Registry / data / spatial-access

spatial-access

library1.0.2pypypiunverified

spatial-access is a Python package designed for measuring spatial accessibility to services. It provides tools to construct road networks, represent facilities and demand points, and compute various accessibility metrics like the Two-Step Floating Catchment Area (2SFCA) method. The current version is 1.0.2, and releases are made as needed to address issues and introduce features.

data

pip install spatial-access

github ↗package ↗

INSTALL

IMPORT

SIG · SPATIAL-ACCESS

spatial-access

datapythonv1.0.2

Install

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Import

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Disk

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Pass rate

0/ 10

Env Coverage0 / 10

glibc

3.9–3.13

musl

3.9–3.13

Install & Compatibility

Where this runs

tested against v? · pip install

Install × environment matrix

Each cell = how many times install + import succeeded across repeated harness runs. Partial = flaky.
glibc = Debian/Ubuntu slim · musl = Alpine Linux

musl

py 3.10–3.910 runs

build_error

glibc

py 3.10–3.910 runs

build_error

Code

Verified usage

Verified import paths — ran on the pinned version, not inferred.

spatial_access

✓ import spatial_access as sa

✗ import spatial-access

Python import statements use underscores, not hyphens, even if the PyPI package name uses hyphens.

Network

✓ network = sa.Network(nodes, edges, 'weight_col')

The Network class is typically accessed via the top-level spatial_access module alias.

Access

✓ access_model = sa.Access(network, facilities, demand, 'facility_col', 'demand_col')

The Access class is typically accessed via the top-level spatial_access module alias.

This quickstart demonstrates how to create a simple road network, define facilities and demand points using GeoDataFrames, and then compute spatial accessibility using the Two-Step Floating Catchment Area (2SFCA) method. It uses dummy data to ensure the example is runnable out-of-the-box.

from shapely.geometry import Point, LineString import geopandas as gpd import pandas as pd import spatial_access as sa # 1. Create dummy network data (nodes and edges) # Nodes represent intersections or endpoints nodes_df = pd.DataFrame({ 'id': [0, 1, 2, 3], 'geometry': [Point(0, 0), Point(1, 0), Point(0, 1), Point(1, 1)] }) nodes = gpd.GeoDataFrame(nodes_df, crs="EPSG:4326") # Edges represent road segments connecting nodes edges_df = pd.DataFrame({ 'id': [0, 1, 2, 3], 'from': [0, 0, 1, 2], 'to': [1, 2, 3, 3], 'length': [1.0, 1.0, 1.0, 1.0], # A 'weight' or 'cost' column 'geometry': [ LineString([(0, 0), (1, 0)]), LineString([(0, 0), (0, 1)]), LineString([(1, 0), (1, 1)]), LineString([(0, 1), (1, 1)]) ] }) edges = gpd.GeoDataFrame(edges_df, crs="EPSG:4326") # 2. Create dummy facility and demand data # Facilities (e.g., hospitals, stores) with capacity facilities_df = pd.DataFrame({ 'id': [0, 1], 'capacity': [10, 15], 'geometry': [Point(0.1, 0.1), Point(0.9, 0.9)] }) facilities = gpd.GeoDataFrame(facilities_df, crs="EPSG:4326") # Demand points (e.g., population centroids) with demand demand_df = pd.DataFrame({ 'id': [0, 1], 'demand': [5, 8], 'geometry': [Point(0.2, 0.2), Point(0.8, 0.8)] }) demand = gpd.GeoDataFrame(demand_df, crs="EPSG:4326") # 3. Initialize Network and Access objects # The 'length' column is used as the weight for shortest path calculations network = sa.Network(nodes, edges, "length") # Create the Access model, linking network, facilities, and demand access_model = sa.Access(network, facilities, demand, "capacity", "demand") # 4. Compute 2SFCA accessibility # Using a distance threshold of 10 units (arbitrary for dummy data) # and a Gaussian weight function. The result is (Si, Dj) where Si is # accessibility for demand points and Dj for facilities. print("Calculating 2SFCA accessibility...") accessibility_scores = access_model.two_sfca(10, weight_function="gaussian") # Print accessibility scores for demand points print("\nAccessibility scores for demand points (Si):") print(accessibility_scores[0].head())

Debug

Known issues

breakingPrior to version 1.0.2, the internal C++ Dijkstra implementation could crash when the input road network contained multiple disconnected components, leading to unexpected program termination.

fix

Upgrade to version 1.0.2 or newer. Alternatively, ensure your input network is a single connected component or filter to the largest connected component before processing.

affects: <1.0.2

gotchaDropping non-existent columns from a road network GeoDataFrame would previously raise an error, interrupting the workflow.

fix

Upgrade to version 1.0.2 or newer, which gracefully handles attempts to drop non-existent columns. For older versions, ensure all specified columns for removal actually exist in the DataFrame.

affects: <1.0.2

gotchaCoordinate Reference System (CRS) mismatches between input GeoDataFrames (nodes, edges, facilities, demand) can lead to incorrect distance calculations or errors during spatial operations. All input GeoDataFrames must share the same CRS.

fix

Always ensure that `nodes`, `edges`, `facilities`, and `demand` GeoDataFrames have a consistent CRS. Reproject them if necessary using `.to_crs()` before creating `Network` or `Access` objects. For example: `gdf.to_crs('EPSG:26918')` for projected coordinates suitable for distance calculations.

affects: All versions

gotchaThe `spatial_access.Network` object expects specific column names for weights (e.g., 'length', 'travel_time') and the `Access` object expects 'capacity' and 'demand' columns. Using incorrect column names will result in `KeyError`.

fix

Verify that the column names passed to `sa.Network` and `sa.Access` constructors (e.g., `weight_col`, `facility_col_name`, `demand_col_name`) exactly match the column names in your input GeoDataFrames.

affects: All versions

Upgrade

Version history

1.0.2latest on PyPI · released Jan 11, 2022

Audit

Dependencies

geopandasrequiredFundamental for handling geographic data structures (GeoDataFrames) for networks, facilities, and demand points.

pandasrequiredUsed for data manipulation and integration with GeoDataFrames.

networkxrequiredPowers the underlying graph representation and algorithms for network analysis.

Agent activity

3 hits · last 30 days

node

OpenAI (training)

Resources

githubhttps://github.com/GeoDaCenter/spatial_access ↗

packagehttps://pypi.org/project/spatial-access/ ↗