Introduction
For my geospatial project, I wanted to analyze COVID-19 Google Mobility Data for the state of Connecticut. The COVID-19 pandemic has caused major changes in the way we live our lives and has changed what people do on a daily basis. This dataset analyzes percent changes in how often people go to grocery and pharmacy stores, parks. retail and recreation, transit stations and their workplace from the statewide baseline prior to the pandemic. In this project, I set to understand changes in patterns of various daily activities across the state. Due to restrictions placed throughout the state, we are able to see how these limited daily activity for residents.
Hypothesis
I predict that we will see dramatic decreases in daily activities for most activities, except for parks. This is because many people were able to work from home and limited how often they left the house ever since the pandemic began. I think we will also see the impact of lockdown restrictions throughout the year, as many of them were lifted in the state around the summer months.
Methods
This data is publicly available for anyone to download for a limited time as the COVID-19 pandemic continues. You can download the dataset used at this link: https://www.google.com/covid19/mobility/. From this data, I selected only the data from Connecticut and connected it to Tableau. From here, you can see that I made maps that show the changes of mobility by month for each county in the state. Also, it is important to note that this data is only for the year of 2021 and ranges from January until November. I would have liked to find data from 2020, when the pandemic lockdowns initially began, however, I was unable to find that data for this region.
Map of Connecticut Counties
First, I wanted to show a map of the various eight counties in the state of Connecticut before looking at the mobility data.
Grocery and Pharmacy Mobility
From these maps, we can see that overall, people went to grocery stores and pharmacies at lower rates than they did before the pandemic began. The months June and September were the most positive with Windham, New London and Middlesex County having averages above the baseline. It is also evident that for some reason, February had the lowest dip in mobility across all counties. From these, we also notice that Fairfield, New Haven, Litchfield, Hartford and Tolland County all consistently witnessed a decrease in mobility, and never once reached their baseline or went above it.
Parks Mobility
In terms of park mobility, overall, there seems to be a general increase in how often people utilized these spaces. My prediction for this is that people felt more comfortable meeting with others in an outdoor setting. Also, after being locked up during quarantine, many learned to value nature and all of its benefits. Also, due to the weather it makes sense why months like January and February have more negative mobility scores attached to them.
Retail & Recreation Mobility
In terms of retail & recreation, the months January, February, August and November were all incredibly negative in terms of mobility across all counties. June was by far the most positive and had four counties exceed their baseline averages. I would not be surprised if this was tied to the vaccine becoming widely available at this point, and having the statewide mask mandate lifted. However, I do not fully understand why there were decreases following this month and why it is such an outlier from the rest.
Transit Station Mobility
I would attribute the decreases in mobility for transit stations to many workplaces switching to a remote work environment. For example, before the pandemic, many people who live in Fairfield County would often commute to New York City for their jobs. However, many large companies went virtual as a way to keep their employees safe from infection. However, I find it fascinating that throughout the whole year, there were constantly positive increases in transit station mobility across the state.
Workplace Mobility
As expected, and as I touched on earlier, I am not surprised to see that there were only negative changes in the amount that people went to work. Due to new, online work structures, many worked from home and did not need to commute to a physical office building often. I would be incredibly interested to see how this changes in the coming year(s) as I believe many jobs will continue offering a remote option in the future to decrease money spent on rent.
Overall Trends
I also wanted to create a visualization that shows the averages of all of the counties in one chart so you can easily compare the various mobility types. It is evident that parks by far had the most increase in mobility, and that workplaces were by far the least traveled to over the past year.
NOTE: It is important to take note of the various scales in each section as they can appear deceivingly positive.
Conclusion
Overall, I think that my hypothesis was proven to be mostly accurate. We did witness incredible decreases in workplace mobility, among other activities of daily life. However, in my opinion, it was hard to prove the impact of lockdowns and safety restrictions placed. Capacity limits and the mask mandate were both lifted in mid-May and I would have expected to see higher increases of mobility during the summer months. Consistently, June saw increases in mobility, but returned negative quickly, and I am unsure of why. In the future, I would like to add COVID-19 vaccination and case data to understand if there was any correlation between those two factors and mobility in the state.