In 2012 my grandparents purchased a new Toyota PriusV. They were driving back and forth between Upstate New York and Florida at least four times a year and the investment was worth it for them. In 2016 they had both died and the car sat unused by my mother and aunt, which is a problem for Prius’ because the battery charges when the car is being driven. My sister convinced my mother to “lend” us the car and thus our crash course in car ownership in New York City began. Owning a car in a city like New York, which is known for its extensive subway and bus system, is a privilege and should be treated as such, which includes complying with New York State motor vehicle and traffic regulations and paying fines for not complying with those regulations.

New York City is unique in that you can easily find free street parking, unlike in most large cities where parking permits are required. For many years my sister and I parked our car on the street, doing our very best to keep up with the alternate side parking and remembering to get the car inspected before it expired and we received a ticket for it. Once the pandemic hit in March 2020 and the city ground to a halt, alternate side parking was more or less suspended and even when it resumed on a modified schedule there was rarely enforcement for cars that weren’t moved. We weren’t the only ones taking advantage of this new lawlessness for cars, during this time I also saw a proliferation of counterfeit paper license plates, sometimes called “ghost plates”.

Once I learned more about the phenomenon of ghost plates and the underground network supplying them I was curious to know what New York City was doing about it. Using the NYC Open Data portal I pulled data for 311 complaints and open camera and parking violations. I explored the 311 complaints for paper license plates, which begin in July 2022 and the parking violation dataset for issues with counterfeit or mutilated registrations, expired or missing registrations, and a mismatch between the license plate and the registration sticker in an attempt to get a clearer picture on the state of the issue for the year of 2025. The data from 311 is collected from New Yorkers themselves, while the open camera and parking violations data is managed by the Department of Finance.

In July 2022 the City government announced a crackdown on counterfeit license plates and began encouraging New Yorkers to report paper license plates to 311. A year later, in July 2023 a dedicated portal was launched specifically for these complaints. The most complaints about paper license plates come from the upper west side of Manhattan up into The Bronx, central Queens, and downtown and north Brooklyn. Since the 311 complaints are generated by New Yorkers it can be difficult to determine exactly why some areas have more complaints. For instance, downtown Brooklyn is very dense so it makes sense to see more complaints from that area, but that doesn’t necessarily mean there are more offenders there, it just means more people are reporting on it.

The open camera and parking violations also show that Queens and Brooklyn have the most complaints for expired or missing registration, counterfeit or mutilated registration, and a mismatch between the license plate and registration sticker. There is no specific violation for ghost plates, so I pulled data that would capture a broad view of traffic violations as they relate to vehicle registrations.

The largest share of violations for New York State license plates is for an expired or missing registration.

In order to get a closer look at the cars with violations for ghost plates I filtered the data to just reflect violations for a counterfeit or mutilated registration sticker. This result was fascinating to me. The most 311 complaints for paper license plates come from Brooklyn and Queens, but Manhattan actually registers the most violations for counterfeit or mutilated plates.

The discrepancy between the 311 data and the data from the Department of Finance can be explained by who collects the data. New Yorkers report what they see to 311, so it’s possible that complaints reported in Brooklyn and Queens aren’t resulting in violations being issued or perhaps there is more traffic enforcement in Manhattan than in the outer boroughs.

When the NYPD does issue violations for ghost plates there are a variety of ways they can be resolved. The case can go to court for a hearing or can be resolved through an administrative process. For counterfeit or mutilated registrations, the cases that go to trial result in a guilty verdict more often than not, but when factoring in the other possible resolutions there is just over a 50% chance that offenders will be found guilty.

In March 2025, New York City announced its commitment to cracking down on obscured and counterfeit license plates. Since then the violations issued for counterfeit registrations peaked in early June 2025. The number of violations, though up and down by the day, has remained relatively consistent. This makes me wonder how effective these measures have been.

In 1996 traffic enforcement through the Department of Transportation merged with the NYPD. I think it remains to be seen if the NYPD is capable of policing itself and the communities it is allegedly meant to protect and serve. Police stations in New York City are notorious for illegal parking, illegal modifications to officer vehicles, and obscured license plates, to the point where a man was recently found deceased in his car a block away from a police station. Despite the car having illegally tinted windows and being parked in front of a fire hydrant, the civilian parking agent did not ticket or notify anyone about the car and the deceased man was not found for a week. It is suspected that due to the proximity to the police station (a mere 600 ft.), the agent likely assumed the car belong to a cop. Frankly, I think that until enforcement is done evenly across the city, until the people in charge of enforcing the law are no longer exempt from it, we will not see a meaningful change in counterfeit license plates. For the future of this project I think it would be interesting to looking specifically at police precincts. What are the 311 complaints like in those areas? How do they compare to violations issued?

A hobby my wife and I share is watching movies. She started using Letterboxd with some of our friends and convinced me to join. I started logging my movies in October 2022 and still use the app to keep track of my viewing history. What does my Letterboxd data say about my movie watching habits? Do the genres correlate with the time of year? What genre do I watch the most?

I created the dataset using the movie title, genre, date I watched, date released, my rating, and the Letterboxd’s rating as variables. For genre I used the genre’s assigned by Letterboxd. Each film had at least one genre, but some had as many as six. I kept track of the first three genre’s listed in order, but ultimately I only used the first genre listed. I noted the date that I logged the movie as the date watched and used Letterboxd data to determine the date the movie was released. If possible I used the date of the theatrical release. Some films premiered earlier at film festivals and others were released on streaming and had no theatrical release. For films that were released on streaming I used the date they became digitally available. There are a total of 65 movies in this dataset.

The first thing I looked at was which genre’s I tend to watch. A few things surprised me about the genre data. First in collecting the data, I realized how difficult it is to categorize a movie with a single genre. In order to make sense of the varying and oftentimes overlapping genre assignments, I interpreted the top genre to be the primary genre and so on. Since each film had at least one genre I created this visualization using the primary genre for each film and created groups for horror/thriller and action & adventure because of the overlap of the two. The second thing that surprised me was that my most watched genre was drama. I was anticipating that my top genre would be horror/thriller movies because I generally consider it my favorite genre. This visualization makes me wonder if I should reconsider that.

For as long as I can remember every October I try to watch as many horror (or thriller, crime, mystery, sci-fi, anything that can be “scary”) movies as possible. I think it sets the mood for Halloween and it’s the only time I can convince my wife to watch scary movies with me. I was expecting to see a concentration of horror/horror adjacent movies logged in the month of October across 2022 to 2025. The movie I kicked off my Letterboxd account with was The Texas Chainsaw Massacre (1974) and while I do tend to watch horror/horror adjacent movies in October, I also watch films in those genres over the course of the whole year. By far October 2025 has been my most successful “spooky season” with a total of 5 movies in horror, thriller, and sci-fi. The three preceding years I logged 1-2 movies in the month of October and October 2023 does not show any scary movies watched at all. Another interesting observation from this visualization is that for the 25 months where I have logged a film, only 6 months don’t have a comedy or drama. This makes sense when you consider that out of 65 movies 29 are in the drama or comedy genre, or approximately 44% of all films logged.

This chart looks at the rating I gave each film compared to the Letterboxd rating, which is the average of all submitted reviews across the app. I’m a fairly uncritical viewer and I tend to give most movies between 3.5-5 stars. Rating films is very subjective and I have no standardized criteria that I bring into each evaluation. I am not surprised by the films that I rated more highly than the average from Letterboxd users. To me the most interesting findings in this chart are the films that have a higher Letterboxd rating. Some of them are really close, like Rosemary’s Baby (1968) or Anatomy of a Fall (2023). These films are within tenths of a point of each other and the difference appears to be due to the way Letterboxd calculates its rating, using the averages of all it’s users rates, and how I am able to rate the film in .5 increments. The films with the most dramatic differences in my rating and the Letterboxd rating, where Letterboxd rated higher are The Lord of the Rings: The Two Towers (2002), an action movie and Chinatown (1974), a thriller.

In this visualization I was interested in exploring the amount of time in days that passed between when a movie was released and when I saw it. I decided to filter the data to only include movies released in 2024 and 2025 because 2024 was when I started using the Letterboxd app seriously. 25 out of 65 total movies in this dataset were released between January 2024 and today, or approximately 38%. The movies at the very top of the list are those with the least amount of days elapsed between the release and my viewing. Of these movies I saw 8 in theaters and the rest on streaming services.

My Letterboxd dataset relies entirely on self reporting. When I decided to use this dataset I thought I was much more committed to logging my movies in Letterboxd than I actually was. Despite using the app since October 2022, I only seriously began logging my movies in January 2024.

Aside from half hearted commitment to logging movies I think some other things can account for the gaps in my dataset. I only count movies that I intentionally sit down to watch. I will not record a movie that is playing in the background or if I was on my phone/distracted while viewing. Letterboxd does allow you to log limited series, but I have inconsistently logged them so decided to leave them out of the dataset. Initially I had also planned to include rewatches, but due to inconsistent logging of these I also chose to exclude them. Overall, I’d say that I have a more diverse taste in movies than I thought when I went into this project. It’s clear that I began logging movies more seriously in 2024 and I would like to continue to track my viewing. Working with this dataset has made me want to develop a more precise rating criteria and to continue to be disciplined in consistently logging my watched films.

I think it would be interesting to explore this dataset further by adding additional variables such as where or how I watched the film (at a theater, rented at home, streaming) and whether or not I liked the film, a feature on Letterboxd, in order to see if there are movies that I didn’t like but rated highly.

• Rat Mitigation & Eric Adams’ “Trash Revolution”