Phase 2 : answer question 5,6,8

5.Are there historical precedents for this kind of work with data? For this, please explore and cite at least one of the books from the “Historical/Foundational” section of the course-reserved books (see syllabus).

 

City Icon is a generative city simulation featured at Sustainable Cities exhibition in the Crystal building in London.
The city is a mix of intersecting systems, fluently transforming and interacting with each other. Traffic jams, water streams, nature enclaves, emergency states and energy sources appear and disappear during the day giving a feeling of dynamic but well balanced city.

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http://variable.io/city-icon/

 

Visualizations of mobility data such as taxi or bike sharing trips have become very popular. I found the one of the best most recent examples is cf. city flows developed by Till Nagel and Christopher Pietsch at the FH Potsdam. cf. city flows visualizes the rides in bike sharing systems in New York, Berlin and London at different levels of detail, from overviews of the whole city to detailed comparisons of individual stations:

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https://uclab.fh-potsdam.de/cf/

 

6.What kinds of data visualizations can help them? What kinds of visualizations can’t help them? For this, it may help to explore the books in the “Technique/Science” section of the course reserves (see syllabus).

I try to use the QGIS to show people the movement of the route of each person.

The data visualizations aiming to different tester ride in NYC public spaces. The visualizations have a focus on the patterns of moving entities in public like riders as well as the interaction between these entities and physical structures like roads, sidewalks, buildings and parks. The data visualization intends to provide strong visuals on what we all experience in our daily lives in different routes.

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8.If applicable, please integrate your thoughts on speakers and/or site visits so far.

*User experience lead what we will visualization in each project.

*communicate information clearly and efficiently

*makes complex data more accessible, understandable and usable.

*Color and structure is important.

 

Rahul Bhargava: Data Storytelling

final idea-icm

My Refrigerator

 

What’s in Your Fridge?

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GOAL:

  • Ability to create shopping lists based on what is currently in your fridge.
  • Receive notifications when items in your fridge are about to expire.
  • Know the shelf life of various foods, fruits and veggies.
  • Know how to properly store various foods.

 

search in app store :

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Customer Reviews

This app was rated number one on another site for organizing my panty and making meals. The UI is great and was excited to start using. I noticed a crash and emailed support. I just received a permanent failure on the email as the website is no longer there and the Facebook page I found tonight has not been updated in two years. Bummer, this app looked like what I needed….😟

Crashes every time I open the app
     

This app has needed an update for sometime but this update has made the app worse. The app crashes every single time I try to enter a category. In addition, Why scan items when even the most common grocery items are not in the database? Is there even a database?

Crashing almost all the time
     

Whenever I try to change categories or any other properties of the item the app crashes. Please fix. Apple take notice. Support email of this developer is non-functional.

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cons:
*difficult to  add/scan food in the app
*app crashing all the time
* common grocery items are not in the database
*UI/UX design not good
*no updated for a long time

 

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input to app:

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1.search:

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2:scan or take photo

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New York Hall of Science “Data in the Midst” project

New York Hall of Science “Data in the Midst” project.

Contact: Catherine Cramer

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I work as an volunteer with Catherine in the Maker Faire.

The project name is “Data in the Midst”. What I am doing is to ask people what the type are they. Are you a Makers? or Watchers? or none of that?

Orange ---- Makers

Yellow —-      Watchers

red      ——-N/A

After the people choice the right color ribbon I will show them how to tie it on the net under the current time slot. At the same time, I will measure them eye level with the net, so they will tie the ribbon on the cross at time line with eye level.

It was a simple way but very effective live visualization of data. People do not need wait and they can saw the result after they finished tie the ribbons.

It dose inspired me about how to show my phase 1 project. Using the simple and clear, also interactive way, I will refine my phase 1 project shortly.

Phase 2 Paper–MindRider

  1. What are the questions they are researching or investigating with data?
  2. What are their issues in collecting the data?
  3. What are their issues in cleaning the data?
  4. What are their issues in understanding the data?
  5. Are there historical precedents for this kind of work with data? For this, please explore and cite at least one of the books from the “Historical/Foundational” section of the course-reserved books (seesyllabus).
  6. What kinds of data visualizations can help them? What kinds of visualizations can’t help them? For this, it may help to explore the books in the “Technique/Science” section of the course reserves (seesyllabus).
  7. Please provide possible vis sketches and/or notes from your exchanges with your community partner. Keep in mind that some community partners may have limited time to work with you. Plan accordingly!
  8. If applicable, please integrate your thoughts on speakers and/or site visits so far.

Timeline:

October 29 – November 6rd: Review the data and find the right part.

November 6th – November 10th:  discuss data visualization ideas with the community partner for feedback and keeping write the paper

November 11th – November 25th: Sketches and initial design prototypes

November 25st – November 30th: Review prototypes and designs with the CP for feedback

December 1st – December 15th: Final iterations and feedback

 

community partner–MindRider

What is MindRider?

Originally developed at MIT, MindRider is a head-based wearable that tracks, in real time, how your rides, movement, and location engage your mind. The MindRider app maps your engagement, giving you new insight into your riding experience. Part of the Multimer analytics system, Mindrider is the first biosensor developed to collect human experience data and process it in a large-scale, location-aware context.

The MindRider app maps your experience and engagement in real-time. As you can see in the Prospect Park (Brooklyn) map below, every MindRider map has its pure green “Sweetspots” of relaxation, and its pure red “Hotspots” of focused concentration. Riders have examined their maps and have seen how Sweetspots and Hotspots can be influenced by the rhythm of the road, pedestrian and vehicular traffic, and social interactions of many kinds. This insight provides the opportunity to challenge, enjoy and maximize your experiences.

 

I have read the book of the MindRider. There are a lot of data going on.

NYC map with clear data visualization graphic, I would like to talk to my

community partner to ask them how to get this data and how to analyst this.