spatial-access
Decision gist · record as of 2026-08-14
No, unless maintaining legacy code. The package is abandoned (last update 2022-01-11, 1676 days without commits), carries 17 heavy dependencies prone to version conflicts, requires system-level compilation, and has no Python version support specification. For new projects, seek actively maintained alternatives. If you must use it, expect significant dependency resolution work and thorough testing.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires system-level spatial indexing library (libspatialindex-dev on Ubuntu, spatialindex via Homebrew on macOS) and a modern C/C++ compiler.
- Package is abandoned and may not install cleanly on Python versions released after 2022-01-11.
- Medium install friction: requires a modern compiler and system-level spatial indexing libraries (libspatialindex-dev on Ubuntu, spatialindex via brew on macOS).
License · maintenance · safety
BSD (permissive) — BSD permissive license allows commercial and private use with minimal restrictions, though abandoned maintenance status means no upstream support for license disputes.
last release 2022-01-11 (1676 days) · last repo commit 2022-01-11 · 40 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 152,147 downloads/mo, #10,910 on PyPI
Alternatives
Verify before relying
# Install system dependencies first (Ubuntu):
# sudo apt-get install libspatialindex-dev
# pip install spatial_access
from spatial_access.p2p import TransitMatrix
from spatial_access.Configs import Configs
config = Configs()
tm = TransitMatrix(origins, destinations, configs=config)
# Compute travel times for walking, biking, or driving- Compatibility with Python versions released after 2022-01-11 (wheels exist for cp36–cp310 but no newer versions)
- Whether all 17 runtime dependencies remain compatible with each other and modern releases
- Whether the package works on Windows despite documentation noting Windows users need Ubuntu VM setup
What it is and what it does
spatial_access is a geospatial analysis library that calculates travel times and accessibility metrics for large sets of coordinate pairs. It splits into two main submodules: p2p generates many-to-many travel time matrices across walking, biking, and driving networks, while Models computes spatial accessibility measures like average time to nearest provider, provider-to-people ratios, and two-stage floating catchment areas. The package is built on a heavy stack of geospatial and scientific dependencies (geopandas, shapely, rtree, scipy, scikit-learn) and requires compiled spatial indexing libraries at the system level.
The package is designed for bulk computation and is typically used in public health, urban planning, and equity research to measure access to amenities. However, the project has been abandoned since 2022-01-11 with no active maintenance, meaning dependency conflicts, Python version incompatibilities, and unresolved bugs are likely on modern systems.
Use it for
- Measure healthcare facility accessibility by computing average travel times from residential locations to nearest providers.
- Analyze food desert coverage by calculating provider-to-people ratios for grocery stores using floating catchment area methods.
- Evaluate transit equity by generating travel time matrices for walking and biking routes to employment centers.
- Assess school accessibility by computing catchment areas and counting nearby schools within travel time thresholds.
- Model geographic disparities in service access by comparing weighted accessibility scores across regions.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
No, unless maintaining legacy code.
The package is abandoned (last update 2022-01-11, 1676 days without commits), carries 17 heavy dependencies prone to version conflicts, requires system-level compilation, and has no Python version support specification. For new projects, seek actively maintained alternatives. If you must use it, expect significant dependency resolution work and thorough testing.
Install
spatial-access on PyPI
Before you install
Medium install friction: requires a modern compiler and system-level spatial indexing libraries (libspatialindex-dev on Ubuntu, spatialindex via brew on macOS). The package carries 17 runtime dependencies including heavy scientific stacks. Project is abandoned—last commit 2022-01-11, no updates in 1676 days—so dependency conflicts are a real risk.
Requires system-level spatial indexing library (libspatialindex-dev on Ubuntu, spatialindex via Homebrew on macOS) and a modern C/C++ compiler. Package is abandoned and may not install cleanly on Python versions released after 2022-01-11.
License in practice
BSD permissive license allows commercial and private use with minimal restrictions, though abandoned maintenance status means no upstream support for license disputes.
Quickstart
# Install system dependencies first (Ubuntu):
# sudo apt-get install libspatialindex-dev
# pip install spatial_access
from spatial_access.p2p import TransitMatrix
from spatial_access.Configs import Configs
config = Configs()
tm = TransitMatrix(origins, destinations, configs=config)
# Compute travel times for walking, biking, or drivingVerify before relying
- Compatibility with Python versions released after 2022-01-11 (wheels exist for cp36–cp310 but no newer versions)
- Whether all 17 runtime dependencies remain compatible with each other and modern releases
- Whether the package works on Windows despite documentation noting Windows users need Ubuntu VM setup
Package facts
| License | BSD permissive |
| Python support | Not specified |
| Install friction | Medium. Platform-specific wheel |
| Runtime dependencies | 17 packagesfionacythonmatplotlibjellyfishgeopandaspsutilpandasnumpyosmnetscipygeopyshapelytablesscikit-learnatlasdescartesrtree |
| Maintenance | Abandoned 1,676 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 152,147 / month, #10,910 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
Evidence: spatial_access-1.0.2-cp310-cp310-macosx_10_9_x86_64.whl; spatial_access-1.0.2-cp310-cp310-manylinux_2_17_i686.manylinux2014_i686.whl; spatial_access-1.0.2-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl; spatial_access-1.0.2-cp310-cp310-musllinux_1_1_i686.whl; spatial_access-1.0.2-cp310-cp310-musllinux_1_1_x86_64.whl; spatial_access-1.0.2-cp36-cp36m-macosx_10_9_x86_64.whl; spatial_access-1.0.2-cp36-cp36m-manylinux_2_17_i686.manylinux2014_i686.whl; spatial_access-1.0.2-cp36-cp36m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl; spatial_access-1.0.2-cp36-cp36m-musllinux_1_1_i686.whl; spatial_access-1.0.2-cp36-cp36m-musllinux_1_1_x86_64.whl; spatial_access-1.0.2-cp37-cp37m-macosx_10_9_x86_64.whl; spatial_access-1.0.2-cp37-cp37m-manylinux_2_17_i686.manylinux2014_i686.whl; spatial_access-1.0.2-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl; spatial_access-1.0.2-cp37-cp37m-musllinux_1_1_i686.whl; spatial_access-1.0.2-cp37-cp37m-musllinux_1_1_x86_64.whl; spatial_access-1.0.2-cp38-cp38-macosx_10_9_x86_64.whl; spatial_access-1.0.2-cp38-cp38-manylinux_2_17_i686.manylinux2014_i686.whl; spatial_access-1.0.2-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl; spatial_access-1.0.2-cp38-cp38-musllinux_1_1_i686.whl; spatial_access-1.0.2-cp38-cp38-musllinux_1_1_x86_64.whl
Tags
Let your AI agent find packages like this
Example. Real query, live index.
You found this page by searching. An agent finds it by wishing: SkillFed indexes 14,416 PyPI packages by what they can do, searchable in plain language.
wish › “travel time matrix computation”
- spatial-accessComputes travel times and spatial access metrics at scale for…
- stumpySTUMPY computes the matrix profile for time series data, enabling…
- time-machineMocks and freezes time in tests, allowing you to travel to any point…
Give your agent the search over MCP, or paste the wish link into any chat.
More Information Analysis packages
A drop-in replacement for Python's standard `re` module that adds advanced regex features like nested sets, fuzzy matching, lookaround in conditionals, and full Unicode case-folding while maintaining backward compatibility.
pyarrow provides Python bindings to Apache Arrow's C++ libraries for efficient columnar data processing, serialization, and interoperability with pandas, NumPy, and other Python ecosystem tools.
NetworkX provides data structures and algorithms for creating, analyzing, and manipulating graphs and networks, supporting everything from simple undirected graphs to complex directed and weighted networks.
Connects Python applications to Snowflake data warehouses using the DB API 2.0 specification, enabling SQL queries, data transfers, and warehouse operations.
ContourPy calculates contours of 2D quadrilateral grids using C++11 algorithms wrapped in Python, offering serial and multithreaded implementations without requiring Matplotlib as a dependency.
Snowpark Python provides APIs to query and process data directly in Snowflake without moving data to your local system, with support for both native Snowpark and pandas-compatible interfaces.
Install it if you use Snowflake and want to process data without moving it to your application layer.
See also access · segregation · giddy · spint · cityseer · spatialdata · libpysal · pysal · osmnx · arcgis