COVID-19 Data Visualizations
All of these graphs were made by Ansh Motiani using multiple libraries in the Python programming language to convert CSV (Comma Separated Values) formatted data, provided by multiple sources, into interactive visuals displaying change overtime in certain key areas.
They may appear very squished if viewed on a mobile device. For the best experience, please use a desktop or personal computer.
They may appear very squished if viewed on a mobile device. For the best experience, please use a desktop or personal computer.
The graph above displays mobility trends in the United States from Google Data. Until April, there have been declines in all areas other than residential mobility. Park mobility trends have increased exponentially after April, as compared to all other areas. For other forms of mobility to increase, social distancing measures must be taken into account. However, even with social distancing enforced, other areas of mobility may only be able to increase back to the baseline due to the uncertainty that encompasses the future of this pandemic.
The graph above displays mobility trends from Waze data taken from several countries. Italy demonstrated the greatest decline in mobility, but now adopted a positive trend. It can be taken from this data, therefore, that the greatest decline in mobility trends will relatively demonstrate a greater increase in mobility after lockdown periods. Mobility in other countries have failed to cope with the demands of this pandemic since they still remain under the baseline.
The graph above displays mobility trends in the United States from Apple Maps data. Public transit usage has decreased the most, when compared to driving and walking. During the month of April, preferences for driving and walking increased, causing the mobility trends for these two areas to remain where they were before pandemic lockdowns. However, public transit still lags behind. We project that public transit, especially in urban areas, will take more time to go back to its baseline level as shown before lockdowns were placed throughout the United States.
The graph above displays the US change in spending in different areas since January until the middle of July. Once lockdowns were placed in the majority of the country, spending surged for grocery and food, but plummeted for all other areas. Disregarding the spending of groceries and food, there was an inverse relationship in spending and COVID-19 cases per 100,000 until the start of April. Now, as new COVID-19 cases per 1000 increases, spending for all areas other than grocery and food is generally increasing, demonstrating a direct relationship. While the use of different commerce mediums may depend based on the areas they specialize in, it can be inferred that commerce on average will increase back to the baseline and remain there.
The graph above displays the US change in spending in different income levels since January until the middle of July. Once lockdowns were placed in the majority of the country, spending plummeted on average for the three income levels. Spending by high income individuals decreased the most, followed by middle income and then low income. It can be inferred that high income classes During this time, there was an inverse relationship in spending and COVID-19 cases per 100,000 until the start of April. Now, as new COVID-19 cases per 100,000 increases, spending based on each income level has increased. While the use of different commerce mediums may depend based on each individual’s preference, it can be inferred that commerce on average will increase back to the baseline and remain there.
This visual above displays the drastic increase in total unemployment claims by state from January until the middle of spring followed by a gradual decrease until the end of June. Due to the coronavirus pandemic, companies have not been able to produce at profitable levels. Therefore, regarding the uncertainty of the future of this pandemic, it may be in the best interest for companies to choose to have employees work from home if possible. In terms of transport, this may indicate a miniscule decrease in domestic mobility, since a small percentage of Americans may not have to choose to drive to and from work.