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Now, I know, this seems like an absurd assumption to make. And it is. I intend to prove that (even very simple) data can make the most strange and illogical arguments seem far more credible.

As we all know, Covid in the US is pretty bad. I looked at the CDC’s data and made this map that shows the total number of cases, just to really illustrate how bad Corona is, if you needed another reminder.


One thing that really interested me in the process of looking at data was the trends in alcohol/drug use during significant moments in US history. Specifically, I made the connection to the 2009 recession but thought looking at Covid-19 for the final might be a bit more timely.

First, I made sure that my conspiracies were true. I found this New York Times piece about excessive drinking during the pandemic, which led me to even more data.

I made the following graphs from this data set.

Participants of this study were asked if their drinking has increased, decreased, or not…


I looked at the Youth Risk Behavior Surveillance from the CDC.

First I looked at tobacco product use among high school students. Initially, I thought it would be interesting because I figured high schoolers don’t use as many tobacco products in recent years. However, I failed to recognize Juuls/other vapes as tobacco products. The data showed that in 2019, 32.7% of high schoolers were electronic vapor users and 5.7% are cigarette smokers. …


In 2020, the NYPD reported 82 arrests for fare evasion. 38 of the individuals arrested were Black and 30 were Hispanic. The Marshall Project, a nonprofit organization specializing in criminal justice, finds that the highest arrest rates for fare evasion are in predominantly Black and Hispanic areas, like Brooklyn and the Bronx.

2020 Q4 Fare Evasion Arrests, by Race

Finding data to show the pattern of over-policing in these communities of color was difficult to do. One way I tried to do this was by linking the relationship between race and poverty.

The most recently released New York City Government Poverty Measure, which is from 2018, found…


Subway fare evasion arrests in the Bronx, by race

Headline: Black and Hispanic Individuals in the Bronx Are Much More Likely to Be Arrested For Fare Evasion

I looked at the Bronx specifically for this assignment because the Bronx has more lower income residents than Manhattan does. For my midterm specifically, I wanted to show how fare evasion arrests disproportionally affect Black and Hispanic individuals in these areas. In NYC, it’s common knowledge that Black and brown people are over-policed, especially in poor areas. I wanted to use data as a way to show this. Fare evaders aren’t a single…


My first pitch idea for the midterm was to look at data reported by the NYPD, regarding fare evasion tickets. Specifically, I want to look at how fines disproportionally effect poor black and brown people. The NYPD provides this data on their website and I think it would be a very important critique of the relationship between law enforcement and racial discrimination.

My second pitch idea is to look at data provided by NYC open data, specifically, the Walk-to-a-Park initiative. It was created to make sure that all New Yorkers are within walking distance of a park or outside open…


I looked at the life expectancy at birth for women in Ethiopia, Germany, India, Finland, Mexico, and Portugal. If I were to manipulate the way this data is interpreted I would say it could be used to say how European countries are “better” in how their women have a higher life expectancy compared to those in non-European countries. This is a clear example of manipulating data because there are underlying factors that determine life expectancy. Such as living conditions, healthcare, domestic violence, etc. To say that European countries are better because their life expectancy is generally higher is to ignore obstacles that other countries face and make a broad statement. Because of this, I wonder if data is always used ethically because it is extremely easy to present it a certain way in order to promote your personal agenda.


The DataJournalism article was really interesting in how it framed data as a source. It helped me better understand how you can use data to find a story and that certain patterns can expose a larger narrative, often worth reporting.

The Source article was very informative. It really highlighted the logistics of having an organized spreadsheet. I thought the part about the data dictionary was helpful and the break down of how each portion should be filled out was very detailed, so as to not leave room for error.

I looked at hate crimes incidents in NY state. I found…


I thought the class census gave us very interesting information about everyone. In my visualization, I tried to highlight a few of the things people had in common.

Kleigh

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