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j40-cejst-2/data/data-pipeline/data_pipeline/etl/sources/national_risk_index
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Travis Newby 6f39033dde
Add ability to cache ETL data sources (#2169) 
* Add a rough prototype allowing a developer to pre-download data sources for all ETLs

* Update code to be more production-ish

* Move fetch to Extract part of ETL
* Create a downloader to house all downloading operations
* Remove unnecessary "name" in data source

* Format source files with black

* Fix issues from pylint and get the tests working with the new folder structure

* Clean up files with black

* Fix unzip test

* Add caching notes to README

* Fix tests (linting and case sensitivity bug)

* Address PR comments and add API keys for census where missing

* Merging comparator changes from main into this branch for the sake of the PR

* Add note on using cache (-u) during pipeline
2023-03-03 12:26:24 -06:00
..
__init__.py Adds National Risk Index data to ETL pipeline (#549) 2021-09-07 20:51:34 -04:00
etl.py Add ability to cache ETL data sources (#2169) 2023-03-03 12:26:24 -06:00
README.md Adds National Risk Index data to ETL pipeline (#549) 2021-09-07 20:51:34 -04:00

README.md

FEMA National Risk Index

Description

The National Risk Index is a new, online mapping application from FEMA that identifies communities most at risk to 18 natural hazards. This application visualizes natural hazard risk metrics and includes data about expected annual losses from natural hazards, social vulnerability and community resilience.

The National Risk Index's interactive web maps are at the county and Census tract level and made available via geographic information system (GIS) services for custom analyses. For this project, we've utilized the NRI data collected at the Census tract level

Data Transformation Summary

The following transformations were applied to the NRI data during the ETL process:

  • The TRACTFIPS column was renamed to GEOID10_TRACT to match the name of columns that hold the Census Tract FIPS code in other data sets
  • The NRI score values for each Census tract were applied to each of the Census block groups inside of that Census tract so that the unit of analysis would match that of other datasets like the American Communities Survey