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A data-package including the 2016 TTS origins, TTS destinations, number of jobs and workers at origin and destination, calculated travel times by car and a developed vignette demonstrating analysis which can be conducted using this package.

Home Page: https://soukhova.github.io/TTS2016R/

License: Creative Commons Attribution 4.0 International

R 23.22% TeX 76.78%
package r data tts employment jobs origin-destination spatial

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tts2016r's Issues

Geometries for various areas of interest

A nice addition would be simple features objects with the geometries of various areas of interest: the CMAs in the region, the planning districts, and so on. These could be obtained from the Census.

Update documentation

Update the documentation to give an brief but informative description of the tables. At the moment the description is simply a repeat of the name of the object.

image

Update the documentation to clarify that "jobs" is the number of people with full time jobs that work in the TAZ.

image

The table od_ft_tt uses "trips" but in the documentation it says "persons". I suggest keeping it as "persons" and clarify in the documentation that this table is a cross-tabulation of people by place of residence and place of employment. It is not actually trips, but a proxy for trips.

image

License

Update license information: suggest CC BY-NC-SA 4.0; see:

https://github.com/paezha/envsocty3LT3/blob/master/LICENSE

This is the snipet for the badge:

<!-- License --->
<a rel="license" href="http://creativecommons.org/licenses/by-nc-sa/4.0/"><img alt="Creative Commons License" style="border-width:0" src="https://i.creativecommons.org/l/by-nc-sa/4.0/88x31.png" /><br /></a>This work is licensed under a <a rel="license" href="http://creativecommons.org/licenses/by-nc-sa/4.0/">Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License</a><br />.

update bibliographic information

Final bibliographic information of paper is available now:

Soukhov, A., & Páez, A. (2023). TTS2016R: A data set to study population and employment patterns from the 2016 Transportation Tomorrow Survey in the Greater Golden Horseshoe area, Ontario, Canada. Environment and Planning B: Urban Analytics and City Science, 50(2), 556-563. doi:10.1177/23998083221146781

Add identifiers for CMAs

At the moment the tables lack a dictionary to other areas of interest, such as the CMAs in the region. Adding such ids would help to extract traffic analysis zones for regions of interest. In particular TTS does not seem to code the traffic analysis zones to the geography of CMAs which are important for planning purposes.

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