Tagging a release so that version name is correct in docs.

v1.1.9

v1.1.9

Release v1.1.8

v1.1.8

Release v1.1.7

v1.1.7

Access v1.1.3

Version 1.1.3 (2021-01-31)

We closed a total of 10 issues (enhancements and bug fixes) through 2 pull requests, since our last release on 2020-07-12.

Issues Closed

• capitalize Access class (#10)
• Changes case on Access and Datasets classess (#15)
• Ensure that the meta import for access exposes intended classes and functions (#2)
• Confirm access version to freeze in meta (#3)
• sphinx enforced to install w/ packages (#11)
• The package does not depend on scipy (#12)
• Adjust dependenies (#13)
• Release on conda-forge (#1)

Pull Requests

• Changes case on Access and Datasets classess (#15)
• Adjust dependenies (#13)

The following individuals contributed to this release:

• Jamie Saxon
• Vidal Anguiano Jr
• Jkoschinsky

access 1.1.1

The PySAL spatial access package aims to make it easy for analysis to calculate measures of spatial accessibility. This work has traditionally had two challenges:

1. to calculate accurate travel time matrices at scale and
2. to derive measures of access using the travel times and supply and demand locations.

Our project addresses both of these challenges.

• We implement classic spatial access models, allowing easy comparison of methodologies and assumptions.
• We support spatial access research by providing pre-computed travel time matrices and share code for computing new matrices at scale.
• We also developed a simple web app that runs the package on Amazon Web Services, allowing users to explore results without installing the package. We think this is a fun new strategy for trying new analysis methods, and hope that it will make the package more accessible to professionals.

This access models implement include:

• Floating Catchment Areas (FCA): For each provider, this is the ratio of providers to clients within a given travel time to the provider (Huff 1963, Joseph and Bantock 1982, and Luo 2004).
• Two-Stage FCAs (2SFCA): Calculated in two steps for a given travel time to the provider: 1) for each provider, the provider-to-client ratio is generated, 2) for each point of origin, these ratios are then summed (Luo and Wang 2002, and Wang and Luo 2005).
• Enhanced 2SFCA (E2SFCA): 2SFCA but with less weight to providers that are still within the travel threshold but at larger distances from the point of origin (Luo and Qi 2009).
• Three-Stage FCA (3SFCA): adds distance-based allocation function to E2SFCA (Wan, Zou, and Sternberg, 2012).
• Rational Agent Access Model (RAAM) (Saxon and Snow 2019).
• Access Score: This is a weighted sum of access components like distance to provider and relative importance of provider type (Isard 1960).

The package is implemented as a single class with a number of helper functions. According to PySAL tradition, we have also developed a broad set of tutorials and examples.