Category: Student Work

Assignment 5 – Elena DeLuccia

So, I’m not sure if everyone heard my little rant in class on Thursday about this new, ridiculous, tourist-attracting U-shaped building that New York City is currently on it’s way to approving.

You can find all the info about it in the article here.

Basically, it’s so irritating because I (along with many others) believe that the money that’s going to be going towards building this impractical and useless structure could be going to certain things that could help the city such as reducing the homeless population, creating more affordable housing, fixing the roads or cleaning up the parks – and supporting New York City’s schools.

This is why I chose to do my tabletop and chart on the retention rates of CUNY (City of New York) schools in order to show that maybe this is something the money could go to – creating more opportunities for students in order to keep them enrolled and create a better future for them.

For my final article I’m going to look at things like the homeless population and much of the other things I mentioned in order to show where this money could be distributed to improve the city of New York.

In the meantime, I had a little trouble working the chart to get the key out of the way, but you can still see most numbers.


Groh Assignment 5

It’s commonplace to hear that the more education one receives the better the job he will have and the higher the salary he will receive. Here is the data that supports that fact. This table was taken from the California Open Data Portal. It reveals how many people make a given salary based on their level of education.

Groh Assignment 4

I put together a data table showing the amount of major sports teams in each state. To qualify as a major sport, the team needs to be in either the NBA, NFL, NHL, or MLS. The States are in alphabetical order. It’s important to note that these are all of the states with teams. This means that not all states have a major sports team. Also, Nascar is not accounted for even though it is one of the most popular sports in the U.S. It could not be included since it is not a team in the traditional sense like the other sports.

Assignment 4 – Elena DeLuccia

I decided to take a look at Donald Trump’s tweeting activity in the days following inauguration (January 20th). There is a clear distinction between which tweets he authored and those he decided to retweet.

Assignment 3 Tony Yao

I once worked for a TV reality show of this game. It would be interesting to look back in the development history of this game.

Assignment 3 – Elena DeLuccia

I thought it would be a good idea to do a timeline on something really relevant to news right now. So I decided to do mine on how Jeff Sessions got to the Attorney General position. I updated it to include the most recent controversies with his contact with Russia, and will continue to update if there are any other developments throughout the weekend!


Assignment 2- Jakubowski

For my chart, i wanted to look at the data from the jail censuses taken from all the jails in NY.  The data , was separated by county and dates.  I left in all the counties, but filtered the data into the most recent census year, 2015.  The names of the county jails are on the x axis, with the total inmate populations in the jail and the amount of inmates that were sentenced are highlighted on the y axis.

I took interest in the findings from the Onondaga county jail.  There are two jails within the country limits, with nearly an inmate population of 1000, and only 377 being sentenced.

NY State County Jail Populations (2015)
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Assignment 2 Groh

I examined the population density of New York counties in regards to the number of liquor licenses each county has. I had to utilize two different data sources to obtain the population density. I put the number of liquor licenses in one column and the counties’ population in another. After that I inserted the formula =C:C/B:B. The significance of this formula is that I did not have to manually enter each formula. I was able to apply that formula to an entire column and excel was able to identify the proper numbers that corresponded to each row.

People per Liquor License in New York Countie
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Assignment 2 – Zhong

Most hate crimes in New York State from 2010 to 2015 were against Jewish people, according to the dataset of hate crimes by county and bias type published by the New York State government.

Of the 3,671 hate crimes from 2010 to 2015 in New York State, 1442, about one quarter, were motivated by bias against Jews. Black people were also main victims of those hate crimes, with 670 against them during the six years. Hate crimes against the LGBT group also took a large percentage of the total incidents, with 548 targeting male-homosexuals.

In New York State, Kings is the county that had the largest number of hate crimes. About one fifth of the hate crimes happened there. New York had 522 hate crimes during those six years.


New York State Hate Crime from 2010 to 2015
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I found these dramatic numbers and trends mainly using Excel. I created a Pivot Table first and set county names as row labels. I also summed up the number of each type of crimes in the table. Then I arranged data in descending order and found out the most common types of hate crimes. To find out what counties had most hate crimes, I created another Pivot Table with county names as row labels and the total of crimes in each county as values. Besides this, I also did some calculations, filtering and sorting to ensure what I found above was correct.

Assignment 2 : Mahima Singh

All data is interesting. Choosing one dataset to visualize took a little longer than I thought it would. Ultimately I decided on the “Hate Crime” data because it has so many levels. The data dictionary is adequality explains the fields and I didn’t have to clean any of the data myself. Some other datasets that involved age groups required me to convert 0ct-10-2019 to 10-19 because the file had that column set to date type.

There are two kinds of crimes in the dataset: “Crimes Against Persons” and “Property Crimes”. While these two are choices under the “Crime Type” column, the type of bias isn’t classified into groups. This data was found in the “Hate Crime Overview” document in the “about” tab on the site.

To find the total number of crimes per type for each bias, I filtered on that particular crime type and summed all the columns. Then I just took these numbers and pasted them into different sheets of the chart making sure that the correct columns came under the correct bias type.

I have worked with before and my personal favorite theme for a chart has always been “Tokyo.” It fit well with the nature of the data. Even though the area charts are giving us an idea of the number of crimes across the different biases, having information on the total number of incidents and especially the number of offenders and the number of victims adds value to the visualization.

Hate Crimes by Bias Type: 2010 -2015
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