"A City That Never Sleeps?" - new data and analysis from "On Broadway" project

ON BROADWAY from Moritz Stefaner on Vimeo.

To create our interactive installation and web application On Broadway (currently on view at New York City Public Library), we assembled lots of images and numbers:

661,809 Instagram photos shared along Broadway during six months in 2014;
Twitter posts with images for the same period in 2014;
8,527,198 Foursquare check-ins, 2009-2014;
22 million taxi pickups and drop-offs for al of 2013;
selected indicators from US Census Bureau for 2013 (latest data available).

On Broadway visualizes some of the patterns in the collected datasets, but there are many other interesting things to discover in this data.

In this fist post we discuss temporal patterns of Instagram use in some of the areas of NYC.

These are the areas crossed by Broadway street as it runs through all of Manhattan (13 miles). (In a later post we will present analysis of 10.5 million Instagram images we collected for all of NYC.) Representing the city through a single "slice" (one cross street) simplifies data analysis - instead of dealing with two dimensions of space we only have one (position along Broadway. This also allows for interesting visualizations that do not have to use all too familiar maps.

Analyzing patterns of human activity through Instagram

Why should we care about the times when and where people post on Instagram? Combined information about the locations of posts and their times can give us insights into patters of human activities. Some areas and time periods will have lots of posts, and some almost none. Of course, not every type of activity will create a strong Instagram signals, but many are (going out with friends, sightseeing, celebrating, civic events, etc.)

For example, in an earlier project (phototrails.net) we analyzed Instagram patterns during two memorial days in Tel Aviv, Israel (Holocaust Memorial Day; Israeli Fallen Soldiers and Victims of Terrorism Remembrance Day). Another project (the-everyday.net) looked at Instagram patterns during Maidan Revolution in Kyiv, Ukaine (February 2014). In both cases we found that Instagram usage gives us valuable spatial-temporal "maps" of the events, revealing their dynamics and rhythm.

Importantly, Instagram (and other media sharing networks that record location information) gives us much more than simply points in time and spaces corresponding to the users sharing images. We can also examine the images to understand what people chose to photograph and how. (Both images and their metadata can be downloaded by using Instagram API. Here are examples of recent research articles that use Instagram data). This post only discusses time and space information (when and where images were posted), in another post we will examine patterns in the content of 660,000 images we collected along Broadway.

A sample of Instagram images shared around Broadway and Maiden Lane (this area is close to Wall Street).

A sample of Instagram images shared around Broadway and West 184th Street.

1. Hours of the day

"The City That Never Sleeps" is a popular nickname for New York. But is it true? Analysis of Instagram patterns shows that this common image of New York is not quite correct (at least for the parts crossed by Broadway). Or rather, instead of full 7-8 hours of sleep, NYC only naps for couple of hours.

Numbers of posted Instagram images increases during the morning, reach their peak during the day, and decrease in the evening. The most quite period is 3am - 5am.

The volume of Instagram posts by hour.

Here is an alternative visualization of the same data that shows the differences between times of the day more dramatically. In this visualization, each hour of the day gets its own “clock”:

Data: 190K Instagram images shared along Broadway street during, weeks 10-15, 2014.

2. Hours of the day - comparison with other global cites

We can compare Broadway hourly Instagram patterns with the patterns in other global cities: Bangkok, Berlin, Moscow, Sao Paolo.

These plots use data for 20,000 Instagram images shared during exactly the same week (December 5-11, 2013). The graphs show numbers of Instagram images shared per hour averaged over one week. (We collected these images for selfiecity project using same size central area of each city.) NYC, Berlin, Moscow and Sao Paulo have similar patterns, but Bangkok and Tokyo differ: there is a peak around lunch time, and then another peak after 7pm.

3. Hours of the day - Broadway 1 vs Broadway 2:

Since Broadway crosses some of the most popular areas of NYC such as Time Square, a significant proportion of Instagram images shared along some areas along Broadway are from tourists. (In this post we don't separate tourists from locals - this will be a subject of another future post.) It is equally important to remember that Broadway crosses areas with different economic and social characteristics. Therefore, if until now we considered "Broadway" as a single data source, we will now look at temporal differences in Instagram use between its parts.

When we took all data we collected (Instagram, Twitter, FourSquare, taxi rides) and graphing it along the duration of Broadway, we found two completely different parts. It is as though one street connects two different countries. We called them Broadway 1 (from Financial District up to 110th street) and Broadway 2 (from 110th street to 220th street). The first part has the famous tourist spots, and also much more social media and taxi activity than the second part.

For example, this graph shows numbers of Instagram images along duration of Broadway (left to right):

Data: 660K Instagram images, 2/27/2014 - 8/01/2014. "Points" are centers of 100m wide rectangles spaced 30 meters apart along Broadway (713 points covering 13 miles, south to north).

The difference in Instagram volume between Broadway 1 and Broadway 2 is immediately obvious, even if we don't take into account a few spikes corresponding to popular tourists photo taking spots.

(Note the small peaks in some areas in Broadway 2 which may be reflections of gentrifications of these areas. (In a later post we will do a more detailed analysis comparing all neighborhoods crossed by Broadway).

Averaging all data we collected for Broadway 1 and Broadway 2 shows that Broadway 1 part there are 6.83 more Instagram images, 3.91 more tweets with images, 9.29 more taxi drop-offs and 7.9 more taxi pick-ups.

If we calculate household income averages for two parts using ACS 2013 census tracks data), we found that average for Broadway 1 is $119,000, while the average for Broadway 2 is $39380.

There are many reasons why we see much higher activity in Broadway 1: presence of tourists, more affluent locals, lots of people working in downtown and midtown, etc. Given how much money an average tourist spends during a visit to NYC, economically many tourists have more in common with the people living along Broadway 1 rather than Broadway 2. So we may expect that while tourists greatly magnify the difference between two parts of Broadway in social media activity and taxi use, the basic difference would exist anyway without them. (Proving or disproving this hypothesis will require further data analysis.)

Do Broadway 1 and Broadway 2 have the same temporal patterns?

In Broadway 1 (left graph) afternoon hours clearly dominate. In Broadway 2 (right graph) there is more activity in late evenings.

Note that since Broadway 1 part contains most of the Instagram images in our dataset, the left graph is quite similar to the very first graph above that shows activity for all Broadway. This is an important lesson - often when you are analyzing data representing some phenomena, the patterns you see actually correspond to only the dominant part of this phenomena. Other parts may have different patterns but they remain hidden unless we look at them separately. This is what happens in our case: only then we plotted data separately for Broadway 1 and Broadway 2, we realize that these two parts have distinct temporal patterns. (We may speculate that afternoons dominate in Broadway 1 because of tourists and also because of many people who work in downtown and midtown but go home to other boroughs or upper Manhattan in the evening).

To check that the temporal difference between two areas we are seeing is not due to particular days of the weeks, we plot the data separately for each day. In the following plots 1 to 7 labels correspond to Monday though Friday. First set of plots is for Broadway 1, and the second set is for Broadway 2.

Just as plotting data for all 13 miles of Broadway in Manhattan together hides the differences between its two parts, if we split each parts into smaller area, we may expect to find more differences. The advantage of simplification we used (Broadway 1 vs Broadway 2) is that the differences are become bigger and therefore they are easier to see. Dividing data into smaller and smaller subsets is a mixed blessing - we may gain in local specificity interpretability, but the distinctions can become smaller and smaller. Therefore its useful to both divide and gather - look at subsets of the data as well as look at data as a whole.

This is the end of our first post reporting analysis of the data we collected and organized for On Broadway project. More posts will be coming soon!

P.S. We are also working on a paper where we are comparing patterns in our datasets across all of NYC. We hope to release it on arxiv in April or May.

On Broadway - a new interactive urban data visualization from Selfiecity team

The interactive installation and web application On Broadway represents life in the 21st-century city through a compilation of images and data covering the 13 miles of Broadway that span Manhattan. The result is a new type of city view, created from the activities and media shared by hundreds of thousands of people.

On Broadway installation is currently on view in New York Public Library as part of the exhibition Public Eye: 175 Years of Sharing Photography. The exhibition will be opened until January 3, 2016. The installation uses a 46-inch multi-touch monitor.

ON BROADWAY from Moritz Stefaner on Vimeo.

video showing interaction with On Broadway

A photo of the part of On Broadway installation in New York Public Library

Media and web coverage:

1) 'On Broadway' Is a Stunning, Data-Driven Portrait of Life in New York City. Creators Project.

2) On Broadway shows city life through data cross-sections, FlowingData.

3) A Digital Collage of Broadway Made From Strips of Data. CityLab (The Atlantic).

4) Massive Data Visualization Brings NYC's Busiest Street To Life. Co.Design.

5) Take A Stroll Down Broadway Through Hundreds Of Thousands Of Instagram Images. Co.Exist.

6) Visualizing Life Along Broadway. Center for Data Innovation.

7) Data visualisatie brengt drukste straat NYC tot leven ("Data visualization brings the busiest street in NYC to life.") numrush.nl (Netherlands).

8) A febre da ‘selfie’ pelo mundo (video program aired on Globo TV, largest media channel in Brazil; includes coverage of On Broadway installation at NYPL and long interview with Lev Manovich)

9) Exploring a City Through Social Media Snapshots. PSFK.COM

10) On Broadway. Dragonweb (Hungary)

11) . Broadway the Digital Way. theinfomonkey.com

12) Coverage of "On Broadway" in Living in the Digital Age video program. Deutsche Welle (Germany)

13) NYC is a city that does sleep, a bit. blog.revolutionanalytics.com.

Image and data used in the project include:

660,000 Instagram photos shared along Broadway during six months in 2014
Twitter posts with images for the same period
over 8 million Foursquare check-ins (2009-2014)
22 million taxi pickups and drop-offs (2013)
selected economic indicators for the parts of NYC from US Census Bureau (2013).


Daniel Goddemeyer, Moritz Stefaner, Dominikus Baur, Lev Manovich.


Members of Software Studies Initiative: Mehrdad Yazdani, Jay Chow;
Brynn Shepherd and Leah Meisterlin;
PhD students at The Graduate Center, City University of New York (CUNY): Agustin Indaco (Economics), Michelle Morales (Computational Linguistics), Emanuel Moss (Anthropology), Alise Tifentale (Art History).

Interactive application:
The app offering similar experience and functions as the installation version is available from project web site: http://on-broadway.nyc/app/

Creating On Broadway:

Today companies, government agencies and other organizations collect massive data about the cities. This data is used in many ways invisible to us. At the same time, many cities make available some of their datasets and sponsor hackathons to encourage creation of useful apps using this data. Our project is supportive of the ideas to give citizens back their data, but it takes a unique approach to this goal. Using ‘On Broadway’ interactive interface, citizens can navigate their city made from hundreds of millions of data points and social media images they have shared.

How we can best represent a "data city"? We did not want to show the data in a conventional way as graphs and numbers. We also did not want to use another convention of showing spatial data – a map. The result of our explorations is "On Broadway": a visually rich image-centric interface, where numbers play only a secondary role, and no maps are used. The project proposes a new visual metaphor for thinking about the city: a vertical stack of image and data layers. There are 13 such layers in the project, all aligned to locations along Broadway. Using our unique interface (available as the online application and as a version for a large interactive multi-touch screen, currently installed at New York Public Library), you can see all data at once, or zoom and follow Broadway block by block.

Project updates and new research using the datasets we assembled for On Broadway will be published here as blog posts and as articles in academic journals.

A screenshot from the interactive application showing all Broadway view

A screenshot from the interactive application showing a closeup view

New essay about Maidan revolution and Instagram is available from "144 hours in Kyiv" project

A selection from all Instagram images publically shared in the central part of Kiev in the evening of February 18, 2014.

Last Fall we published a research project The Exceptional and the Everyday: 144 hours in Kyiv. This was the first project to analyze use of Instagram during a social upheaval. We used computational and visualization techniques to analyze over 13,000 Instagram images shared by people in Kyiv during one week of the Euromaidan Revolution in February 2014.

This revolution took place exactly one year ago - but it is not over yet. Immediately after the success of revolution events in February 2014, Russia sent its troops to annex Crimea, and later to support separatists in Eastern Ukraine. Russian military intervention in Ukraine continues, despite all international efforts to stop it.

Today we are publishing a new essay on our 144 hours in Kyiv project site:

Liquid Categories for Augmented Revolutions

Dr. Svitlana Matviyenko, University of Western Ontario

The essay is written by Dr. Svitlana Matviyenko. Born in Ukraine, she became the most interesting young literary critic in Kyiv. Currently she is completing her second PhD and teaching at the University of Western Ontario (Canada). While we worked on the project last year, we asked Svitlana to help us understand the use of Ukrainian tags in our Instagram dataset, given her connections in Kyiv and understanding of Maidan movements. Her insights were so interesting that we asked her to develop a longer text. The essay she wrote is very interesting and original - you must read it if you are interested in social movements, use of social media, contemporary media theory or events in Ukraine. We produced a few new data visualizations specifically for Svitlana, and I am happy to see that she put them to good use in her essay.

"Analyzing Cultural Data" - Lev Manovich's Spring 2015 course at The Graduate Center, CUNY

Analyzing Cultural Data

Spring 2014 semester / The Graduate Center, City University of New York
Wednesday, 4:15-6:15 pm / 3 credits
MALS 78500 / IDS 81650

Instructor: Dr. Lev Manovich, Professor, The Graduate Center, CUNY.
One of 50 most important people of 2014 (Verge Top 50 list, 2014;
one of 25 People Shaping the Future of Design (Complex, 2013)

Course description:

The explosive growth of social media and digitization of cultural artifacts by libraries and museums opened up exiting new possibilities for the study of cultural life. The “big data turn” already affected many fields in humanities (digital humanities, history, literary studies, art history, film studies, archeology, etc.), social sciences (e.g., computational sociology), and professional fields such as journalism and arts administration.

This course explores the possibilities, the methods, and the tools for working with cultural data sets. We will cover both small and big humanistic data and different data sources (images, video, texts, library collections, sensor data, etc.) Students will learn the practical techniques for organizing, analyzing and visualizing cultural datasets used leading open source tools. We will also discuss relevant readings and projects from a number of fields including digital art, artistic visualization, media theory, social computing, and science and technology studies.

The course is open to all graduate students, and does not require any previous technical knowledge. The practical tutorials and homework will be adjusted to fit students backgrounds and interests.

The course will use of some of the data sets from Dr. Manovich's Software Studies Initiative such as 10.5 million Instagram images shared in NYC in 2014.

Examples of projects from Software Studies Initiative:

The Exceptional and the Everyday: 144 hours in Kyiv
One million manga pages

When do people share? Comparing Instagram activity in six global cities

120,000 images from six global cities organized by average hue (distance to the center). The angle of each image is the day/time it was shared. All images use their local times (i.e. we keep offsets between the time zones). Because the temporal patterns for each city overlap, we see a uniform global image 24/7 cycle, without any separation between times of day. (This visualization and the post: Lev Manovich.)

In this post we compare patterns in Instagram activity between six cities: Bangkok, Berlin, Moscow, New York, Sao Paolo and Tokyo.

The analysis uses 120,000 images (20,000 from each city). To create this dataset, we first downloaded details of all geo-tagged Instagram images shared in the central same size area in each city during our full week (December 4-11, 2013; over 660,000 images in total). We then downloaded a random sample of 20,000 images from each city.

(This dataset was created as part of our Selficity project - see details below).

1. Numbers of Instagram images shared per hour in a 24 hour cycle

Berlin, Moscow, New York, and Sao Paolo have similar patterns: most images are shared between 1 and 11pm, with the peak around 7-8pm.

In Tokyo and Bangkok, there are two peaks: lunch time (1 or 2pm) and evening (7pm-11pm).

2. Numbers of Instagram images shared for every day of the week:

In most cities, people share most images on Saturday and Sunday. However, while in Berlin, Moscow and Tokyo and Bangkok, people appear to start their weekend already on Friday, in New York and Sao Paolo Friday is no different from previous weekdays.

(Because we are only using data for a single week, these patterns may not be typical. In particular, different Bangkok patterns maybe related to the political events in the city during that particular weeks.)

3. Number of Instagram users:

Our dataset contains twice as many users in New York than in Berlin -

4. Average number of images per user in each city:

- which means that while more people post on Instagram in NYC, on the average each user posts much fewer images (same as in Moscow and Sao Paolo)


1. Capture time versus share time.

Instagram allows users to post any image from their phones - i.e. users are not limiting to capture and immediately post images with Instagram app. Therefore, the volume of sharing does not directly tell when people take pictures, but rather than they use the app to share them.

2. Dataset details.

To create our data set, we used Gnip service to download Instagram data and images, so we were not constrained by Instagram API download limits. Both Instagram and Gnip provide only publicaly shared images. We were only downloading images with location data, which represents only a part of all shared images.

Selfiecity receives Golden Award in a data visualization competition

Our project selfiecity has received Golden Award from 2014 Information is Beautiful competition.

Selfiecity is a collaboration between the outstanding team of data visualization designers and programmers - Moritz Stefaner, Dominicus Baur and Daniel Goddemeyer - and five members of Software Studies Initiative. The collaboration was a great experience for us. Everybody worked hard. Moritz was the heart of the project designing data visualizations and the web site and making sure all pieces come together.

Amazingly, Moritz's another recent project OECD Regional Well-Being received the Silver Award in the same competition. Bravo, Moritz!

Our new animated Phototrails visualizations for Google Zeitgeist 2014 conference

Phototrails video 1 for Google Zeitgeist 2014 from Lev Manovich on Vimeo.

Phototrails video 2 for Google Zeitgeist 2014 from Lev Manovich on Vimeo.

This summer we received a commission to create new artworks to be shown during Google Zeitgeist 2014 conference. The conference is an invitation only two day event; this year it took place during September 14-16 in Paradise Valley, Arizona.

Google produced high quality video of many of the presentations. (You can also find videos of the talks from the earlier conferences at www.zeitgeistminds.com). For me personally, the highlights were the talks of Presidents Carter and Clinton, Google's own Eric Schmidt and Larry Page, and Lawrence Lessig - and also chatting with the people from Google X who were showing their amazing research.

We were asked to create animated versions of our Phototrails project. In the original project, we analyzed and visualized 2.3 million Instagram photos from 13 global cities. For the new Google Zeitgeist project, we created a number of new still visualizations using our our ImagePlot tool. We also used the animation option in ImagePlot to render a long sequence of visualization frames. The frames were rendered in 4K and then scaled to HD resolution. We used Premiere and After Effects to assemble the videos.

The two final videos which were exhibited at the conference are above. The fist video dissolves between both original and new Phototrails visualizations. The second is a slow zoom into the animated visualization of 120,000 Instagram photos from 6 cities. (Note: because of the Vimeo compression, the videos do not look as sharp as the originals).

The project was created by the original Phototrails team: Nadav Hochman, Jay Chow and Lev Manovich.

During the weeks leading to the event, we collaborated using Dropbox because each of us was in a different place: Nadav in NYC, Jay in California, and I was first in Brazil and then in Ireland. After we saw our videos playing at the site the morning of September 14th, we went back to the hotel, made some adjustments and rendered new versions. Good thing that ImagePlot (originally written by Manovich in 2010, and later expanded by Chow) kept rendering and never quit - even in Arizona's heat!

Lev Manovich's slides - Tate Live: On Mediated Experience: Oct 27, 2014

The Imaginary App: a new book from Software Studies series @ The MIT Press

The latest book from Software Studies series at The MIT Press:

The Imaginary App

Edited by Paul D. Miller (aka DJ Spooky that Subliminal Kid) and Svitlana Matviyenko. The MIT Press, 2014.

From the publisher:

Mobile apps promise to deliver (h)appiness to our devices at the touch of a finger or two. Apps offer gratifyingly immediate access to connection and entertainment. The array of apps downloadable from the app store may come from the cloud, but they attach themselves firmly to our individual movement from location to location on earth. In The Imaginary App, writers, theorists, and artists--including Stephen Wolfram (in conversation with Paul Miller) and Lev Manovich--explore the cultural and technological shifts that have accompanied the emergence of the mobile app. These contributors and interviewees see apps variously as “a machine of transcendence,” “a hulking wound in our nervous system,” or “a promise of new possibilities.” They ask whether the app is an object or a relation, and if it could be a “metamedium” that supersedes all other artistic media. They consider the control and power exercised by software architecture; the app’s prosthetic ability to enhance certain human capacities, in reality or in imagination; the app economy, and the divergent possibilities it offers of making a living or making a fortune; and the app as medium and remediator of reality.

Also included (and documented in color) are selected projects by artists asked to design truly imaginary apps, “icons of the impossible.” These include a female sexual arousal graph using Doppler images; “The Ultimate App,” which accepts a payment and then closes, without providing information or functionality; and “iLuck,” which uses GPS technology and four-leaf-clover icons to mark places where luck might be found.


Christian Ulrik Andersen, Thierry Bardini, Nandita Biswas Mellamphy, Benjamin H. Bratton, Drew S. Burk, Patricia Ticineto Clough, Robbie Cormier, Dock Currie, Dal Yong Jin, Nick Dyer-Witheford, Ryan and Hays Holladay, Atle Mikkola Kjøsen, Eric Kluitenberg, Lev Manovich, Vincent Manzerolle, Svitlana Matviyenko, Dan Mellamphy, Paul D. Miller aka DJ Spooky That Subliminal Kid, Steven Millward, Anna Munster, Søren Bro Pold, Chris Richards, Scott Snibbe, Nick Srnicek, Stephen Wolfram.

About Software Studies series at MIT Press:

The Software Studies series publishes the best new work in a critical and experimental field that is at once culturally and technically literate, reflecting the reality of today’s software culture. The field of software studies engages and contributes to the research of computer scientists, the work of software designers and engineers, and the creations of software artists. Software studies tracks how software is substantially integrated into the processes of contemporary culture and society. It does this both in the scholarly modes of the humanities and social sciences and in the software creation/research modes of computer science, the arts, and design.

Software Studies series co-editors:

Dr. Noah Wardrip-Fruin, The University of California, Santa Cruz (UCSC).

Dr. Lev Manovich, The Graduate Center, City University of New York (CUNY).

The cover of The Imaginary App

"The Exceptional and the Everyday: 144 hours in Kiev" - our new project exploring 13K Instagram photos from 2014 Ukrainian revolution


The Exceptional and the Everyday: 144 hours in Kiev is the first project to analyze the use of Instagram during a social upheaval.

Using computational and data visualization techniques, we explore 13,208 Instagram images shared by 6,165 people in the central area of Kiev during 2014 Ukrainian revolution (February 17 - February 22, 2014).

From The Everyday Project

Over a few days in February 2014, a revolution took place in Kiev, Ukraine. How was this exceptional event reflected on Instagram? What can visual social media tell us about the experiences of people during social upheavals?

If we look at images of Kiev published by many global media outlets during the 2014 Ukrainian Revolution, the whole city is reduced to what was taking place on its main square. On Instagram, it looks different. The images of clashes between protesters and the police and political slogans appear next to the images of the typical Instagram subjects. Most people continue their lives. The exceptional co-exists with the everyday. We saw this in the collected images, and we wanted to communicate it in the project.

The Exceptional and the Everyday: 144 hours in Kiev continues previous work of our lab (Software Studies Initiative, softwarestudies.com) with visual social media:

phototrails.net (analysis and visualization of 2.3 Instagram photos in 14 global cities, 2013)

selfiecity.net (comparison between 3200 selfie photos shared in six cities, 2014; collaboration with Moritz Stefaner).

In the new project we specifically focus on the content of images, as opposed to only their visual characteristics. We also explore non-visual data that accompanies the images: most frequent tags, the use of English, Ukrainian and Russian languages, dates and times when images their shared, and their geo-coordinates.

Project web site: