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An open software environment to make spatial access metrics more accessible

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Abstract

This article introduces a new open software environment to support the measurement of a range of accessibility indices at scales going from the local to the national. In practice, the use of such indices has been impeded by the lack of open resources and the computational burden associated with large scale analyses. The environment consists of three parts: a new package, access, as part of the Python-based PySAL Spatial Analysis Library, a user-friendly point-and-click web implementation of the access computations, and support for the calculation of large-scale travel cost matrices, including a set of pre-computed origin-destination distance matrices for all the census tracts in the U.S. and census blocks in the 20 major cities. All three elements are open source and free to use. After motivating the development of the software environment, and situating the problem of access measurement in the literature, we briefly describe six commonly used access metrics. We then discuss in more detail the three important components of our software infrastructure. We close with an empirical illustration pertaining to access to health care providers, comparing the approach in the package to that taken in the web application.

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Notes

  1. The code and documentation can be found at https://github.com/pysal/access and https://access.readthedocs.io/.

  2. Extensive documentation on how to use the web application is available at https://access.readthedocs.io/en/latest/tutorials.html.

  3. https://github.com/ATFutures/dodgr.

  4. https://access.readthedocs.io/en/latest/resources.html.

  5. This package was developed by Logan Noel and is available at https://pypi.org/project/spatial-access/.

  6. https://access.readthedocs.io/en/latest/tutorials.html.

  7. https://access.readthedocs.io/en/latest/api.html.

  8. https://access.readthedocs.io/en/latest/tutorials.html#tutorials.

  9. https://access.readthedocs.io/en/latest/resources.html.

  10. A more detailed version of this example can be found in the Jupyter notebook of the tutorial section of the website https://bit.ly/3sFVvNK.

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Acknowledgements

This open data and analytics infrastructure for the quantification of spatial access has many components that a large team of research assistants and staff at the University of Chicago’s Center for Spatial Data Science contributed to over the past years. In particular, we acknowledge the contributions of Logan Noel, Irene Farah, Xun Li, George Oliver, Caitlyn Tien, Richard Lu, Larissa Vieira, Yair Atlas and Bryan Wang.

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Authors and Affiliations

  1. University of Chicago, Chicago, USA

    James Saxon, Vidal Anguiano & Luc Anselin

  2. Center for Spatial Data Science, University of Chicago, 1155 E 60th St, Rm 212B, Chicago, IL, 60627, USA

    Julia Koschinsky

  3. Cornell University, Ithaca, USA

    Karina Acosta

  4. University of California, Riverside, USA

    Sergio Rey

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  1. James Saxon
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Correspondence to Julia Koschinsky.

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Saxon, J., Koschinsky, J., Acosta, K. et al. An open software environment to make spatial access metrics more accessible. J Comput Soc Sc 5, 265–284 (2022). https://doi.org/10.1007/s42001-021-00126-8

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