Key Features

• Multimodal transport — car, walking, and public transit with independent catchment radii and Gaussian decay
• Specialisation decomposition — four healthcare layers: primary care, paediatrics, specialist outpatient, inpatient (hospital)
• M/M/1 queue correction — capacity adjustment based on single-server queuing theory
• Four policy scenarios — telemedicine boost, new hospital placement, road closure stress test, real facility opening
• Inequality metrics — Gini coefficient, Lorenz curves, Moran's I spatial autocorrelation
• Monte Carlo simulation — parameter uncertainty quantification
• Sensitivity analysis — systematic exploration of decay parameter (β) and catchment radius (r)
• Publication-ready maps — settlement choropleths, building-level quintile maps, before/after scenario maps, delta maps

Study Area

Vsevolozhsky District, Leningrad Oblast, Russia — 19 settlements, 589,000 residents, 49 healthcare facilities.

Installation

From PyPI

pip install access-lib

From source (development)

git clone https://github.com/Lizatol/access_lib.git
cd access_lib
pip install -e ".[full,dev]"

Optional dependencies

# Spatial statistics (Moran's I, LISA)
pip install access-lib[spatial]

# Interactive Folium maps
pip install access-lib[interactive]

# Everything
pip install access-lib[full]

Quick Start

from access_lib.config import Config
from access_lib.core import composite_accessibility, gini

# Load configuration and data
cfg = Config()
# ... load GeoPackage files ...

# Compute multimodal E2SFCA
A = composite_accessibility(
    od_matrices=OD_matrices,
    demand=demand,
    supply=supply,
    params=cfg.e2sfca,
    mode_weights=cfg.mode_weights.as_dict(),
)

print(f"Mean accessibility: {A.mean():.5f}")
print(f"Gini coefficient:   {gini(A):.3f}")

See examples/walkthrough.ipynb for a complete end-to-end analysis.

Method

E2SFCA with Gaussian Decay

The Enhanced Two-Step Floating Catchment Area method computes an accessibility index for each demand point (residential building):

Step 1 — Supply-to-demand ratio:

$$R_j = \frac{S_j}{\sum_{k \in {d_{kj} \le r}} D_k \cdot W(d_{kj}, \beta, r)}$$

Step 2 — Accessibility index:

$$A_i = \sum_{j \in {d_{ij} \le r}} R_j \cdot W(d_{ij}, \beta, r)$$

Where $W(t, \beta, r) = \exp\left(-\left(\frac{t}{\sigma}\right)^2\right)$ is the Gaussian decay function with $\sigma = r \cdot 0.5 / \sqrt{\beta}$.

Multimodal Composite

$$A_{\text{composite}} = w_{\text{car}} \cdot A_{\text{car}} + w_{\text{walk}} \cdot A_{\text{walk}} + w_{\text{pt}} \cdot A_{\text{pt}}$$

Default weights: car 40%, walking 20%, public transit 40%.

Mode	Catchment radius	Decay β
Car	60 min	1.5
Walking	30 min	1.0
Public transit	90 min	1.2

Specialisation Decomposition

Healthcare facilities are grouped into four specialisation layers, each computed independently:

Layer	Facility types
Primary care	Therapist clinics, FAPs
Paediatrics	Children's polyclinics
Specialist outpatient	Outpatient clinics, maternal health
Inpatient (hospital)	Full-service hospitals

The hospital layer includes M/M/1 queue correction: effective capacity is reduced by $\exp(-\alpha \cdot W_q)$ where $W_q = \rho / (\mu(1-\rho))$.

Scenarios

Scenario	Description	Effect
S1: Telemedicine	Distance-independent virtual care capacity	+19% mean, Gini −0.09
S2: New hospitals	3 hospitals in worst-served settlements (greedy placement)	+170% mean, Gini −0.21
S3: Road closure	~10 km closure in Toksovo + Bugrovo (stress test)	Localised degradation
S4: Real facility	Actual planned hospital opening	+1.2% mean

Architecture

access_lib/
├── config.py              # Configuration dataclasses
├── core/
│   ├── engine.py          # E2SFCA computation engine
│   ├── demand.py          # Population distribution to buildings
│   ├── facilities.py      # Facility processing and capacity
│   ├── graph.py           # Road network graph construction
│   ├── od_matrix.py       # Origin-Destination matrix computation
│   ├── aggregation.py     # Building → settlement aggregation
│   ├── specialization.py  # Healthcare layer decomposition
│   └── isochrones.py      # Isochrone analysis
├── scenarios/
│   ├── base.py            # ScenarioResult dataclass
│   ├── telemedicine.py    # S1: telemedicine boost
│   ├── new_hospital.py    # S2: optimal hospital placement (greedy)
│   ├── road_closure.py    # S3: road closure stress test
│   └── real_hospital.py   # S4: real facility opening
├── simulation/
│   ├── monte_carlo.py     # Monte Carlo uncertainty analysis
│   └── sensitivity.py     # Parameter sensitivity (β, r)
├── stats/
│   └── spatial.py         # Moran's I, LISA, spatial statistics
└── viz/
    ├── style.py           # Colour palette, constants
    ├── maps.py            # Cartographic visualisations
    ├── charts.py          # Statistical charts (Lorenz, bars, heatmaps)
    ├── method_diagram.py  # E2SFCA architecture diagram
    └── interactive.py     # Folium interactive maps

Data Requirements

Input data should be provided as GeoPackage (.gpkg) files:

File	Contents	Key columns
residential.gpkg	Building footprints	geometry, building_floors
facilities.gpkg	Healthcare facilities	geometry, specialization, capacity
boundaries.gpkg	Settlement boundaries	geometry, name, population_2025
roads.gpkg	Road network	geometry, highway, maxspeed
bus_stops.gpkg	Public transit stops	geometry

Sample data for Vsevolozhsky District is included in data/sample/.

Citation

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

@software{tolmacheva2026accesslib,
  author    = {Tolmacheva, Elizaveta M.},
  title     = {access\_lib: Multimodal E2SFCA Healthcare Accessibility Analysis},
  year      = {2026},
  url       = {https://github.com/Lizatol/access_lib},
  version   = {0.1.0},
}

References

• Luo, W., & Qi, Y. (2009). An enhanced two-step floating catchment area (E2SFCA) method for measuring spatial accessibility to primary care physicians. Health & Place, 15(4), 1100–1107.
• Dai, D. (2010). Black residential segregation, disparities in spatial access to health care facilities, and late-stage breast cancer diagnosis in metropolitan Detroit. Health & Place, 16(5), 1038–1052.
• McGrail, M. R., & Humphreys, J. S. (2014). Measuring spatial accessibility to primary health care services: Utilising dynamic catchment sizes. Applied Geography, 54, 182–188.

License

MIT License — free for academic and commercial use.

Author

Elizaveta Tolmacheva ITMO University, Institute of Design and Urban Studies Saint Petersburg, Russia