Issue 198 score docs (#358)

* Adds matplotlib to the pyproject.toml for score/ to avoid import error

* Adds document structure for workflow steps and source data dictionaries

* Reverts 793381e because Matplotlib is no longer a dependency

* Adds the workflow steps for the first 2 stages of the workflow

* Updates the README with some TODOs

* Makes changes requested in PR review

* Fixes one more reference to Justice40 score

score/README.md

@@ -1,5 +1,92 @@
 # Justice 40 Score application
 
+<details open="open">
+<summary>Table of Contents</summary>
+
+<!-- TOC -->
+
+- [About this application](#about-this-application)
+  - [Score comparison workflow](#score-comparison-workflow)
+    - [Workflow Diagram](#workflow-diagram)
+    - [Step 0: Set up your environment](#step-0-set-up-your-environment)
+    - [Step 1: Run the ETL script for each data source](#step-1-run-the-etl-script-for-each-data-source)
+    - [Step 2: Calculate the Justice40 score experiments](#step-2-calculate-the-justice40-score-experiments)
+    - [Step 3: Compare the Justice40 score experiments to other indices](#step-3-compare-the-justice40-score-experiments-to-other-indices)
+  - [Data Sources](#data-sources)
+- [Running using Docker](#running-using-docker)
+- [Log visualization](#log-visualization)
+- [Local development](#local-development)
+  - [Downloading Census Block Groups GeoJSON and Generating CBG CSVs](#downloading-census-block-groups-geojson-and-generating-cbg-csvs)
+  - [Generating mbtiles](#generating-mbtiles)
+  - [Serve the map locally](#serve-the-map-locally)
+  - [Running Jupyter notebooks](#running-jupyter-notebooks)
+  - [Activating variable-enabled Markdown for Jupyter notebooks](#activating-variable-enabled-markdown-for-jupyter-notebooks)
+- [Miscellaneous](#miscellaneous)
+
+<!-- /TOC -->
+
+</details>
+
+## About this application
+
+This application is used to compare experimental versions of the Justice40 score to established environmental justice indices, such as EJSCREEN, CalEnviroScreen, and so on.
+
+_**NOTE:** These scores **do not** represent final versions of the Justice40 scores and are merely used for comparative purposes. As a result, the specific input columns and formulas used to calculate them are likely to change over time._
+
+
+### Score comparison workflow
+
+The descriptions below provide a more detailed outline of what happens at each step of ETL and score calculation workflow.
+
+#### Workflow Diagram
+
+TODO add mermaid diagram
+
+#### Step 0: Set up your environment
+
+1. After cloning the project locally, change to this directory: `cd score`
+1. Choose whether you'd like to run this application using Docker or if you'd like to install the dependencies locally so you can contribute to the project.
+    - **With Docker:** Follow these [installation instructions](https://docs.docker.com/get-docker/) and skip down to the [Running with Docker section](#running-with-docker) for more information
+    - **For Local Development:** Skip down to the [Local Development section](#local-development) for more detailed installation instructions
+
+
+#### Step 1: Run the ETL script for each data source
+
+1. Call the `etl-run` command using the application manager `application.py` **NOTE:** This may take several minutes to execute.
+   - With Docker: `docker run --rm -it j40_score /bin/sh -c "python3 application.py etl-run"`
+   - With Poetry: `poetry run python application.py etl-run`
+1. The `etl-run` command will execute the corresponding ETL script for each data source in `etl/sources/`. For example, `etl/sources/ejscreen/etl.py` is the ETL script for EJSCREEN data.
+1. Each ETL script will extract the data from its original source, then format the data into `.csv` files that get stored in the relevant folder in `data/dataset/`. For example, HUD Housing data is stored in `data/dataset/hud_housing/usa.csv`
+
+_**NOTE:** You have the option to pass the name of a specific data source to the `etl-run` command, which will limit the execution of the ETL process to that specific data source._
+_For example: `poetry run python application.py etl-run ejscreen` would only run the ETL process for EJSCREEN data._
+
+#### Step 2: Calculate the Justice40 score experiments
+
+1. Call the `score-run` command using the application manager `application.py` **NOTE:** This may take several minutes to execute.
+   - With Docker: `docker run --rm -it j40_score /bin/sh -c "python3 application.py score-run"`
+   - With Poetry: `poetry run python application.py score-run`
+1. The `score-run` command will execute the `etl/score/etl.py` script which loads the data from each of the source files added to the `data/dataset/` directory by the ETL scripts in Step 1.
+1. These data sets are merged into a single dataframe using their Census Block Group GEOID as a common key, and the data in each of the columns is standardized in two ways:
+    - Their [percentile rank](https://en.wikipedia.org/wiki/Percentile_rank) is calculated, which tells us what percentage of other Census Block Groups have a lower value for that particular column.
+    - They are normalized using [min-max normalization](https://en.wikipedia.org/wiki/Feature_scaling), which adjusts the scale of the data so that the Census Block Group with the highest value for that column is set to 1, the Census Block Group with the lowest value is set to 0, and all of the other values are adjusted to fit within that range based on how close they were to the highest or lowest value.
+1. The standardized columns are then used to calculate each of the Justice40 score experiments described in greater detail below, and the results are exported to a `.csv` file in [`data/score/csv`](data/score/csv)
+
+#### Step 3: Compare the Justice40 score experiments to other indices
+
+1. TODO: Describe the steps for this
+
+### Data Sources
+
+- **[EJSCREEN](etl/sources/ejscreen):** TODO Add description of data source
+- **[Census](etl/sources/census):** TODO Add description of data source
+- **[American Communities Survey](etl/sources/census_acs):** TODO Add description of data source
+- **[Housing and Transportation](etl/sources/housing_and_transportation):** TODO Add description of data source
+- **[HUD Housing](etl/sources/hud_housing):** TODO Add description of data source
+- **[HUD Recap](etl/sources/hud_recap):** TODO Add description of data source
+- **[CalEnviroScreen](etl/scores/calenviroscreen):** TODO Add description of data source
+
+
 ## Running using Docker
 
 We use Docker to install the necessary libraries in a container that can be run in any operating system.