Manovich's lectures May - July, 2015




Photo: Lev with members of Software Studies Initiative in front of HiperWall (next generation visualization system featuring multiple flat screens, Calit2. Dataset: 3200 selfies from Selfiecity project.


May 20 - July 10, 2015:


1. LDV Vision Summit, New York City, 5/20 (keynote)

2. Museum of Contemporary Art of Vojvodina, Novi Sad, 5/25

3. Belgrade Cultural Center, Belgrade, 5/26

4. Technarte 2015, Bilbao, 5/29

5. American Center, Moscow, 6/6

6. Da-Da Architecture School, Naberezhnye_Chelny (Russia), 6/7

7. Kazan (Russia), 6/8

8. School of Urban Studies, Higher School of Economics, Moscow, 6/9

9. Strelka Institute, Moscow, 6/10

10. School of Urban Studies, Higher School of Economics, 6/11 (roundtable)

11. Philosophy department, Moscow State University, 6/11

12. The European University, St. Petersburg, 6/15 (roundtable)

13. Digital Methods Summer School, University of Amsterdam, 6/29 (keynote)

14. "Big Data in the Context of Culture & Society," House of Electronic Arts Basel, 7/3 (keynote)

15. Europeana Creative Culture Jam, Vienna, 7/10 (keynote)



Manovich's lecture in St. Petersburg, June 15, 2015





в понедельник 15 июня 2015
в рамках серии мероприятий летней школы "Адаптивный город"
Высшей школы урбанистики при поддержке Института урбанистики «Среда» и ЕУСПб

пройдет лекция и дискуссия на тему "Информационный ландшафт города"

Спикер: Лев Манович (The Graduate Center, CUNY; Software Studies Initiative, softwarestudies.com)
Модератор дискуссии: Диана Вест (ЕУСПб, центр STS)
Участники дискуссии:
- Данияр Юсупов ( Институт «Среда» / СПбГАСУ)
- Александр Бухановский (НИУ ИТМО),


Мероприятие состоится в 19.00 в Белом Зале Европейского университета в Санкт-Петербурге
Гагаринская ул., 3-А.

Предварительная регистрация не требуется.



О летней школе "Адаптивный город":

http://urban.hse.ru/announcements/150393836.html
Высшая школа урбанистами НИУ ВШЭ запускает цикл мероприятий, посвященных концепции Adaptable city - устойчивого развития города с учетом сегодняшних экономических условий и социальных потребностей жителей с их быстро меняющейся моделью потребления, перемещения и коммуникации.
Концепция адаптивного города — одна из доминант в стратегии развития Высшей школы урбанистики. Мы пригласили на лекции и дискуссии международных исследователей из разных областей, и наша задача — вместе изучить и осмыслить сегодняшние общественные перемены в рамках городского пространства.
Выступления будут проходить на площадках Высшей школы урбанистики, городской библиотеки, в Институте «Стрелка».

ВШУ планирует презентовать свою программу в Казани, Санкт-Петербурге и Риге,
тем самым расширить географию заинтересованных студентов и привлечь внимание специалистов из разных областей к новым подходам к развитию городов.

Партнером летней школы ВШУ в Петербурге являются:
Институт урбанистики СРЕДА (Санкт-Петербург) sredadesign.org
Центр исследований науки и технологии Европейского университета в Санкт-Петербурге (Санкт-Петербург), (http://eu.spb.ru/research-centers/sts)

Спикер: Лев Манович / Lev Manovich,
Ведущий специалист в области теории новых медиа, автор пяти книг и более 130 статей, изданных на 30 языках. Профессор в университете Калифорнии, Сан-Диего (UCSD), в Visual Arts Department, в European Graduate School в Швейцарии и в университете Де Монтфорт в Англии. В 2007 году основал лабораторию Software Studies Initiative при университете Калифорнии. Ранее, в 2001 году, издательство MIT Press выпустило книгу The Language of New Media, ставшую впоследствии одной из основополагающих работ в сфере новых медиа. Работает над применением компьютерного анализа для исследования культурных трендов. В своей лекции Лев Манович расскажет о том, как и почему анализ пользовательского контента помогает нам лучше понимать современное общество.
http://manovich.net/index.php/about

Модератор: Диана Вест
Доктор истории и теории архитектуры (Ph.D., Принстонский университет; тема диссертации: «"Киберсоветика": планирование, проектирование и кибернетика советского пространства, 1954-1986). Автор научных публикаций по кибернетике, истории искусства и технологий, организации пространства и применении кибернетических идей в урбанистике. Преподавала в Принстонском университете, Университете Дрекселя и других. Член ряда международных профессиональных ассоциаций: Ассоциации арт-колледжей (CAA), Ассоциации славянских, восточноевропейских и евразийских исследований (ASEEES), Общества историков архитектуры (SAH), Общества историков науки (HSS), Общества историков техники (SHOT). В настоящее время – заместитель директора по проектам Центра исследований науки и технологий (STS) Европейского университета в Санкт-Петербурге и научный сотрудник проекта Russian Computer Scientists at Home and Abroad.
http://contextfound.org/speakers/diana-west


Эксперты:

Александр Бухановский, доктор технических наук, профессор кафедры информационных систем, директор НИИ Наукоемких компьютерных технологий Санкт-Петербургского государственного университета информационных технологий, механики и оптики. Специалист в области компьютерного моделирования сложных систем с использованием высокопроизводительных вычислений. Имеет значительный опыт профессиональной деятельности в области разработки распределенных предметно-ориентированных программных комплексов на основе интеллектуальных технологий. Автор более 150 научных работ.
http://www.ifmo.ru/ru/viewperson/309/buhanovskiy_aleksandr_valerevich.htm

Данияр Юсупов, архитектор-урбанист, преподаватель СПбГАСУ, один из со-основателей группы U:lab и экспертной платформы «Открытая Лаборатория Город. О.Л.Г.», эксперт Института урбанистики «Среда»; опытный специалист в области междисциплинарных урбанистических проектов; автор ряда методик диагностики и картирования состояния и потенциала развития городской среды, аудита городского пространства для размещения ключевых объектов, динамической стратегии развития территории.
https://readymag.com/u97509577/sreda-mail/6/

Schedule and topics of Manovich's lectures in Russia, June 6-15, 2015





This is my schedule of lectures in Moscow, St. Petersburg, Naberezhnye_Chelny and Kazan. I will add details (locations and times) as I receive them.


JUNE 6: Lecture in Moscow, at American Center


JUNE 7: Lecture in Naberezhnye_Chelny, at Da-Da Architecture School (https://en.wikipedia.org/wiki/Naberezhnye_Chelny)


JUNE 8: Lecture in Kazan

topic: "Analyzing Cities using visual social media"



JUNE 9: Lecture for students at School of Urban Studies, Higher School of Economics (HSE), 7pm

topic: "How to compare one million images? Analysis and visualization of patterns in art, games, comics, cinema, web, print, and user-generated content"



JUNE 10: public lecture at Strelka (Moscow) - 8pm

topic: "How to Analyze Culture Using Social Networks" / abstract



JUNE 11: roundtable at School of Urban Studies, Higher School of Economics, 3-5pm

topic: "City, Urbanism, Social Media"



JUNE 11: Moscow State University, Faculty of Philosophy, meeting and roundtable with students and faculty, 6-8pm (location will be added)

topic: "Digital Humanities, Computational Social Science, Software Epistemology" / relevant post


JUNE 13: Presentation at electromuseum.ru, Moscow



JUNE 15: roundtable at St. Petersburg, at The European University

topic: Urban Information Landscapes




My visit is organized by U.S. Embassy in Moscow, Strelka Institure (Moscow), School of Urban Studies (HSE), and Habidatum company. I am very grateful to everybody involved for making this trip possible.

If you want to meet me during my visit to Russia, please contact me via email (my email address is here).





"Visualizing Instagram: selfies, cities, and protests" - lecture by Manovich in Belgrade, 5/26/2015



Interaction with On Broadway installation currently on view at New York Public Library (NYPL).


Visualizing Instagram: selfies, cities, and protests

Belgrade Cultural Centre, Belgrade
May 26, 2015 - 7pm


Abstract

The explosive growth of social media and cultural content on the web along with the digitization of historical cultural artifacts opened up exiting new possibilities for the analysis of cultural trends, patterns and histories. Today, thousands of researchers have already published papers analyzing massive cultural datasets in many areas including social networks, urban data, online video, web site design fashion photography, popular 20th century music, 19th century literature, etc. While most of this work is done by researchers in computer science, a number of very interesting projects were also created by data designers, media artists, and humanities scholars. Here are selected examples of this work.

In my lecture I will show a number of projects created in our lab (softwarestudies.com) since 2008. They include comparison of 2.3 million Instagram images from 13 global cities (phototrails.net), interactive installation exploring Broadway street in NYC using 30 million data points and images (on-broadway.nyc), a web tool for comparison of selfie photos from 5 cities (selfiecity.net) and analysis and visualizations of 1 million manga pages and 1 million artworks from the largest network for “user-generated art” (deviantart.com). I will also talk about our current work in progress - analysis of 260 million images shared on Twitter worldwide during 2011-2014.

I will discuss how we combine methods from data science, media art, and design, and how the
use of big cultural data helps us question our existing assumptions about culture. More details about our research

Finally, I will also offer comments about the new emerging "social physics" that uses big data and computation to study the social. Our spontaneous online actions become source of behavioral and cognitive data used for commercial and surveillance purposes - improving results of search engines, customizing recommendations, determining what are the best images to be used in online ads, etc. The science used to focus on nature, with smartest people coming to work in physics, chemistry, astronomy and biology. Today, the social has become the new object of science, with hundreds of thousands of computer scientists, researchers and companies mining and mapping the data about our behaviors. In this way, the humans have become the new "nature" for the sciences. The implications of this grand shift are now beginning to unfold. Will we become the atoms in the "social physics," first dreamed by the founder of sociology Auguste Comte in the middle of 19th century? Will predictive analytics rule every aspect of our lives? What happens to the society and the individuals when they can rationalize all their communication - the way millions of people already using their Twitter and Facebook analytics to tailor their posts to their audiences?


"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:


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).

Artists:

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

Contributors:

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:

Selfiecity
Phototrails
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)





Notes:

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!


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