skillfed

access

Calculate spatial accessibility metrics.

access v1.1.10.post3 104.4K downloads/30d#12,750 on PyPI27
Permissive license BSD 3-Clause Active released

What it is and what it does

Access is a spatial analysis package that computes measures of how accessible services are from different geographic locations. It builds on geopandas, numpy, pandas, and scipy to work with spatial data and calculate both established and novel accessibility metrics. The package is part of the PySAL ecosystem and targets researchers, educators, and developers working with geographic information systems and spatial statistics.

You would use this package when you need to quantify service accessibility—for instance, measuring how far residents are from hospitals, schools, or transit hubs, or analyzing whether service distribution is equitable across a region. It handles the spatial calculations required to turn geographic coordinates and service locations into accessibility indices.

Use it for:

  • Measure hospital or healthcare facility accessibility for urban planning and equity analysis.
  • Analyze public transit accessibility to identify underserved neighborhoods.
  • Evaluate school or educational facility distribution and reachability across a region.
  • Assess food desert conditions by measuring distance to grocery stores or markets.
  • Support research on spatial inequality and service distribution patterns.
  • Benchmark accessibility improvements after infrastructure changes.

Worth the install?

AI-flagged interpretation of the facts on this page — verify before relying

Calculates spatial accessibility metrics to measure how easily services can be reached from different locations using classical and novel methods.

Yes. The package is actively maintained, has no known vulnerabilities, installs with low friction, and uses a permissive license. It fills a specific niche in spatial analysis with a stable API and clear documentation. Install it if you need to compute accessibility metrics; skip it if your workflow doesn't involve measuring service proximity.

Install

access on PyPI

pip

pip install access

uv

uv add access

poetry

poetry add access

Installing access

Before you install

Low friction install with a pure Python wheel. Active maintenance with recent commits and a stable production-ready status.

License in practice

BSD 3-Clause permissive license allows commercial and private use with minimal restrictions.

Quickstart

pip install access

import access
# Use geopandas and pandas to load spatial data, then call accessibility measures
import geopandas as gpd
import pandas as pd

Requires Python 3.11 or later; geopandas and scipy must be installed for spatial operations.

Verify before relying

  • Specific accessibility measures available (classical vs. novel methods not detailed in excerpt)
  • Performance characteristics with large datasets or high-dimensional spatial data
  • Integration patterns with common GIS workflows beyond basic import

Package facts

License BSD 3-Clause (permissive)
Python support supports the current Python release (>=3.11)
Install friction low — pure-Python wheel
Runtime dependencies 4 — geopandas, numpy, pandas, scipy
Maintenance actively maintained — 245 days since the last release
Last repo commit
First released
Downloads 104,415/month — #12,750 on PyPI (30-day window, as of 2026-08-14)
Known vulnerabilities none known (OSV.dev, checked 2026-08-14)

Evidence: access-1.1.10.post3-py3-none-any.whl

Keywords: spatial, statistics, access

Development Status :: 5 - Production/StableIntended Audience :: DevelopersIntended Audience :: EducationIntended Audience :: Science/ResearchLicense :: OSI Approved :: BSD LicenseOperating System :: OS IndependentProgramming Language :: Python :: 3Topic :: Scientific/Engineering :: GIS

Tags

spatial accessibility metricsservice access distance analysisgeographic accessibility measurementspatial statistics GISlocation-based service reachaccessibility index calculationproximity analysis geospatial
spatial-analysisgisaccessibility-metrics

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