{"categories":[{"label":"GIS","url":"https://skillfed.io/packages/category/scientific-engineering-gis"}],"enrichment":{"capability":"Spaghetti analyzes network-based spatial data by building graph-theoretic networks from geographic features and computing network events, topology, and spatial inference on them.","skillfed_tags":["spatial-networks","graph-analysis","gis"],"use_cases":["Analyze transportation networks to find shortest routes or identify bottlenecks in road or transit systems.","Study utility infrastructure (power grids, water pipes) as spatial graphs to detect network fragmentation.","Compute network-constrained distances for spatial statistical tests that account for connectivity.","Build minimum spanning trees from geographic point or line data for network design problems.","Integrate network topology with spatial weights for neighborhood analysis along network edges."],"what_it_does":"Spaghetti is a Python library for analyzing spatial networks\u2014geographic data structured as graphs of connected line segments and nodes. It originated from PySAL's network module and provides tools for building networks from geographic features, computing network topology, finding shortest paths, and analyzing network events within a spatial context. The library integrates tightly with the PySAL ecosystem, allowing users to combine network analysis with spatial statistics (via esda), spatial weights (via libpysal), and geospatial operations (via geopandas and shapely).\n\nThe package is designed for spatial data scientists and researchers studying network-centric phenomena\u2014transportation networks, utility grids, social networks with geographic constraints, and similar structures where both topology and location matter. It depends on eight runtime packages including numpy, scipy, geopandas, and rtree, making it part of a larger geospatial Python stack rather than a standalone tool. Installation via conda-forge is recommended to avoid manual system dependency setup.","worth_installing":"Yes, if you work with geographic networks and need graph-theoretic analysis integrated with spatial statistics. The package is actively maintained, has no known vulnerabilities, and fits naturally into the PySAL ecosystem. Install via conda-forge to avoid dependency friction. Not necessary if you only need basic graph algorithms without spatial context."},"id":"spaghetti","links":{"html":"https://skillfed.io/packages/spaghetti","md":"https://skillfed.io/packages/spaghetti.md","pypi":"https://pypi.org/project/spaghetti/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2024-06-21","license_spdx":null,"license_treatment":"permissive","name":"spaghetti","python_support":"supports_current","summary":"Analysis of Network-constrained Spatial Data"},"popularity":{"monthly_downloads":101121,"position":12962,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"1.7.6"}