Our story explores the spread of Coronavirus and the effectiveness of the “Stay at home” Order by comparing 5 different cities worldwide.
Idea
As Coronavirus outbreak (COVID-19) has continued to explore and grow, we are interested in the relationship between the growth rates of confirmed cases and the effectiveness of the “Stay-at-home” order. To better understand worldwide community mobilities and the number of diagnoses, we selected the 5 most representatives regions around the world, including Wuhan, China; Milan, Italy; Los Angeles, California; Boston, Massachusetts, and New York City. At the same time, we collected the dates of issuing the “Stay-at-Home” order in each region. Besides, we also want to find other possible correlations or causations between different factors that might affect the growth of COVID-19.
Collecting and Cleaning Data
We collected the dates and timelines of issuing the “stay-at-home” order.
Wuhan, China - Jan. 23rd
Milan, Italy - March. 8th
Los Angeles, California - March. 19th
New York City, New York - March. 22rd
Boston, Massachusetts - March. 24th
We chose these cities for reasons. As the first case reported in Wuhan and the outbreak of COVID-19, Wuhan decided to close the city after confirming it as an infectious disease. The government built makeshift hospitals within a short period. Finally, the quarantine order in wuhan ended on Apr.8th. The Lombardy region of Itlay was the first region in Europe to be severely hit by the coronavirus. In order to control the spread of disease, the government imposed a national closure on Mar.9th. Therefore, we chose data from Milan, Italy. California broke out earlier than New York, but the population density of both areas is relatively large, thus we chose these two cities. In addition, we selected Boston, as the Biogen meeting in February facilitates the spread of disease in Massachusetts. After the meeting, many schools began to suspend classes and finally implemented the “Stay-at-home” order on Mar.24th.
We also collected confirmed cases by region.
First of all, we researched a large number of datasets about COVID-19. At the same time, daily data is dramatically growing, which made our data collection works very difficult. We also found errors and deviations from different data resources. So we spent about a week collecting data from these five regions, also we compared datasets from different resources. Since we hoped to find the relationship between the effectiveness of quarantine and the spread of coronavirus, we paid attention to these data and observed the decrease in the number of diagnosed cases on Apr. 25th.
We have mainly obtained data from the following websites, and updated every day:
Wuhan City (Jan.23th - Apr.8th)
Milan (Feb. 26th - Apr.25th)
Los Angeles & New York City (Mar.1st - Apr.25th)
Boston (Mar.1st - Apr.25th)
After collected data, we used OpenRefine and Excel to clean the raw data. We finally organized five datasets into an Excel file.
Moreover, we found an interesting data website made by Citymapper. The main users of Citymapper are people who use public transportation including walking. Thus, it has enough data to record the change of user’s mobility behavior. We chose to download Citymapper Mobility Index which is used to calculate travel rate by city. We downloaded raw data from the website and filtered the date of the cities we chose.
Other possible correlation and causation
Besides, we also considered other factors that might affect the confirmed cases, such as the population density and the amount of aging. Thus, we searched the population data in Wuhan, Milan, Los Angeles, New York City, Boston, and compared them with the diagnosed cases in each city.
After we talked with professor Lina, she mentioned an interesting point that smoking can also be related to the diagnosed cases. We did researches from this aspect, although we did not find the datasets, we found the opposite statement from different research results.
Smoking is likely associated with COVID-19 https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7083240/
Smoking may lower the risk of being infected COVID-19
Interview
As we had the data files, we decided to interview people who are under-self-quarantine worldwide. At first, we interviewed a graduate school student from NYU, Karry Kan, she is living in New York City with the most confirmed cases. In our interview, she introduced that even before the “stay-at-home” order, she paid attention to this pandemic and wore masks, but she suffered discrimination. After that, she chose to take a few public transportations as possible and go out on foot. After this interview, we changed our angles of interviewing people who are currently following the “Stay-at-Home” order. We also wanted to interview some people who experienced this pandemic in Wuhan. We interviewed Herry Huang, who is working in Wuhan. The day after the lockdown of Wuhan was Chinese New York, so Herry could not return to his hometown and reunite with his family. He spent a special festival in Wuhan City, We think this can help us write stories.
Initially, we focused on comparing US cities with European cities. But after finishing the interview, we also wanted to show readers the experiences and changes of WUhan through our stories. Fortunately, Wuhan closed its “Lockdown” on Apr.8th, and the outbreak has been brought under control. Therefore, we chose to use Wuhan as one of the possibilities for the development of the pandemic, and we tried to use the stories of Wuhan people to help better understand the importance of the “Stay-at-home” order and what to do during COVID-19 outbreak.
Visualizations
Considering that Wuhan has ended its closure and the number of diagnoses no longer rises, we chose to use Wuhan as a reference group, and we compared the growth rate and amounts of the other four cities. We highlighted the “Stay-at-home” order dates in each city and also calculated the difference of mobility before and after the order implementation of the city.
Contribution
Collecting data
Wuhan City: Ruoyi
Other Cities: Singing
City Mobility: Hope
Population Demographics Data: Ruoyi
Interview
Hope
Visualization
Singing
Story
Hope, Ruoyi, Singing
Methods
Singing, Hope