Behind the Scenes of “New York, I Love You (2020)”
By: Xinran Qi
Tags: Data Visualization, New York, Filmmaking, Tableau, Visual Storytelling

Introduction

This piece of work is inspired by the video “I Love New York,” created by Bardia Zeinali for Vogue, as well as my personal experience working as a location scout and producer for a number TV series and short films in New York in 2017 and 2018.

In the summer of 2017, I had a chance to work as an assistant location manager for a TV series called “For You” shot mainly in New York City. One challenge I found in production was the uncertainties involved in securing a location — the process of scouting, comparing, negotiating, and applying for a permit in the most efficient and cost-effective way possible was tricky, to say the least.

According to The Office of Film, Theatre, and Broadcasting under the Mayor’s Office of Media and Entertainment (MOME), most professional shooting would require a permit if equipment (anything more than a hand-held camera) is used and parking spots are requested. Although film permits data is made publicly available by the MOME since 2012, no location manager I know seems to be interested in discovering insights from it. This experience inspired me to explore and visualize the filming activities in the city.

In this project, I developed a scrollytelling narrative that tells the story of filmmaking in New York through essays, interactive data visualizations, images, and moving images. Using film permits data, archive photography and news articles, I aimed to create a data-informed narrative to help New Yorkers and visitors to learn something new and fun about the city. I imagine the audience to be average readers and film and television fans who are interested in casually learning about the film scene in New York, and I intend to show the project in a web environment on laptop, mobile, and tablet.

Related Work

While doing research on filmmaking in New York, I encountered two related projects using similar data. One is a map created by Max Galka and Steve Melendez at Metrocosm, titled “Filmed in NYC: Mapping Three Years of Film Permits.” Using film permits data from 2011 to 2013, totaling 517 movies and 17,241 locations, the map enables users to click a street to see a list of projects filmed there. The map is a great model with clear visuals and street-level details. However, with the dataset stopped in 2013 and the title of movies and TV series became anonymous, it is challenging to locate film shootings in New York without relying on crowdsourced tips. After realizing the difficulty in creating a movie-specific map, I decided to focus on the larger picture — using zip code-level data to explore the trends of filmmaking in the five boroughs in recent years, especially during the pandemic.

Another source of inspiration came from articles on several local sites including On The Set Of New York, On Location Tours, Travel + Leisure, and a few discussion threads on Reddit. I appreciate the organic observations, insights, and tips generated from enthusiastic New Yorkers all over the Internet.

Methodology

The data I used is from the Film Permits data published by the Mayor’s Office of Media and Entertainment (MOME) on NYC Open Data. Automatically updated on a daily basis, the dataset contains 65,545 individual permits issued from January 1st, 2012 to December 13th, 2020, the day I accessed the data.

After downloading the dataset, I cleaned the data in Excel by deleting unnecessary variables (ID, Country, EndDate) and repeating events (Rigging Permit). I conducted further research on event types by reading through permit requirements on the Department of Citywide Administrative Services (DCAS) guidelines. I decided to include “Shooting Permit”, “DCAS Prep/Shoot/Wrap Permit” (productions in DCAS-managed special properties), and “Theater Load In and Load Outs Permit,” which include all types of productions from major feature films to television series, theatre shows, on-location news, commercials, music videos, documentaries, and student films. Lastly, I formatted time and zip codes before transporting the csv file to Tableau.

In Tableau, I first plotted some explorative visuals to see the number of productions per year, per borough, and per zip code-level area. This stage of research led to the three questions I decided to explore in this project:

Where do most film, TV productions, and shows happen in NYC? What is the seasonality like for the creative industry in NYC? How does the pandemic affect filmmaking in NYC in 2020?

After narrowing down the topic to these specific questions, I started to research the “why” behind my findings. I read through many essays about the creative industry in New York, as well as Covid-related city announcements and news articles. Two pieces of writing I found particularly helpful were a Deadline.com article on the scale of the 2020 production halt and a New York Times article about the recovery of filmmaking in November.

Design & Implementation

I decided early on that I want to create a data-informed narrative through a multimedia scrollytelling format. I further affirmed my choice after reading many stories that either present Covid-related data using charts in a rather dry and didactic manner, or touch on the topic with sentiments-filled writing without data and evidence. My goal is to combine the two parts — data and feelings — to develop a narrative that is both reliable and relatable.

Overall, my goal was to create visualizations that serve as a vehicle of clarification and truth. My primary considerations for the project are clarity and interactivity. Aesthetically, I wanted my visuals to be clear, modern, and cinematic. I am a fan of the visual stories and graphics at the Times and the interactive story on “Mapping the Whitney Biennial” served as a visual inspiration.

I used a bubble-chart-on-map and two line charts in Tableau to plot the three main visualizations. In the map, I used the size of the bubble to represent the number of productions in each zip code area. I chose the orange circles with white borders to make sure the bubbles pop up on a dark-themed map, and edited the transparency to achieve a more elegant and soft look. I added time as an additional layer of encoding through “pages” feature and selected mid-paced animated transitions to ensure a smooth user experience. Lastly, I highlighted key information like year and insights I came up with using annotations. For web-based presentation, I included the legend in the still version of the data viz using Illustrator to integrate the visual better. The finished map can be found here on my Tableau page.

For the line charts, I adopted a similar aesthetics and used a black-orange-grey color combination to highlight the data and insights. The chart on seasonality provides a comprehensive view of how many productions are made in each month of the year. With the charts embedded on the site, viewers are allowed to play around with the data with the maximum flexibility. It is my intention to ask the viewers to contemplate on the charts in full screen, zoom in and out, and maybe have a look at the data nearby, or even plan their visit accordingly.

The scrollytelling format was hosted on Shorthand, a digital platform that enables storytelling using interactive features without relying on sophisticated coding. I started creating my story after browsing several examples to get familiar with the product. “Background scrollmation” and “text over media” are two features I frequently use to build the flow of the story. I used the reveal feature to build suspense in my story and the transition was smooth. I had some difficulties embedding my vizs into the story through HTML codes, but thanks to Jason Aston, it turned out to work fine if the code is generated from the chart on Tableau Public (as opposed to Tableau Server.) The features on Shorthand are very intuitive and easy to navigate for people with limited coding experience.

Future Implications

The project tells the story of filmmaking in New York with a focus on the timely issue of Covid-related impact in a general sense. I’m pleased with the insights I gained from the data and the visualizations I created answered the questions I raised in the beginning.

Moving forward, one direction to extend is to analyze the New York film scene’s recovery process in 2021. As vaccines roll out in large scale and it starts to get warm in the spring, filming can hopefully resume faster in studios and gradually on the streets in New York. It’d be great to link the film permits dataset to my project so that it can automatically update daily.

Another aspect to improve is to develop more in-depth insights about the part on seasonality. Further research can be done regarding what are some possible reasons behind my observation that October is the most popular month for film and TV productions. Beyond the reason I mentioned about the picturesque nature of fall-time New York, it may also be related to post-production and distribution scheduling in the film industry. With cinema closed and theatrical windowing changing swiftly in 2020, it may be interesting to explore whether the seasonality in production carries on in 2021 and beyond.

References