Describe the smoke alarm model.

[nbanion]

@kelsonSS, may I have your help documenting the smoke alarm model?

The goal is to enable a newcomer to understand the purpose, approach, and basic mechanics of the model so that they could get started working on it. Details welcome. We'll add your content to the documentation where it'll help all users leverage your work. Here's a sneak peek from my own rcp2 fork.

A few key questions. Feel free to add anything else that's pertinent.

• What does the model predict?
• What scripts / notebooks are involved?
• What data sources do you use?
• What are the dimensions of the data?
• What does each record represent?
• What years do you use?
• What is the outcome variable?
• What features do you use?
• What modeling approach do you use?
• How do you transform the raw data?
• When / how often will more data become available?
• Any other important details?

I'm happy to wordsmith or discuss 1:1. Thanks for your help.

[nbanion]

Some notes from our discussion. Will add to the documentation.

• statistical model: % HH with smoke detectors at block group level.
• Red Cross 500k visits over 5 years. Check for smoke detectors.
• Outcome variable: # working detectors. We want at least one.
• Mean for the block group w/ confidence interval.
• Red Cross records are houses. We roll up to block groups.
• Hierarchical estimation strategy.
  • State, county, tract, block group.
  • Choose the finest granularity with threshold of data points (i.e. 30).
• Output: block-level estimates, along with CI and source geography.
• Speed issues when running the code.
• The notebook: code/models/smoke_alarm_model
• HHs are geocoded to block groups in the raw data-- we take them as given.
• Processing: We remove cases that don't have the right data.
• We use all available data. Can see available years in the data.
• Continue adding data to the same estimates. Potentially crowdsource.