A talk presented by Nick Tierney, presented at UseR!2018
Twitter: @nj_tierney
GitHub: @njtierney
maxcovr R package: maxcovr
Want better wifi at the office? Improved access to healthcare? The maximal covering location problem (MCLP) can help! The MCLP finds optimal locations of facilities to improve their coverage on a set of targets. This means better placed wifi routers and healthcare facilities. Although the MCLP was described in the 1970s, it can be daunting to actually implement as you need to know how to:
- Formulate an optimisation problem
- Make it talk to a solver engine
- Get the data into the appropriate format for the solver to recognise
- Translate the model output into a usable format
It is challenging, particularly if you are not familiar with optimisation, or techniques such as linear programming. It is, however, a great use case for an R package to abstract away detail you don’t need to worry about. The R package maxcovr provides a set of tools to perform, summarise, and visualise the MCLP, so that you can move on with your analysis, place better cellphone towers, and create better access to health facilities.In this talk, I describe why the MCLP is useful, where it can be applied, and demonstrate of the use of maxcovr, before finally discussing future directions.
The source code for the slides can be found in
UseR2018-maxcovr-talk.Rmd, and you can view the slides as they were
presented as UseR,
here.
Thanks to the R Consortium, you can view the video of my talk here.
This talk drew upon publicly available datasets from the Brisbane government - from https://www.data.brisbane.qld.gov.au/. The datasets were initially suggested by friendly folks from the Stories with Data Slack channel, Alex Sadleir, and Dave A.
The data comes from the following repositories:
Each of the datasets, along with code for cleaning them up for their use
in the talk, and a README description of the data can be found in the
data-raw/
folder