Class 06 – Feb 29 2016:
Project Proposals
For the next two weeks in and out of class we will be working towards our midterm prototypes. We have five weeks before our final projects are due. We will review our proposals in class, to determine a pragmatic approach to get us to great final projects.
Scheduled Presentations:
- Feb 29: Lorzenzo, Sever, and Cara
- Mar 14: June and Andrew
- Mar 28: Kimi and Jiani
- Apr 4: Jessie and Patric
- Apr 11: Ryan and Navi
Study groups.
Everyone should be in a study group. Lets try to organize ~3-5 groups of people working on related topics. Put the group name and member names on the baord. We should make Slack channels for these groups.
Does an online IDV2 makeup class work for people Tomorrow?
Presentations from Lorzenzo, Sever, and Cara
The Flikr SEARCH API and data explorer:
Look at project proposals.
Identify intention.
What phenomenon are you exploring? What features are you expecting to uncover? Who has done related work? If this work is narrative then What is the story you are trying to tell? If the work is editorial: What perspectives are you taking? what are the some alternate perspectives? If the objective is analytical: What is being measured? What is being ignored?
Start with your ideal visual representation and work backwards towards the data. What would the data need to look like to implement your vision? Think about feature mapping. What features do you care the most about? How are they represented?
Can you answer:
- Who cares? (iishi)
- Static vs. Interactive
- Exploratory vs. Narrative
- Anecdotal vs. Scientific
- Historic vs. Live
- Related works and precedent?
- Where and how will you publish?
Work in class and Problem Set 06: Challenges.
You will be expected to present some challenges in class next week. Your response can be PDF, Text, or Web page. After answering the questions, you prepare another revision of your design, addressing any concerns raised in class.
- Identify next steps in your project. Enumerate and prioritize what you need to complete your project. The biggest challenges and unknowns should be put at the top of the list.
- For each challenge spend 5-10 minutes thinking of a plan-b. Would you be able to rework your project? What would it look like with these change?
- For each challenge, can you identify someone in class, or a close friend outside of class who might be able to help?
- For each challenge, spend up to one hour doing initial research. Does the problem look solvable? What are the biggest 'pieces' of the problem? Write down the online resources available to solve this problem.
Questions to consider:
- What kind of data are you exploring?
- Where do you get it?
- What dimensions does it have?
- Is some data missing?
- Framing:
- Over what time period?
- Categorized by what?
- Sorted by what?
- Compared to what?
- Where / who?
- How much data do you have or care to work with?
- Can we 'dig in' to find out more?
- Can this visualization be personalized or customized?
Data capture process:
- Collect Data (CSV, XML, JSON) from web services. Archive?
- Combine data (merge multiple records into one): Identify attributes you care about.
- Add sentiment
- Add image tags
- Dig into responses, likes, retweets … &etc.
- Filter. Pluck. Summarize.
- Reshape. Make the data conform to your visualization needs.
- Display in a web browser.
Previous Reading / Watching: