{"categories":[{"label":"GIS","url":"https://skillfed.io/packages/category/scientific-engineering-gis"}],"enrichment":{"capability":"SpInt calibrates spatial interaction models (gravity, production-constrained, attraction-constrained, and doubly-constrained) using an entropy-maximizing framework and iteratively weighted least squares, with support for Poisson and QuasiPoisson count models.","skillfed_tags":["spatial-analysis","gravity-models","count-data"],"use_cases":["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."],"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.\n\nThe package is designed for researchers studying spatial interaction processes\u2014such 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.","worth_installing":"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."},"id":"spint","links":{"html":"https://skillfed.io/packages/spint","md":"https://skillfed.io/packages/spint.md","pypi":"https://pypi.org/project/spint/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2026-06-17","license_spdx":null,"license_treatment":"permissive","name":"spint","python_support":"supports_current","summary":"SPatial INTeraction models"},"popularity":{"monthly_downloads":101387,"position":12940,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"1.1.0"}