spint
SPatial INTeraction models
Decision gist · record as of 2026-08-14
Yes. SpInt is actively maintained, has no known vulnerabilities, low install friction, and fills a specialized but well-defined niche in spatial interaction modeling. The BSD 3-Clause license is permissive. Install it if you need to calibrate gravity or constrained spatial interaction models; skip it if your work does not involve origin-destination flow analysis.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires Python 3.12 or later; depends on scipy, numpy, and libpysal for spatial data structures.
- Low install friction; pure Python wheel.
- Actively maintained with a recent release (58 days old) and current Python 3.12+ support.
License · maintenance · safety
BSD 3-Clause (permissive) — BSD 3-Clause permissive license allows commercial and private use with minimal restrictions; attribution required.
last release 2026-06-17 (58 days) · last repo commit 2026-07-07 · 58 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 101,387 downloads/mo, #12,940 on PyPI
Alternatives
Verify before relying
pip install spint
import spint
from spint import gravity
# Calibrate a spatial interaction model with your origin-destination flow data- Whether local subset calibration produces parameter estimates suitable for direct mapping without post-processing.
- Performance characteristics when working with large origin-destination matrices or high-dimensional spatial weights.
- Current status of Spatial Autoregressive (Lag) model implementation listed as 'In Progress'.
What it is and what it does
SpInt is a spatial interaction modeling library that calibrates gravity-type models for analyzing flows between geographic origins and destinations. It implements the Wilson (1971) family of entropy-maximizing models, supporting unconstrained, production-constrained, attraction-constrained, and doubly-constrained variants. All models are fit using iteratively weighted least squares in a generalized linear modeling framework, with results verified against comparable R and statsmodels routines.
The package is designed for researchers studying spatial interaction processes—such as migration, trade, or commuting patterns. It provides Poisson and QuasiPoisson estimation, overdispersion tests, model fit statistics (including Moran's I for spatial autocorrelation), local subset calibration for mappable parameter estimates, and three types of spatial weights (origin-destination contiguity, network-based, and vector distance). Sparse data structures are used for memory efficiency and speed.
Use it for
- Calibrate gravity models to predict origin-destination flows (e.g., commuting, migration, or trade volumes) from population and distance data.
- Test spatial autocorrelation in interaction residuals using vector-based Moran's I to detect model misspecification.
- Fit production- or attraction-constrained models when total outflows or inflows are known but individual flows must be estimated.
- Generate mappable local parameter estimates and diagnostics by calibrating models on spatial subsets.
- Compare overdispersion across Poisson and QuasiPoisson specifications to assess model adequacy for count data.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes.
SpInt is actively maintained, has no known vulnerabilities, low install friction, and fills a specialized but well-defined niche in spatial interaction modeling. The BSD 3-Clause license is permissive. Install it if you need to calibrate gravity or constrained spatial interaction models; skip it if your work does not involve origin-destination flow analysis.
Install
spint on PyPI
Before you install
Low install friction; pure Python wheel. Actively maintained with a recent release (58 days old) and current Python 3.12+ support. Depends on established scientific stack (numpy, scipy, libpysal, spglm, spreg).
Requires Python 3.12 or later; depends on scipy, numpy, and libpysal for spatial data structures.
License in practice
BSD 3-Clause permissive license allows commercial and private use with minimal restrictions; attribution required.
Quickstart
pip install spint
import spint
from spint import gravity
# Calibrate a spatial interaction model with your origin-destination flow dataVerify before relying
- Whether local subset calibration produces parameter estimates suitable for direct mapping without post-processing.
- Performance characteristics when working with large origin-destination matrices or high-dimensional spatial weights.
- Current status of Spatial Autoregressive (Lag) model implementation listed as 'In Progress'.
Package facts
| License | BSD 3-Clause permissive |
| Python support | Supports the current Python release >=3.12 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 5 packageslibpysalnumpyscipyspglmspreg |
| Maintenance | Actively maintained 58 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 101,387 / month, #12,940 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Intended Audience :: Science/ResearchLicense :: OSI Approved :: BSD LicenseOperating System :: OS IndependentProgramming Language :: Python :: 3Topic :: Scientific/Engineering :: GIS |
Evidence: spint-1.1.0-py3-none-any.whl
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