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Real-Time Cities: an Introduction to Urban Cybernetics Harvard Design School: SCI 0646900 Spring 2014 PowerPoint Presentation
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Jian He Exercise #1: Case Studies in Sensing and Data Collection. Real-Time Cities: an Introduction to Urban Cybernetics Harvard Design School: SCI 0646900 Spring 2014. Narrative Title.

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Jian He

Exercise #1: Case Studies in Sensing and Data Collection

Real-Time Cities: an Introduction to Urban Cybernetics

Harvard Design School: SCI 0646900

Spring 2014

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Narrative Title

The case studies that I collected mainly focus on the dynamic data of various scales. The data that has been visualized, either big data or small data, contributes to a deep understanding of different stories.

1 | The Next Big Spill Animation

2 | Foursquare check-ins show the pulse of New York City

3 | Interactive: What is the safest time to drive?

4 | Visualizing Life After Fukushima

5 | Bird flight paths

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1 | The Next Big Spill Animation

This project shows one day of marine traffic in the Baltic Sea. It is an unofficial visualization technology preview made originally for HELCOM (Baltic Marine Environment Protection Comission).

Project Video: http://tinyurl.com/pvrw24j

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1 | The Next Big Spill Animation

How was the data collected?

The data was collected from HELCOM (Baltic Marine Environment Protection Commission) Automatic Identification System (AIS).

Why was the data collected? What is interesting about the data?

The data was collected to visualize the marine traffic and accident for the Baltic Marine Environment Protection Commision.

What stories about the urban dynamics can the collected data tell?

It shows one day of shipping in the Baltic Sea. It is a great example of the use of data visualisation to make a political (or in this case environmental) point.

What sort of questions about urban dynamics can be answered by looking at the data?

It suggests how to protect the vulnerable and polluted sea in the future.

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?

It focuses on the Baltic Sea region. The raw data was from the HELCOM AIS. This project picked on day data, showing one day of shipping in the Baltic Sea.

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1 | The Next Big Spill Animation

How are particular patterns highlighted through techniques for tagging the data in order of their importance?

It illustrates the incredible density of maritime traffic on the Baltic Sea and just how lucky the area is to have thus far avoided a serious environmental disaster.

How does the original question to be addressed operate as the benchmark for eliminating unnecessary details in the data?

The map provokes a questioning approach to what you’re seeing as the story unfolds. The zooming, panning and soft-focus gives the map a strong aesthetic and the use of a sensible soundscape adds to the atmosphere.

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?

Dynamic. 00:01 hours.

Who is the target audience of the data presentation?

Baltic Marine Environment Protection Commision, as well as the public.

What are their goals when approaching the data presentation? What do they stand to learn?

The map is the main actor but the supporting cast of captions makes it easy to understand. This map goes beyond simply animating data and expecting the viewer to figure out what’s going on. It asks a question in the title, builds expectation with a series of statements before the map is revealed..

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2 | Foursquare check-ins show the pulse of New York City

Foursquare check-ins can be self-encapsulated and personal to the individual, where each dot represents a specific place in time. Each point represents a stop at a restaurant, store, or place of business.

Because of Foursquare, there's an added dimension of location categories, so color codes show people go to work, grab lunch, shop, and get after-work drinks.

Project Video: http://vimeo.com/75413842/

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2 | Foursquare check-ins show the pulse of New York City

How was the data collected?

The data was collected through Foursquare, a location-based social networking website for mobile devices, such as smartphones. Users "check in" at venues using a mobile website, text messaging or a device-specific application by selecting from a list of venues the application locates nearby. Location is based on GPS hardware in the mobile device or network location provided by the application, and the map is based on data from the OpenStreetMap project. 

Why was the data collected? What is interesting about the data?

The data shows the pulse of the most popular cities on Foursquare. Taking a year of check-ins and condensing them to show what each city looks like on an average day.

What stories about the urban dynamics can the collected data tell?

The collected data shows “the urban ebbs and flows”. It not only indicates where people are but, more significantly, how, when, and why they're going there.

What sort of questions about urban dynamics can be answered by looking at the data?

It not only indicates where people are but, more significantly, how, when, and why they're going there.

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?

It collects a massive amount of location data 4.5 billion check-ins shared by its 40 million users according to the website.

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2 | Foursquare check-ins show the pulse of New York City

How are particular patterns highlighted through techniques for tagging the data in order of their importance?

Color-coded signals show at what times of day and by what means users travel to various locations. In New York, for example, you can see yellow streaks of light zoom into Manhattan as users commute to work, and then slip away as they head home; at night, the city lights up in blue as more users head to nightlife spots.

How does the original question to be addressed operate as the benchmark for eliminating unnecessary details in the data?

All the information are visualized using different color tunes. Thus it is very informative and also it is easy to read.

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?

Dynamic. Hourly?

Who is the target audience of the data presentation?

The public.

What are their goals when approaching the data presentation? What do they stand to learn?

The data is informative, giving a strong overview of what makes cities tick during the day. 

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3 | Interactive: What is the safest time to drive?

New statistics show the safest and most dangerous times of the day, week, and month to drive. Showing mid-2012 through to mid-2013, the animations indicate the frequency of collisions using red flashes.

Project Video: http://www.theguardian.com/news/datablog/interactive/2013/oct/27/interactive-safest-time-to-drive

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3 | Interactive: What is the safest time to drive?

How was the data collected?

The data was collected through Source: NRMA Insurance, average of NSW, QLD, SA, WA, ACT; Australian Road Deaths Database, Bureau of Infrastructure, Transport and Regional Economics; Road traffic crashes in New South Wales (PDF), Centre for Road Safety, NSW Transport.

Why was the data collected? What is interesting about the data?

The statistics show the safest and most dangerous times of the day, week, and month to drive.

What stories about the urban dynamics can the collected data tell?

It shows Average percentage of collision claims: Average percentage of claims by day, Average number of fatalities by day; as well as different types of collision. 

What sort of questions about urban dynamics can be answered by looking at the data?

Which is the safest month of the year? Which day of the week is the worst? What is the most dangerous time of day? What are the most common types of crashes? How does vehicle colour affect the risks of crashing?

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?

It is dealt with the data of whole country however limiting them to certain areas/regions according to the data source they have.

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3 | Interactive: What is the safest time to drive?

How are particular patterns highlighted through techniques for tagging the data in order of their importance?

The number of collisions grew through the working week, peaking on Friday, with 16% more collisions than the average day. Sunday was the quietest day, with 27% fewer collisions than the average day. Unlike collisions, fatalities peaked on Friday and weekends. Saturday, which had 29% more deaths than the average day, was the worst.

How does the original question to be addressed operate as the benchmark for eliminating unnecessary details in the data?

The project eliminates unnecessary data to certain area. All the information are simplified using different colors and symbols.

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?

Dynamic.

Who is the target audience of the data presentation?

The public.

What are their goals when approaching the data presentation? What do they stand to learn?

The projects suggest safety driving for the public.

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4 | Visualizing Life After Fukushima

This project is a web publication about the Fukushima nuclear disaster that took place in Japan in 2011. The reporter Marcel Gyr and photographer ChristophBangert revisited the same people they met two years earlier and interviewed them again to hear their personal accounts on how their lives have changed during that time. It aims to create an online reading experience to present the long form text, the photos and the video footage in a sincere but pleasant way. To support the readers in their understanding of the text, the project creates several visualizations to clarify and point out important facts. Since the article would include media such as video, photos and visualizations, this project wants to use them in a way that explained certain subtopics of the stories without creating a distraction for the reader.

Project Video: http://datavisualization.ch/inside/how-we-visualized-life-after-fukushima/

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4 | Visualizing Life After Fukushima

How was the data collected?

The data was collected through field work by data journalist.

Why was the data collected? What is interesting about the data?

The first-hand data speaks to the life of people after the big event. The photos, videos and interviews help to render the vivid life of the local people, their experience during and after the disaster.

What stories about the urban dynamics can the collected data tell?

The collected data shows the radiation measured directly in the Fukushima reactor, the concrete evacuation zones mandated by the government and how far the people were evacuated from their homes; also, informs about which areas are currently contaminated.

What sort of questions about urban dynamics can be answered by looking at the data?

The concrete evacuation zones and how far the people were evacuated from their homes: classified as “Area to which people may return but not stay overnight”, “Restricted area”, “Difficult area”, “Fully evacuated area” and “Planned evacuation area”.

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 mainly deal with the stories of the local people in the format of photos, videos and interviews. Four people the journalist interviewed played an important role that the story should be told around them.

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4 | Visualizing Life After Fukushima

How are particular patterns highlighted through techniques for tagging the data in order of their importance?

The radiation measured in the Fukushima reactor; evacuation zones and how far the people were evacuated from their homes.

How does the original question to be addressed operate as the benchmark for eliminating unnecessary details in the data?

This project tries to use the data in a way that explained certain subtopics of the stories without creating a distraction for the reader. The placement of them was in many ways crucial both for keeping the amount of material similar through all the chapters, as well as creating a balance between visual elements and the text.

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?

Both static and dynamic.

Who is the target audience of the data presentation?

The public. It’s an online journalistic project.

What are their goals when approaching the data presentation? What do they stand to learn?

This project wants to use data visualization in a way that explained certain subtopics of the stories.

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5 | Bird flight paths

Video artist and RISD professor Dennis Hlynsky films birds flying, then composites frames together so that we see their flight paths outlined — kind of like a mouse pointer with trails turned all the way up. The results are quietly gorgeous. According to Hlynsky, the video above, of starlings congregating on some power lines, “gets good at around 6 minutes.”

Project Video: http://vimeo.com/32363204/

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5 | Bird flight paths

How was the data collected?

Hlynsky first started filming birds in 2005 using a small Flip video recorder, but now uses a Lumix GH2 to record gigabytes of bird footage from locations around Rhode Island. 

Why was the data collected? What is interesting about the data?

Each “flock” has a form, a rhythm, and pattern to the glyphs they leave as they perambulate.

What stories about the urban dynamics can the collected data tell?

Most of the image is captured the normal way, but the birds themselves leave behind a record of their flight, twisting and turning into captivating patterns that are captured on the screen instead of fading away.

What sort of questions about urban dynamics can be answered by looking at the data?

The author wondered what would happen if he could better trace the flight paths of individual birds, what kinds of patterns would emerge from these flying social networks?

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 deals with different moments of bird flying, which last about a few minutes.

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5 | Bird flight paths

How are particular patterns highlighted through techniques for tagging the data in order of their importance?

Each of the trails is made bt a single bird. When the bird is flying fast, the images are separated. When the bird is slowing to land, they bunch together. 

How does the original question to be addressed operate as the benchmark for eliminating unnecessary details in the data?

Non-moving objects like trees and telephone poles remain stationary, and with the added ambient noise of where he was filming, an amazing balance between abstraction and reality emerges. 

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?

Dynamic.

Who is the target audience of the data presentation?

The public.

What are their goals when approaching the data presentation? What do they stand to learn?

The birds aren’t digitally animated or layered in any way, but are shown just as they’ve flown, creating a sort of temporary time-lapse.