RIT Students Use Data Visualizations to Pose Questions About Crime, Policing, Parks, and More


ROCHESTER, NY – Journalists – whether they are old, young or aspiring – know that they’re only as good as their ability to generate story ideas. Yet story-idea generation is hard. Where to start? What sources of information can we turn to for inspiration?


For the data journalism course at RIT that I’m teaching this semester, students are relying on data from Open NY as the first stop on the story-idea journey. They each select a dataset, and from that they envision a story idea. Here, you’ll see the story ideas they came up with, and a corresponding data visualization that’s backed up by the data set. Students in this class stem from undergraduate majors across RIT, including journalism, photojournalism, computer science, animation, game design – and more.

But first, a note: One thing that I noticed – and this is clearly a sign of our times – is that out of a class of 18 students, four selected a dataset quantifying the number and types of hate crimes in the state. This was the only dataset that drew multiple students to it. Yet regardless of the dataset and what it quantifies and chronicles, students are seeking answers about the world around them. And this buoys me.

–Hinda Mandell, Associate Professor, School of Communication, RIT (hbmgpt@rit.edu)


Visualizing Hate Crimes Data

Jose Estevez

Computing & Information Sciences student, RIT

Story idea:

This piece explores why certain counties have higher hate crime rates than others.


Brief explanation of data source and how you arrived at this story idea:

This data was provided by the New York Division of Criminal Justice Services. It details crimes which have been committed out of hostility towards a certain demographic, whether that be people of a certain race, religion, sexual orientation, or any other identifier. I arrived at the story idea when, while looking through the data, I noticed that the Bronx had a much higher number of anti-Semitic hate crimes than other counties, with 13 property crimes in 2019. Kings in 2019 ended up having the most anti-Semitic hate crimes in the data set, with a staggering 90 property crimes and 32 crimes against people. This disparity jumped out at me, and immediately had me wondering about the cause for this difference.

Brief explanation why this data speaks to you:

This data increases in importance as we are made more aware of the social disparities in this country; whether that’s across racial lines, gender lines, or any other figurative line. With events last year such as George Floyd’s murder at the hands of Minneapolis police, the idea that people are attacked simply because of their identity is all the more terrifying. While perusing through the data, the much higher amount of hate crimes in certain counties immediately jumped out at me, and made me consider why certain counties are more prone to such hateful violence. More understanding is desperately needed on what fuels these direct attacks on the “other.”

Visualizing Rural Hate Crimes Data

Cayley Smith

Journalism student, RIT

Story idea:

This piece investigates the rate of hate crimes that occur in rural counties in New York State from 2010-2019.


Brief explanation of the data source and how you arrived at the story idea:

I found the dataset, “Hate crimes by county and bias type: Beginning 2010” on Open NY. The data source shows the hate crimes in New York State according to number of occurrences, bias type, which county, type of crime, and what year. The bias type includes race, religion, and sexuality. The type of crime presented was either crime against the person or a property crime. The years the data was recorded was between 2010 and 2019. While looking through data sets, I was focused on what kind of data would jump out at me and that I could develop a story from. The data was interesting due to the difference in hate crimes between urban and rural counties. I arrived at the story idea due to the large difference in the rate of hate crimes. There are 62 counties in New York state. Only 12 out of the 62 are urban. The total incidents of hate crimes from all the rural counties are still far less than those of the 12 urban counties.

Brief explanation of why this data speaks to you:

The data set concerning hate crimes by county and bias type was a very intriguing set for me. A couple of things shocked me when exploring the data such as the differences between rural and urban counties as well as the bias type within the hate crimes. Even though there are far more rural counties in New York State, the number of hate crimes is higher in urban counties. The rate is much lower in rural counties. In the data set, I found that from 2010-2019 the occurrences of hate crimes in urban counties were 3,851 whereas from those years in rural counties, it was only 1,944 instances. I think this set is so compelling because, in the news, we see these stories of hate crimes, but don’t often see the straightforward data set. Something that was newly revealed to me in this data set is seeing the biases against people in certain counties, it created a harsh reality that deserves to be explored. Initially, the large number of anti-Semitic hate crimes was extremely shocking to me, and then to see that mainly in urban counties. In rural counties the hate crimes were different, more so due to race or sexuality. For instance, “The F.B.I. collected data on 7,314 criminal incidents motivated by bias toward race or ethnicity or gender identity in 2019,” as said in the New York Times article about the rise of hate crimes.

Visualizing Crime Data

Jaiden Tripi

Photographic & Imaging Arts student, RIT

Story idea:

This piece explores why violent firearm crimes have begun to gradually decrease across Monroe County in New York since 2017.

Brief explanation of data source and how you arrived at this story idea:

The data source I used was called “Index, Violent, Property, and Firearm Rates by County: Beginning 1990.” This data set illustrates many ways to look at firearm crimes, such as violent firearm crimes by year, county, and population. After filtering through the data with the visualization tool, I arrived at the total violent firearm crimes committed by the county-- in this case Monroe county, where we reside-- and by year, to see if the total crimes committed has been increasing or decreasing. At first, I saw the fluctuating numbers (increasing and decreasing by year), but then I saw a sudden dip in 2017. In 2017 there were 743 crimes, then in 2018 there was a drop to 634 crimes committed. Additionally, an article published by the Washington Post discusses why this dip may have occurred. I found it interesting and wanted to explore this dip even more.

Brief explanation why this data speaks to you:

I chose this data because of its relevance in the recent months with the election and protests going on in society. Along with this I also found that my data being in Monroe county-- the county I reside and grew up-- having less violent firearm crimes in recent years, starting in 2017, to be very pleasing for the community. Prior to 2017, however, the total committed crimes fluctuated up and down for years. I was quite surprised by this data because I, for some reason in my head, thought violent firearm crimes around Monroe county were getting worse; this may have been from a societal stigma, however. According to a Time magazine article, this past year broke record high firearm crimes, so this is probably the reason. This discrepancy between a national trend and a local decrease would be worth exploring in an article.

Visualizing Police Personnel Data

Vincent Alban

Photographic & Imaging Arts student, RIT

Story idea:

This story highlights how New York State police departments have increased in number of officers since 2007.


Brief explanation of data source and how you arrived at this story idea:

This data set tallies the amount of police officers in every police department across New York State annually, starting in 2007 and it is still ongoing. The data is collected by the Division of Criminal Justice Services (DCJS). The data set includes more than 500 police departments. The data is collected using the Uniform Crime Reporting (UCR) system where the law enforcement agencies report their annual personnel counts to the DCJS. The data set includes the county, the police department, the year, and the numbers of sworn full-time officers, sworn part-time officers, civilian full-time officers, and sworn officers in total. The data set was created on March 1, 2013 and was last updated on May 29, 2020. I arrived at this story idea because I was initially intrigued in how the numbers of police officers in a department fluctuate and change over time. I was curious to know if they increased, decreased, or didn’t change at all. I looked over the data and made the conclusion that over the last 12 years, the number of police officers in New York State has increased by close to three thousand. The number of police officers dipped from 2008 to 2012, but overall there was increase in the number of police officers between the start and end of the data set. The more noticeable increase was from 2012 to 2019, where the number of police officers increased by six thousand.


Brief explanation why this data speaks to you:

This data intrigues me because of the recent wave of protests against police departments across America, sparked by the murder of George Floyd. Many Americans, especially a large portion of my generation (Gen Z), are very critical of the police and how vast their budgets are compared to budgets for schooling, rehabilitation, and mental health services, among other things. I would want to investigate and write this story because I am curious to know if more police officers are training as an effort to change the system or if they are trying to keep it the same. This information was partially new to me as I did not know the numbers of police officers in the state. I figured it was an increasing number as the budgets generally increase and this confirmed my predictions. My data-based findings revealed that the police departments could show an increase in support as more officers are joining the force. This would inevitably lead to an increase in their budgets as more money is needed for more training, more salaries, and resources, etc. According to The New York Times, budgets for police departments in 150 of the largest cities in America have “gradually increased by about 1.2% since the late 1970s, to 7.8%”. This increase rises by millions of dollars every year. To put this into perspective, the same cities have gradually decreased budgets for housing and parks to 5% and 3%, respectively.

Visualizing Energy Subsidies Data

Eryk White

Film & Animation student, RIT

Story idea:

This story investigates the amount of discounted energy that businesses receive through the Recharge New York program and whether it aids job growth in Monroe County.


Brief explanation of the data source and how you arrived at this story idea:

This data source shows how much low-cost energy organizations are receiving through the Recharge New York program. Furthermore, this data set shows which county, city, and region benefitting organizations are located in, how much energy they are receiving through the program, how many jobs have been committed in turn, and the date as of which this information is valid. After coming across this data, I wanted to write a story about the amount of energy received and jobs committed to ensure each company was upholding their responsibility to develop the local community. The statistics offered enable journalists to ensure that commitments to the community are upheld and that each recipient is following the program’s goal. For example, the data states that Quality Vision International receives 326 kW and has committed 250 jobs to Monroe County. The associated infographic quickly shows that this is one of the highest ratios of jobs committed to energy received and therefore teaches us that Quality Vision International is relatively successful at improving the community through job creation. It is important to note, however, that the statistics from data.ny.gov only provided numerical values for energy allocation and job commitments. A lower ratio between these two stats does not inherently mean that a company is abusing the program, since they may be prioritizing other goals of Recharge New York such as expanding and attracting businesses. If no mishandling of the energy is discovered, this dataset easily allows the reporter to highlight local companies that go above and beyond to develop the community through high ratios of jobs committed to energy received.

Brief explanation of why this data speaks to you:

This data is important because it tells us what companies are receiving heavily discounted energy through the Recharge New York program and how many jobs they are creating in return. Examining the relationship between these two values lets us determine which companies are giving back the most by retaining and creating jobs in Monroe county. In an announcement about the program in 2018, Governor Andrew Cuomo said: "These economic development programs incentivize businesses to lay down roots and expand in New York State, creating jobs and bolstering the economy for all hardworking New Yorkers." Locally, this program has had a dramatic impact in the past by supporting “the retention and creation of 304 jobs in addition to capital investment commitments of $145 million” by companies such as Rochester Insulated Glass, HP Hood, and AmChar Wholesale (all three of the aforementioned companies reside in the Finger Lakes Region, which Monroe County is a part of). Furthermore, an independent investigation of this info lets us determine what companies may be disproportionately benefitting from free energy and therefore lead to more analysis that ensures every recipient is truly committed to improving the community through the program’s goals of “job retention and business expansion and attraction (data.ny.gov).” While browsing through this data, I was surprised to see discrepancies between the ratio of energy received and jobs created; however, this might be because the Recharge New York program is somewhat vague in its goals and promotes “business expansion and attraction” on top of job creation and retention. Using the given metrics of energy allocation and jobs committed, it was revealed that Rotork Controls Inc. has the highest ratio of jobs committed to energy received in Monroe County.

Visualizing Parks Attendance Data

Brett Peters

Computational Mathematics student, RIT

Story idea:

This piece explores how the attendance at camping sites in New York has drastically increased since 2014.

Brief explanation of the data source and how you arrived at the story idea:

The data source is named State Park Annual Attendance Figures by Facility: Beginning 2003. This data source lists the yearly attendance at state parks within New York. While viewing potential topics that I could’ve chosen, I realized that there was too much focus already on politics and how everything has gone wrong since last year. I wanted to take a look at how people are living their lives in a limited capacity now. I realized that there might’ve been a noticeable change in the amount of people who have been visiting state parks as a means of recreation, as it is an activity not entirely hampered by the societal changes over the past year.

Brief explanation of why this data speaks to you:

I think this story speaks to me because I used to go camping in the past, mostly throughout New England. I’ve been to a variety of camping sites firsthand, and the best part of the experience is experiencing nature in a solitary way. With COVID-19 now a threat to most social activities, camping and visiting state parks seems like a fresh alternative. As such, I expected to see a spike in the data from this past summer/year, where more people go out to state parks because that's an open-air activity or vacation. I did not expect, however, that the data would have been gradually increasing year after year. I also learned which state parks were popular on average: naturally, Niagara Falls Reservation takes the top spot, but other parks like Jones Beach and Robert Moses reported large numbers as well.

Visualizing Fish Stocking Data

Marielle Scott

Photographing and Imaging Arts student, RIT

Story idea:

This piece centers on the environmental and commercial reasons behind “fish stocking” that occurs every spring in Monroe County.


Brief explanation of data source and how you arrived at this story idea:

The data source found for this story idea is the current season of spring trout-stocking throughout New York State. According to the data set, every spring the Department of Environmental Conservation stocks more than 2.3 million species of trout across the state. For this story idea, I wanted to focus on trout stocking in Monroe. In the data set, we can see which town the trout will be stocked in; the waterbody within that town; when they will be stocked within the spring season; the number of trout stocked; the species; and the size of the fish. Looking at the data set, I was intrigued by the reasons why only brown trout were stocked in Monroe County and why the stocking occurs every spring in the first place. Although commercial and leisure fishing is a contributing factor for trout stocking, I wanted to explore other reasons why the stocking is important. The brown trout is also not native to North America and I am curious as to how the number of fish and species might be affecting the bodies of water.


Brief explanation why this data speaks to you:

This data speaks to me because of my past experiences in learning about trout and trout stocking. My father works as an environmental science teacher and every year he raises trout in his classroom and releases them into a local creek at the end of the year. Often times I’m involved in my dad’s work at his school and raising the trout is no exception. The trout are released in order to keep the population on a substantial level and to keep the ecosystem fed. Looking at this data interested me because I wanted to know the reasons behind mass trout stocking in larger bodies of water, like Lake Ontario and Irondequoit Bay. I think it’s important to know environmentally what is going on in your community. I was surprised by the large number of trout that are released every spring and the fact that in Monroe county, only brown trout are stocked in nearby bodies of water. In the description of the data however, I was not surprised to see that trout stocking is mostly due to commercial fishing. According to the Department of Environmental Conservation (DEC), trout stocking occurs in the Spring from March through early June and that it’s for the Spring fishing season. What’s interesting about the data and the information from the DEC, is the lack of explanation with environmental impacts. It could possibly be because there are no environmental impacts, but I think it would be interesting to talk about. According to New York Upstate, the DEC is making changes to the trout stocking strategy this year. Focusing on stocking larger fish, at least 9 inches rather than the 6-8 inches, the DEC is looking to fill the needs of anglers, but they also included stocking larger bodies of water more often and stopping the stocking of smaller waterways. The changes in plans are interesting and I think are important to explore.

Visualizing Trail Accessibility Data

Tyler English Journalism student, RIT


Story idea:

This piece explores the accessibility of outdoor recreation in New York State.


Brief explanation of data source and how you arrived at this story idea:

The data set that I have chosen comes from the open data New York website. That dataset shows what trails and locations in the state allow vehicles. It then breaks down what vehicles are allowed on those sites. The data shows – for those who possess a motorized vehicle for disability reasons – which trails and locations are accessible to them. After taking some initial looks at the data I began to wonder what efforts are in place for those with disability to enjoy outdoor recreation in New York state. I am curious to discover if there are regions of the state where they excel in accessibility in outdoor recreation. I am also interested to see if there are statewide issues in this field or if it is a statewide struggle. I arrived at the story idea by simply wondering what individuals with disabilities experience when they go hiking or camping at New York State sponsored trails or parks.


Brief explanation why this data speaks to you:

This data speaks to me because nature and the outdoors is something that every human being is entitled to. Each person should be able to experience nature and what the natural world has to offer. If someone has a disability and trails do not allow them to get this access, that shortcoming needs to be addressed. I think that is also what makes this a worthy story idea; we are all entitled to nature and not many people are talking about the lack of accessibility in outdoor trails. After doing some online research I read an article on EcoWatch, which highlights an organization in Canada that is working to increase accessibility for the World’s Largest Hiking Trail. If strides like this are underway in Canada, they should also be taking place here in the U.S. After having some phone interviews, it came to my attention that Yosemite is doing good work with accessibility services. The data revealed to me that there are a shockingly low number of trails and parks in New York that would be considered accessible to all.

Visualizing Social Distancing Violations Data

Nick Baker

Game Design & Development student, RIT

Story idea:

This piece examines how daily social distancing violations in New York City parks increased in May and July of 2020 and has since declined heavily.

Brief explanation of data source and how you arrived at this story idea:

The data was collected by the New York State Department of Parks and Recreation (DPR) and obtained on the NYC Open Data website. The data has been collected daily from April, 2020, when the patrolling started taking place, to the present day, tracking every encounter that patrons have with patrol officers due to social-distancing violations. I arrived at the story idea after seeing the number of daily social distancing violations in New York City parks spike in May and July 2020, and then decline heavily afterwards.

Brief explanation why this data speaks to you:

This data speaks to me because I feel it is hard to fully understand the scale of the new social-distancing policies. These new rules completely go against what everyone is used to, and while they make sense to me personally, the data clearly shows that many people did not understand them, as seen in the spikes during the summer. For instance, during April, the highest encounter amount was around 1,500 patrons, while in July the highest was at 4,400. I am also interested to learn how effective this patrolling and educating was in teaching the general public. In the data, the number of encounters after August drops down to about 200-500 patrons a day, which is a huge drop from July. The data left me with the question of whether the patrolling worked, or whether violations were still happening in late summer, but perhaps by that point people started to learn the rules.