{"categories":[{"label":"Information Analysis","url":"https://skillfed.io/packages/category/scientific-engineering-information-analysis/3"}],"enrichment":{"capability":"Computes network centrality, land-use accessibility, mixed-use indices, and pedestrian-flow analysis on street networks extracted from OpenStreetMap or custom graphs.","skillfed_tags":["urban-analysis","network-centrality","geospatial"],"use_cases":["Compute network centrality measures on street networks to identify structurally important locations.","Assess land-use accessibility and mixed-use diversity around points of interest using network-distance thresholds.","Analyze pedestrian-flow demand and origin-destination betweenness to understand movement patterns.","Generate multi-variable urban datasets combining centrality and accessibility for machine learning.","Perform high-resolution moving-window urban morphometric analysis with network-based distance constraints."],"what_it_does":"Cityseer is a Python package for pedestrian-scale urban network analysis, combining network topology, geospatial data, and statistical methods to measure urban form and accessibility. It computes network centrality metrics (including harmonic closeness and segment-weighted variants) on primal or dual graphs, calculates land-use accessibility and mixed-use indices with dynamic aggregation, and performs pedestrian-flow betweenness analysis. The package integrates with the NumPy ecosystem (NetworkX, GeoPandas, Shapely, Rasterio) and uses Rust-backed algorithms for computationally intensive loops, enabling analysis of large or decomposed street networks.\n\nTypical workflows involve extracting street networks from OpenStreetMap via osmnx, assigning land-use or other contextual data to street segments, and computing multi-scalar metrics from selected analysis points. The package supports high-resolution, moving-window analysis with strict network-distance thresholds and facilitates downstream machine learning and data science tasks by generating multi-variable datasets. It is designed for urbanists, researchers, and planners conducting computational urban analytics.","worth_installing":"Yes, if you are conducting pedestrian-scale urban network analysis or research. The package is actively maintained, supports current Python versions, has no known vulnerabilities, and integrates well with standard geospatial tools. The AGPL-3.0 license is permissive for open-source and research use but requires review for proprietary integration. Medium install friction is acceptable for specialized geospatial work. Not suitable if you need a lightweight, minimal-dependency tool or are restricted to permissive licenses."},"id":"cityseer","links":{"html":"https://skillfed.io/packages/cityseer","md":"https://skillfed.io/packages/cityseer.md","pypi":"https://pypi.org/project/cityseer/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2026-07-31","license_spdx":null,"license_treatment":"agpl","name":"cityseer","python_support":"supports_current","summary":"Computational tools for network-based pedestrian-scale urban analysis"},"popularity":{"monthly_downloads":126803,"position":11766,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"5.8.0"}