This project examines how unemployment trends intersect with key lifestyle metrics, specifically housing security and public health, for New Yorkers over time. We offer a comprehensive dashboard providing a high-level view of all variables, alongside dedicated pages for deeper dives into each topic. Interactive elements allow users to explore and analyze these trends across different time periods.
- What are the effects of unemployment on the well-being of New Yorkers?
- What is the relationship between housing stability, physical health, and unemployment among New York City residents?
- Is there a correlation between unemployment and housing instability, specifically eviction rates, in New York City?
- Is there a correlation between unemployment and physical health outcomes in New York City
- How can policymakers use data on housing instability and physical health outcomes to design more effective social benefit programs for unemployed New Yorkers?
NYC Open Data: Health Survey
- Updated Semi-Annually
- Community health survey data covering different health indicators and access to care over time.
United States Department of Labor Unemployment Claims (FRED)
- Updated Weekly
- The Unemployment Insurance weekly claims data are used in current economic analysis of unemployment trends in the nation, and in each state. Initial claims measure emerging unemployment and continued weeks claimed measure the number of persons claiming unemployment benefits.
NYC Open Data: Eviction Records
- Updated Monthly
- Eviction filings, shelter census, and housing court data tracking housing instability across NYC boroughs.
- Clone the repository
git clone https://github.com/advanced-computing/bouncy-banana.git
cd bouncy-banana - Create and activate a virtual envirinment
python -m venv venv
source venv/bin/activate #Mac/Linux
venv\Scripts\activate #Windows- Install Packages Install all necessary packages by running:
pip install -r requirements.txt- Set up secrets
- Our datasets are stored in Big Query, you will need to set the
secrets.toml - Use instructions [here] (https://github.com/advanced-computing/course-materials/blob/main/docs/project.md)
- Run the streamlit app locally
You can view the app locally by running
streamlit run streamlit_app.pyin the command line