* First pass of updating documentation for new users
Trying to look at this from the perspective of someone new to the
project, and create some pathways to make it easier for people to get to
the content they are looking for.
* Make it clear that docker is doing the setup
* Link installation again from the main README
* Add some docs about the github actions
* Add markdown link check
* Move git installation first
* Add config for markdown link checker
* Fix some links
* Correct handling of repo root relative links
* Fix broken links in data roadmap
* Fix more broken links
* Fix more links
* Ignore link that's returning a 403 to the checker
It actually works if you go in a browser.
* Fix another broken link
* Ignore more urls that don't work
* Update the readme under docs
* Add some more dataset links
* More strongly call out the quickstart
* Try to call out even more the quickstart link
* Fix dead links
* Add note about initialization time
* Remove broken link from spanish install guide
These will be updated later with a full translation
* Update Side Panel Tile Data
* Update Side Panel Tile Data
* Correct indicator names to match csv
* Replace Score with Rate
* Comment out FEMA Loss Rate to troubleshoot
* Removes all "FEMA Loss Rate" array elements
* Revert FEMA to Score
* Remove expected loss rate
* Remove RMP and NPL from BASIC array
* Attempt to make shape mismatch align
- update README typo
* Add Score L indicators to TILE_SCORE_FLOAT_COLUMNS
* removing cbg references
* completes the ticket
* Update side panel fields
* Update index file writing to create parent dir
* Updates from linting
* fixing missing field_names for island territories 90th percentile fields
* Update downloadable fields and fix field name
* Update file fields and tests
* Update ordering of fields and leave TODO
* Update pickle after re-ordering of file
* fixing bugs in etl_score_geo
* Repeating index for diesel fix
* passing tests
* adding pytest.ini
Co-authored-by: Vim USDS <vimal.k.shah@omb.eop.gov>
Co-authored-by: Shelby Switzer <shelby.switzer@cms.hhs.gov>
Co-authored-by: lucasmbrown-usds <lucas.m.brown@omb.eop.gov>
* switching to low
* fixing score-etl-post
* updating comments
* fixing comparison
* create separate field for clarity
* comment fix
* removing healthy food
* fixing bug in score post
* running black and adding comment
* Update pickles and add a helpful notes to README
Co-authored-by: Shelby Switzer <shelby.switzer@cms.hhs.gov>
* Adds four fields:
* Summer days above 90F
* Percent low access to healthy food
* Percent impenetrable surface areas
* Low third grade reading proficiency
* Each of these four gets added into Definition L in various factors.
* Additionally, I add college attendance fields to the ETL for Census ACS.
* This PR also introduces the notion of "reverse percentiles", relevant to ticket #970.
* Update etl constants to use score field_names
Put strings around tract IDs in downloadable CSV
No need to modify the xls file creation because the string type is
preserved and interpreted correctly in Excel already.
One note is that this does cause the ID in the CSV to be have quotes
around it, which might be annoying. Maybe we don't want this behavior?
* Update based on PR feedback and lint needs
* Change field we're using in downloadable
This reverts the downloadable csv field list to use
MEDIAN_INCOME_AS_PERCENT_OF_STATE_FIELD instead of
MEDIAN_INCOME_AS_PERCENT_OF_AMI_FIELD in order to get the test to pass.
The point of this PR is a refactor (and a small change to the CSV
quotations), not to change the output. That will be a different PR
later.
Co-authored-by: Shelby Switzer <shelby.switzer@cms.hhs.gov>
* added fieldnames
* todo pollution, water, health & workforce
* workforce
* work in progress
* add utility function to replace duplicate summation logic
* move fpl series into add columns - run black .
* added revisions - still a wip
* added fieldnames
* todo pollution, water, health & workforce
* workforce
* work in progress
* add utility function to replace duplicate summation logic
* move fpl series into add columns - run black .
* added revisions - still a wip
* revise workforce and water
* revise housing and add incremental counter for workforce
* last PR nit
* revise workforce
* more PR feedback in score l
* more PR feedback in score l
* more PR feedback in score l
* addd FPL_SERIES and update references in score 1
* fix bugs
* reparameterize function
* final revisions in fieldnames
* make computations all consistent so we assing with FPL_200_SERIES
* fieldnames refactor after clarification and PR review
* finalize
* finalize with no typos
* fix length
* added median income var
* swap thresholds
* remove iteration
* remove stray '
* address flake 8
* added f string formatting and fixed typos
* added f string formatting and fixed typos
* move up
* remove dupes
* reformat
* fix bugs
* fix bugs
* initialize
Co-authored-by: Saran Ahluwalia <sarahluw@cisco.com>
* replace temporary fieldnames that are not found and indexed
* fixed field names
* PR review
* PR review - revert
Co-authored-by: Saran Ahluwalia <sarahluw@cisco.com>
This ended up being a pretty large task. Here's what this PR does:
1. Pulls in Vincent's data from island areas into the score ETL. This is from the 2010 decennial census, the last census of any kind in the island areas.
2. Grabs a few new fields from 2010 island areas decennial census.
3. Calculates area median income for island areas.
4. Stops using EJSCREEN as the source of our high school education data and directly pulls that from census (this was related to this project so I went ahead and fixed it).
5. Grabs a bunch of data from the 2010 ACS in the states/Puerto Rico/DC, so that we can create percentiles comparing apples-to-apples (ish) from 2010 island areas decennial census data to 2010 ACS data. This required creating a new class because all the ACS fields are different between 2010 and 2019, so it wasn't as simple as looping over a year parameter.
6. Creates a combined population field of island areas and mainland so we can use those stats in our comparison tool, and updates the comparison tool accordingly.
