Job Income By State Analysis

2023 Cost of Living and Job Data Analysis: Affordability Insights for Individuals Across U.S. States

Overview

This project focuses on the cost of living across U.S. states, emphasizing job data for individuals. It provides insights into affordability by comparing the general income levels of various jobs within each state against the average cost of living.

The goal of this project is to help individuals identify which states offer the best financial rewards for their skills and vocations, thereby aiding them in making informed decisions about where to live and work.

The project aims to address the rapidly increasing cost of living and highlight the proportionality of job incomes to living costs. Results will be visualized in a heat map of the United States, showcasing which areas or states offer better affordability for individuals.

Instructions

1. Clone the repo to your machine.

2. Create a virtual environment and install the packages listed in the requirements.txt file (instructions below).

3. Open and run "JobPayMap.py" by typing "python JobPayMap.py"

   OR Select your job category and view results on Tableau public here

Virtual Environment Commands

Command	Linux/Mac	GitBash/Windows
Create	python3 -m venv venv	python -m venv venv
Activate	source venv/bin/activate	source venv/Scripts/activate
Install	pip install -r requirements.txt	pip install -r requirements.txt
Deactivate	deactivate	deactivate

Data Interpretation and Goals

This project aims to visualize the states where various jobs are most lucrative by creating a heatmap. The data was collected from two sources: the 2023 Cost of Living dataset and the 2023 Job Income data from the Bureau of Labor Statistics.

Cost of Living Analysis:

1. Averaged the cost of living by state using data for a single person with no children. Combined city and county information to derive a state average.

2. Job Income Analysis: Identified 22 common vocational categories from the Job Income dataset. Calculated the average income for each vocation by state.

3. Data Integration: Merged the two datasets to create a comprehensive list that includes the average cost of living by state, the average income by vocation in each state, and the net difference, termed as "Residual Income". This is NOT to be considered disposable income by any means, only showing how much money on average was leftover yearly after the average cost of living for each state.

4. Visualization: The project offers two visualization options. One uses Matplotlib and GeoPandas in Python to create a choropleth map based on user selections. The other is a Tableau choropleth map illustrating Residual Income by state. Both visualizations employ a red-yellow-green color scale, with green representing the highest residual income.

Code Louisville Project Requirements Met:

1. Loading Data:

   · Read two CSV files (Bureau of Labor Statistics, Kaggle).

2. Cleaning and Merging Data:

   · Used PostgreSQL (pgAdmin4) for a local database.

   · Cleaned and prepped data with SQL.

   · Merged data and performed calculations using Python (pandas).

   · Created a master CSV file for visualizations.

3. Visualize Data:

   · Program creates 22 possible heatmap visuals depending on the user's choice using Matplotlib. I also created a Tableau public link, to more easily view the data.

   The user has the option to run the python program from terminal, or view via Tableau online link.

4. Best Practices:

   · Annotated code for clarity.

   · Created a virtual environment

   · README includes virtual environment setup instructions, schema of the datasets, and a data dictionary (below).

State	Cost of Living	Job Type	avg_income	Net_income	Percentage_Income_Leftover
US State	Average Cost of Living	Job Type	Average Income by Job	Income less the Cost of Living	Percentage of residual income for each job, by state

Data Sources:

Job data sources: https://www.bls.gov/oes/data.htm

Cost of living data source: https://www.kaggle.com/datasets/asaniczka/us-cost-of-living-dataset-3171-counties