Real-Time Cities: an Introduction to Urban Cybernetics Harvard Design School: SCI 0646900

1. TIFFANY CHEN Exercise #1: Case Studies in Sensing and Data Collection Real-Time Cities: an Introduction to Urban Cybernetics Harvard Design School: SCI 0646900 Spring 2014

2. CURATING URBAN IDENTITIES The urban identity is both an individual and crowd-based phenomenon. As many user-generated content sharing networks today have the evolved to become real-time urban maps, we are able to create alter-egos of the city that superimpose physical infrastructure with temporal social “buzz.” Through the harnessing and hacking of big data to curate personal data narratives, we can ultimately begin to map a more human-centered urban experience. 1 | FOURSQUARE TIME MACHINE 2 | THE VIRTUAL CITY 3 | URBAN SENSING - LISTENING TO THE DIGITAL CITY 4 | METROPOLITAIN 5 | SITEGEIST

3. 1 | FOURSQUARE TIME MACHINE The Foursquare Time Machine is a data visualization interface which allows users to explore and relive their history of check-ins. By mapping an archive, this interface is ultimately able to reveal an individual’s patterns of movement, urban habits, and suggest future explorations. Project Video: http://www.youtube.com/watch?v=sRiXJYi1y8w

4. 1 | FOURSQUARE TIME MACHINE Play mode plays back your lifetime of foursquare history. Explore Mode user can step through each individual check-in with detail. Recommendations where to go next. The ultimate goal of the experience is to inspire Foursquare users to do more.

5. 1 | FOURSQUARE TIME MACHINE How was the data collected? The data was collected via Foursquare Why was the data collected? What is interesting about the data? The data was collected in order to create a visualization experience that allows Foursquare users to explore and relive check-ins, discover new places to go, and create a shareable infographic. What stories about the urban dynamics can the collected data tell? By analyzing the data collected, the visualization is able to provide new recommendations for the user. The ultimate goal of the experience is to inspire Foursquare users to do more and discover new places within their urban environment. What sort of questions about urban dynamics can be answered by looking at the data? Urban habits and individual patterns are revealed in a visual synthesis of Foursquare check-in data. How is the magnitude of the data is dealt with; limiting the collected data, limiting the dimensions in the data set, or abstracting the data? The dimensions of the data set is limited, because only the location of the check-in is visualized and expressed in the main video based visualization. Peripheral data attached to the check-ins such as frequency are presented in the supplementary inforgraphic.

6. 1 | FOURSQUARE TIME MACHINE How are particular patterns highlighted through techniques for tagging the data in order of their importance? The infographic produced by this interface illustrates many quantifiable variables which are highlighted through data tagging techniques. Every check-in is categorized different venue types and also placed in comparison with the data set as a whole. ‘Favorite’ spots and synthesized ‘Activity’ meters also present urban patterns of the user, which may not have been apparent before. How does the original question to be addressed operate as the benchmark for eliminating unnecessary details in the data? This data visualization reflects the direct goals of Foursquare as a location-based social media application. All data used was preset by Foursquare interests and variables. Is the data of a static or dynamic nature? If dynamic, what is the frequency of change and what happens when it starts to change? The data is static in the sense that the main visualization is simply playing back an archive of past check-ins by the user. However, it is also dynamic, because there is an additional ‘explore mode’ where the user can step through each individual check-in in detail, and the interface analyzes specific data to show new recommendations based on past locations. Who is the target audience of the data presentation? The target audience includes both new and seasoned Foursquare users. The data presentation showcases the data in a way that is fun for the casual user, but also goes deeper for those who want to see even more. What are their goals when approaching the data presentation? What do they stand to learn? The ultimate goal of the experience is to inspire Foursquare users to do more with the application, and incite new meaning behind the check-ins.

7. 2 | THE VIRTUAL CITY The Virtual City is a digital interface, which presents the alter-go of a case study city via data collected from Facebook check-ins, likes, and posts. Virtual buildings are shaped over a time span of one week corresponding to a public space in the real world. As the shapes of the buildings grow and expand according to number of check-ins and likes, this data visualization presents the temporal and often ephemeral interaction of people with the city they inhabit. Project Source: http://urban-sensing.eu/?p=648

8. 2 | THE VIRTUAL CITY

9. 2 | THE VIRTUAL CITY How was the data collected? Data was collected from Facebook via check-ins, likes, and posts. Why was the data collected? What is interesting about the data? The goal of this project was to visualize the “virtual alter egos” of the cities of Arnhem, Amsterdam, and Schiphol. The data collected is interesting, because it is a representation of the activity of a city which usually does not have a physical or visual presence. What stories about the urban dynamics can the collected data tell? Data collected from Facebook are visualized as virtual buildings, which are shaped over a time span of a week (beginning of the 2012 European Football Cup). What sort of questions about urban dynamics can be answered by looking at the data? The virtual buildings which were shaped in height depending on the number of check-ins and in width depending on the number of likes, depict a physical presence of urban activity, which is often a temporal and ephemeral phenomenon. How is the magnitude of the data is dealt with; limiting the collected data, limiting the dimensions in the data set, or abstracting the data? The data collected was limited, because specific variables were chosen to correlate directly to the height and width of the virtual buildings. Only those specific variables were collected.

10. 2 | THE VIRTUAL CITY How are particular patterns highlighted through techniques for tagging the data in order of their importance? Patterns of urban “buzz” and social media density are reflected through the tagging of Facebook check-ins, likes, and posts. The shaping of the virtual buildings not only represents the number of people who may have inhabited that space during a period of time, but also the number of people simply talking about or digitally interacting with the space via social media. How does the original question to be addressed operate as the benchmark for eliminating unnecessary details in the data? Since this project was mainly interested in creating a representation of a city’s “alter-ego,” emphasis was placed on data that would be otherwise physically invisible, such as Facebook likes and posts. Is the data of a static or dynamic nature? If dynamic, what is the frequency of change and what happens when it starts to change? The data was dynamic, because it originally correlated to live Facebook updates over a span of a week. After the data was collected and set within the formal shapes of the virtual buildings, it then became static. Who is the target audience of the data presentation? The target audience is for those familiar with Facebook and the activities related to social media updating. What are their goals when approaching the data presentation? What do they stand to learn? The goal of this project was to create an interactive application which could allow for a “phantom” city to appear within the context of the physical city.

11. 3 | URBAN SENSING - LISTENING TO THE DIGITAL CITY Urban Sensing is a project which aims to analyze and assess moods, opinions, and trends in an urban population via social media. By synthesizing data on citizen expectations for urban planners and responsible authorities, the project ultimately intends to help cities achieve a higher quality of life in the future. Milan Main directions that people move in Milan, based on tweets between 9:00 AM and 12:00 AM Project Source: http://audi-urban-future-initiative.com/blog/the-city-makes-its-voice-heard

12. 3 | URBAN SENSING - LISTENING TO THE DIGITAL CITY How was the data collected? The data is collected through user-generated updates over social networks and digital media. Why was the data collected? What is interesting about the data? This platform allows for the analysis of users’ perceptions related to specific geographic areas. This user-generated social media data is interesting, because it informs the platform viewer’s understanding of both what the city is, and what it could be. What stories about the urban dynamics can the collected data tell? By extracting patterns of use and perceptions related to city spaces through User Generated Content, the platform also provides insight into the possibilities of bottom-up city initiatives that respond to uncovered needs and desires. What sort of questions about urban dynamics can be answered by looking at the data? This data provides insight into how specific user groups use public spaces, and identifies locations suitable for design interventions. How is the magnitude of the data is dealt with; limiting the collected data, limiting the dimensions in the data set, or abstracting the data? Data is limited and abstracted to produce urban “sentiment” maps, which reflect positive and negatives emotions based on tweets gathered in one day in one specific region (ie. The Netherlands).

13. 3 | URBAN SENSING - LISTENING TO THE DIGITAL CITY How are particular patterns highlighted through techniques for tagging the data in order of their importance? Emotional sentiments in different areas of an urban environment are highlighted through the tagging of relevant twitter data as either positive or negative. Patterns of movement can also be tagged by isolating GPS information attached to collected twitter data. How does the original question to be addressed operate as the benchmark for eliminating unnecessary details in the data? The project’s interest in understanding the emotional sentiment and movement of an urban environment allowed for Twitter data to be tagged and generalized as either positive or negative, eliminating all extraneous social details. Is the data of a static or dynamic nature? If dynamic, what is the frequency of change and what happens when it starts to change? The data is presented in a static map format, that are framed as snapshots based on specific data instances for a given location and spectrum of time. Who is the target audience of the data presentation? The target audiences for this data presentation are the project’s corporate partners who wish to gain insight in their urban policy, communication, and public transportation decision making processes. What are their goals when approaching the data presentation? What do they stand to learn? The goal of the project was to give insight to areas lacking in structures offered by institutions and city administrations through bottom-up digital engagement with the citizen population.

14. 4 | METROPOLITAIN Metropolitain is an interactive data visualization interface, which aims to show a more dynamic reality of a commute on the Parisian metro. By transforming the standard metro map from 2D to 4D, this project allows users to understand not only the spatial relationship of metro stops, but also visualize the temporal nature of station crowd density from their chosen point of reference. Project Source: http://vimeo.com/62691115

15. 4 | METROPOLITAIN How was the data collected? Data on metro crowd turnouts was made openly available by the RATP (Autonomous Operator of Parisian Transports) and datasets about time between stations were provided by the start up Isokron. Why was the data collected? What is interesting about the data? The data was collected to create an interface that would enable users to visualize datasets surrounding transportation time between metro stations, at any hour of the day, as well as the number of people arriving at each station. What stories about the urban dynamics can the collected data tell? The relationship between the individual urbanite and the urban crowd is one interesting story that can be depicted from the collected data. The temporal nature of the city can also be illustrated through the pulsing visualizations of crowd movement and intensity. What sort of questions about urban dynamics can be answered by looking at the data? The urban dynamics of crowd density and movement can be analyzed with the data visualization of this project. How is the magnitude of the data is dealt with; limiting the collected data, limiting the dimensions in the data set, or abstracting the data? The data set collected was limited to specific metro stations and their corresponding crowd density.

16. 4 | METROPOLITAIN How are particular patterns highlighted through techniques for tagging the data in order of their importance? This visualization interface not only presents dynamic data on the metro system, but also allows for the user to insert themselves as a point of reference within the larger context. How does the original question to be addressed operate as the benchmark for eliminating unnecessary details in the data? With time and space acting as the two main variables, this project focused specifically on data that reflects on the fact that commuters tend to select their journeys on the metro based on the perceived smallest distance between two points. Is the data of a static or dynamic nature? If dynamic, what is the frequency of change and what happens when it starts to change? The data is dynamic and fluctuates hourly according to number of people arriving at each station. Who is the target audience of the data presentation? The target audience includes all commuters of the Paris metro. What are their goals when approaching the data presentation? What do they stand to learn? The goals of this data presentation were to represent the Paris underground system in a dynamic way which would better show the true reality of a commute. These data visualizations reveal the density and 24-hour affluence of the city’s metro backbone.

17. 5 | SITEGEIST Sitegeist is a mobile app which curates a snapshot of a user’s given location. By curating a simple “at-a-glance” interface, this applications allows the individual to not only understand their urban context, but also place themselves within an intangible social construct. Project Source: http://sitegeist.sunlightfoundation.com/

18. 5 | SITEGEIST

19. 5 | SITEGEIST How was the data collected? Sitegeist draws on free API’s such as the U.S. Census, Yelp, and Facebook. Why was the data collected? What is interesting about the data? Data is collected in this application to curate a profile on an individual’s location. The data collected is interesting simply because it is presented in a contextual and instanteous format. What stories about the urban dynamics can the collected data tell? This application allows for the user to truly tap into the “pulse” of their location. From demographics to the latest popular spots, Sitegeist curates neighborhood profiles based on the user’s current location. What sort of questions about urban dynamics can be answered by looking at the data? With datasets from multiple social media and census sources, this application is able capture snapshots of many temporal and spatial characteristics of a given urban context. From spots deemed most popular by number Facebook likes, to the closest movie theater in miles, the app provides a multifaceted representation of a single GPS location. How is the magnitude of the data is dealt with; limiting the collected data, limiting the dimensions in the data set, or abstracting the data? The data collected is limited.

20. 5 | SITEGEIST How are particular patterns highlighted through techniques for tagging the data in order of their importance? Since all data collected and presented in this application is intended to not only give the user an idea of the neighborhood, but also allow the user to form an identity within the given context, this highlights patterns of social conventions and urban gentrification How does the original question to be addressed operate as the benchmark for eliminating unnecessary details in the data? The “at-a-glance” intention of the application streamlines data sets into single minimalist infographics. Only data for the user’s chosen location is presented, thus eliminating all unnecessary details. Is the data of a static or dynamic nature? If dynamic, what is the frequency of change and what happens when it starts to change? The data is presented in a static format, but dynamically updated in sync with its sources. Who is the target audience of the data presentation? The target audience includes smart phone users and urban dwellers interested in becoming more contextually aware. What are their goals when approaching the data presentation? What do they stand to learn? The goal of Sitegeist is to present solid data in a simple “at-a-glance” format to help users tap into their current locations.