Access

Latest version: v1.1.9

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1.1.8

1.1.7

1.1.3

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

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](https://access.readthedocs.io/en/latest/generated/access.access.html#access.access), allowing easy comparison of methodologies and assumptions.
- We support spatial access research by providing pre-computed [travel time matrices](https://access.readthedocs.io/en/latest/resources.html) and share code for computing new matrices at scale.
- We also developed a simple [web app](https://access.readthedocs.io/en/latest/app.html) 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](https://doi.org/10.2307/3144521), [Joseph and Bantock 1982](https://doi.org/10.1016/0277-9536(82)90428-2), and [Luo 2004](https://doi.org/10.1016/S1353-8292(02)00067-9)).
- 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](https://doi.org/10.1068/b29120), and [Wang and Luo 2005](https://doi.org/10.1016/j.healthplace.2004.02.003)).
- 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](https://doi.org/10.1016/j.healthplace.2009.06.002)).
- Three-Stage FCA (3SFCA): adds distance-based allocation function to E2SFCA ([Wan, Zou, and Sternberg, 2012](https://doi.org/10.1080/13658816.2011.624987)).
- Rational Agent Access Model (RAAM) ([Saxon and Snow 2019](https://doi.org/10.1080/24694452.2019.1629870)).
- 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](https://access.readthedocs.io/en/latest/tutorials.html).

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