[Paper] huff: A Python package for Market Area Analysis

Source: arXiv - 2602.17640v1

Overview

The new huff Python package bundles everything you need to run market‑area (Huff) models—from raw data ingestion to visualizing catchment‑area maps. By turning a traditionally academic, spreadsheet‑heavy workflow into a clean, object‑oriented library, the author makes spatial market‑share and accessibility analysis instantly usable for developers, data scientists, and GIS professionals.

Key Contributions

• End‑to‑end workflow: single‑call functions for data import, OD‑matrix creation, model calibration, and result visualisation.
• Modular, object‑oriented design: core classes (HuffModel, Accessibility, ODMatrix) can be extended or combined with other Python GIS tools.
• Parameter estimation utilities: built‑in maximum‑likelihood and Bayesian routines to fit Huff model parameters directly from observed transaction or visitation data.
• Travel‑time/distance handling: seamless integration with networkx, osmnx, or custom cost surfaces for realistic impedance measures.
• Spatial accessibility metrics: implements several health‑geography indices (e.g., Two‑Step Floating Catchment, Enhanced Huff) alongside classic market‑share outputs.
• Open‑source distribution: published on PyPI, version‑controlled on GitHub, and archived on Zenodo for reproducibility.

Methodology

1. Data Ingestion – Users feed CSV, GeoJSON, or PostGIS tables containing origin (e.g., residential zones) and destination (e.g., stores, hospitals) attributes.

2. OD‑Matrix Construction – The library computes an origin‑destination matrix using either Euclidean distance, road‑network travel time, or any user‑supplied cost matrix.

3. Huff Model Core – For each destination j and origin i, the probability of a consumer choosing j is calculated as

   [ P_{ij}= \frac{S_j^\alpha , e^{-\beta , c_{ij}}}{\sum_{k} S_k^\alpha , e^{-\beta , c_{ik}}} ]

   where S is the size (e.g., floor area, bed count), c the impedance, and α, β are tunable parameters.

4. Parameter Estimation – The package offers:

   • MLE: maximises the likelihood of observed trips given α and β.
   • Bayesian: uses pymc3/pymc to draw posterior samples, providing uncertainty bounds.

5. Accessibility & Catchment Analysis – Implements extensions such as the Two‑Step Floating Catchment Area (2SFCA) and a “threshold‑based” Huff variant for health‑service planning.

6. Visualization – Results are exported as GeoDataFrames and plotted with matplotlib, geopandas, or interactive folium/kepler.gl maps.

All steps are encapsulated in high‑level methods (run(), fit(), plot_map()) while still allowing low‑level access for custom pipelines.

Results & Findings

• Benchmarking on a synthetic retail dataset showed that the built‑in MLE estimator recovers true α and β within 2 % error, matching hand‑crafted Excel solutions but in a fraction of the time.
• Case study – healthcare accessibility in a mid‑size German region demonstrated that the extended Huff model predicts patient flows with an R² of 0.78, outperforming a simple distance‑decay model (R² = 0.62).
• Performance: generating a 10 k origin × 500 destination OD matrix with road‑network travel times completes in ~3 seconds on a standard laptop (Intel i7, 16 GB RAM).
• Reproducibility: the authors provide a Dockerfile and Jupyter notebooks that reproduce all experiments, confirming the package’s stability across Python 3.9–3.12.

Practical Implications

• Retail & Marketing – Quickly estimate market share for new store locations, run “what‑if” scenarios (e.g., changing store size or opening hours), and integrate results into A/B testing pipelines.
• Urban & Regional Planning – Use the accessibility modules to evaluate public‑service coverage, identify underserved neighborhoods, and support evidence‑based zoning decisions.
• Health‑Service Management – Model patient catchments for hospitals or clinics, assess the impact of new facilities, and feed results into capacity‑planning dashboards.
• Data‑Science Workflows – Because the library returns tidy pandas/geopandas objects, it plugs directly into ML pipelines, enabling feature engineering (e.g., “probability of visiting a competitor”) for churn prediction or demand forecasting.
• Open‑Source Collaboration – The modular design encourages contributions (e.g., adding multimodal travel impedance, integrating with kepler.gl for 3‑D visualisation), fostering a community around spatial market‑analysis tools.

Limitations & Future Work

• Scalability – While efficient for tens of thousands of origins, the current implementation can hit memory limits on nation‑scale OD matrices; sparse‑matrix support is planned.
• Static Impedance – The package assumes time‑invariant travel costs; dynamic congestion or public‑transport schedules are not yet incorporated.
• Model Extensions – Only the classic Huff formulation and a few health‑geography variants are included; extensions such as gravity‑type competition or agent‑based simulation are left to future releases.
• Validation Scope – Empirical validation is limited to retail and German health‑care case studies; broader cross‑industry benchmarks would strengthen generalizability.

The author outlines upcoming work on GPU‑accelerated matrix operations, integration with pyproj for multi‑CRS handling, and a plug‑in system for custom utility functions.

Authors

• Thomas Wieland

Paper Information

• arXiv ID: 2602.17640v1
• Categories: stat.AP, cs.SE
• Published: February 19, 2026
• PDF: Download PDF