Abstract- Catlin Power - Labormatics, Inc
Title: Building a COVID Economic Impact Dashboard
Title: Building a COVID Economic Impact Dashboard
Abstract:
Abstract:
The impact of COVID-19 has had a dramatic impact on individuals and the economy. The media is full of reports on the number of people that have fallen ill. In addition, many people have been left unemployed due to government-mandated business closures. Much has been made about the overall numbers and a few industries. We wanted to look closer at both the wider economic impact and the finer details of the those affected by unemployment and business disruptions. We built a dashboard to help visualize, interact with, and review trends in the labor market for different areas and industries across a time.
The impact of COVID-19 has had a dramatic impact on individuals and the economy. The media is full of reports on the number of people that have fallen ill. In addition, many people have been left unemployed due to government-mandated business closures. Much has been made about the overall numbers and a few industries. We wanted to look closer at both the wider economic impact and the finer details of the those affected by unemployment and business disruptions. We built a dashboard to help visualize, interact with, and review trends in the labor market for different areas and industries across a time.
To bring all of this information together we sought out a variety of data from different sources including the Department of Labor and Department of Commerce. Additional revenue, stock, and COVID data were acquired from Macrotrends, Yahoo Finance, and Our World In Data. The data were scraped from websites and news releases and then formatted for placement on an economic dashboard. For some of the data, additional analysis and modeling was done to project how these economic measures would have played out had COVID not happened. The code used for the analysis section of stock data was written in Python due to its package compatibility via Pandas on Excel csv sheets. Primary packages used include Pandas, Numpy, Sklearn, Matplotlib, and Dash for building web applications.
To bring all of this information together we sought out a variety of data from different sources including the Department of Labor and Department of Commerce. Additional revenue, stock, and COVID data were acquired from Macrotrends, Yahoo Finance, and Our World In Data. The data were scraped from websites and news releases and then formatted for placement on an economic dashboard. For some of the data, additional analysis and modeling was done to project how these economic measures would have played out had COVID not happened. The code used for the analysis section of stock data was written in Python due to its package compatibility via Pandas on Excel csv sheets. Primary packages used include Pandas, Numpy, Sklearn, Matplotlib, and Dash for building web applications.
For our presentation we will discuss this process including the steps we took to automate the process to keep the dashboard current. We will also discuss the additional analysis and modeling done to include pre-COVID trendlines to show where the economy was heading prior to the onset of COVID. We will demonstrate the dashboard to show how users can examine the demographics and details of individuals and businesses affected by unemployment and business closures. Finally, we will discuss our future improvements and plans for the dashboard.
For our presentation we will discuss this process including the steps we took to automate the process to keep the dashboard current. We will also discuss the additional analysis and modeling done to include pre-COVID trendlines to show where the economy was heading prior to the onset of COVID. We will demonstrate the dashboard to show how users can examine the demographics and details of individuals and businesses affected by unemployment and business closures. Finally, we will discuss our future improvements and plans for the dashboard.