NYCPlanning / NYCPlanning/data-engineering

PLUTO QA Page: recs from GIS

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db-pluto QA
Dominant language
Python
Stars
43
Forks
3
Avg merge
23h 3m
Merged PRs (30d)
44

Description

While jointly doing QA of PLUTO with GIS team, we recorded a list of various QA page improvements. Listing them here. These improvements/QA checks can later have their own child GH issues.

  • Source Data table: add column to indicate version type (does the version reflect when the data was pulled, ingested by DE, or Socrata/Bytes version?)

  • All visuals: across all data product pages, make sure the colors are consistent. Also, switch colors for yellow to represent current version.

  • All visuals: add a value thingy when hovering over a data point. Otherwise, when the difference for a given field between 2 lines is vast, one of the lines appears to be zero.

  • Building area graph: Add columns for absolute change.

  • All outlier tables: normalize numbers (no decimals with 8 places); add a button to export tables to csv/excel.

  • Unreasonably small apartments table: some indicate that NYCHA filter worked. Maybe update total # of records above the table.

  • Vanished/new bbls table: make bbl a string type.

  • Logging of streamlit changes: i.e. "changed a header for table X on this date" or "add a new table on that date".

Contributor guide

No contributing guide indexed for this repository

First steps

  1. Read the whole issue, then the project's contributing guide.
  2. Comment on the issue to say you are picking it up — it saves two people doing the same work.
  3. Fork the repository and make your change on a branch.
  4. Open a pull request that references the issue number.

Research direction

Start by reviewing the unchecked items for the Source Data table, all visuals, the vanished/new BBLs table, and Streamlit change logging. Split the list into child issues, identify the relevant QA page entry points, and define completion checks for each requested improvement before making changes.

Written by the indexing model from the issue text.

Assessment

Tech stack
python, streamlit
Domain
data-visualization, testing-qa
Issue type
Feature
Difficulty
5/5
Estimated time
Over a week
Activity status
Stale
Clarity
Mostly clear
Newbie friendliness
20/100

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