more detailed representation of physical space
Nobody has claimed this yet.
Assessment
- Difficulty
- 5/5
- Estimated time
- Over a week
- Newbie friendliness
- 15/100
- Issue type
- Feature
- Clarity
- Needs clarification
- Activity status
- Stale
- Tech stack
- r
- Domain
- machine-learning
Research direction
The issue does not name files, tests, or entry points. Start by reading the project’s existing representation of physical space and how it feeds transmission risk and farUVC installation costs. The issue describes a broad modeling change whose scope is still evolving; done would mean representing physical space more granularly to support these factors and any others the project identifies.
Written by the indexing model from the issue text.
Description
This is an issue in progress and will need to be updated as our understanding improves
Currently our representation of physical space is limited to size as defined by the number of people visiting that space. A more detailed representation of physical space feels important - for things like transmission risk (e.g. occupant density) and costs of farUVC installation (which will likely vary with square footage of the setting).
We'll need to build in a more granular and realistic representation of physical space taking these factors (and potentially others we haven't thought of) into account.
- Dominant language
- R
- Stars
- 8
- Forks
- 4
- PR merge metrics
- No merged PRs in 30d
Getting set up
This project ships no dev container, Dockerfile or contributing guide, so setting up is up to you: start from its README, and see our first-contribution guide for the general steps.
First steps
- Read the whole issue, then the project's contributing guide.
- Comment on the issue to say you are picking it up — it saves two people doing the same work.
- Fork the repository and make your change on a branch.
- Open a pull request that references the issue number.
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