Final Project

This page contains information about the final project.

Timeline

One important thing is (of course) when the various assignments are due. Below is an overview

• Project Assignment A

  • Due: April 16th, 2018, 23:55.

  • Peergrading due: April 23rd, 2018, 23:55.

• Project Assignment B

  • Due: May 14th, 2018, 23:55

  • Peergrading due: May 21st, 2018, 23:55

Project Assignments Overview

The point of the Project Assignments is to try out the skills you’ve learned in the course on your own dataset. In particular, we’ll be building a narrative visualization, as defined in the Segel and Heer paper [Narrative Visualization: Telling Stories with Data](http://vis.stanford.edu/files/2010-Narrative-InfoVis.pdf). More on this below.

We will be working with Open City Data. Here are some examples.

• New York City https://nycopendata.socrata.com

• San Francisco https://data.sfgov.org

• Copenhagen http://data.kk.dk. Better https://portal.opendata.dk

• Melbourne https://data.melbourne.vic.gov.au

• Helsinki http://www.hri.fi/en/

• And there are many more awesome data sets out there.

So what can you do with Open Data? For inspiration, I recommend you sit down and listen to a podcast to get in the right mindset. It’s an interview with Ben Wellington, the author of the site http://iquantny.tumblr.com/ (it’s a super cool blog, so remember to scroll down to check out some of the many projects Ben has worked on).

Check out [this podcast](http://datastori.es/66-iquantnyc/) for inspiration. It’s a 40 minute listen, but well worth your time. After listening to this I predict that you’ll be brimming with ideas for what to start working on. (Or at least have an idea of where to get started).

Summary: The overall idea is to take a deep dive into some aspect of a city dataset and try to understand that data using the tools you’ve learned in this class. Once you understand the data, you should tell the story of what you’ve found on your own website using D3 visualizations (and whichever other tools you need).

Project Assignment A

The first part of the final project is a 3 minute movie, which should explain the central idea/concept that you will investigate in your final project. You’re making the movie so that the TAs and I can give you feedback, and so that other groups can steal your ideas (and you can steal ideas from them). The movie must contain the following

• An explanation of the central idea behind your final project (which city, what is the idea? which datasets do you need to explore the idea?, why is it interesting?)

• A mock up of the visualization that you wish to build. (Anything is fine here. Pen and paper, MS Paint, Inkscape, D3, anything.).

• Make sure you answer the questions

  • What genre is it? (for Genres, see section 4.3 of the Segel and Heer paper)

  • Why is that genre right for telling the story you want to communicate with the data

• An outline on the elements you’ll need to get to your goal.

• The implementation plan.

• A walk-through of your exploratory data-analysis, addressing

  • What is the total size of your data? (MB, number of rows, number of variables, etc)

  • What are other properties? (What is the date range? Is is it geo-data?, then a quick plot of locations, etc.)

But other than that, there are no constraints. And we do appreciate funny/inventive/beautiful movies, although the academic content is most important. Note that we’ll display the movie to the entire class.

(The maximum length is 3 minutes, but its OK if the movie is shorter.)

Handing in the assignment: Simply upload your video to [YouTube](http://youtube.com/) (the higher the resolution the better) and submit the link to [peergrade](http://peergrade.io/).

Project Assignment B

The deliverables for the Final project will be

• A data story. The story should be a single page, containing interactive visualizations (or a single large interactive visualization) to let the reader explore the data that you’re interested in getting across, using the theory we have learned during the lectures (overview first, details on demand, etc). Your analysis behind the scenes can be technical and as advanced as you like, but the story itself should not be technical, but rather aim at using visualization and explanation to get your data driven insights across to a non-scientific reader.

• An explainer page. From the main viz-page there should be a link to a simple page, which contains all the behind the scenes stuff, details on the dataset, why you’ve selected this story genre, explanations of your data analysis methodology, etc.

More about the data story

Your datastory The main point of the website is to present your idea/analyses to the world in a way that showcases your use of what you’ve learned in class. The website should tell the story of your dataset and your findings from that dataset, using the tools (both in terms of D3 and visualization theory) you’ve learned throughout the course. The format should draw on the tools for telling data stories from section 4 or the Segel and Heer paper.

Here are some requirements

• Your story should be told on a single page (for ways to tell data stories see the Segel and Heer paper, sec 4.3 and all of the examples in that article).

• The story should be interactive

• The page must contain (at least one) D3 visualization (and not the barchart/scatter plot from the book - consider something interesting from Chapter 11-12 or [this official page of cool examples](https://github.com/mbostock/d3/wiki/Gallery)).

• In at least one of your visualizations, the data should be linked so that interacting with one part, dynamically changes another (e.g. brush some datapoints to see their aggregate statistics)

• There should be download options for data sets (so the user can play around).

• You must link to the Explainer Page (details below) that explains the details of your analysis.

For hosting, I recommend you use whatever you’ve already set up during the exercises.

More on the explainer page

Think of the page as a [directors commentary](https://en.wikipedia.org/wiki/Audio_commentary) to the data story.

The page should be in black and white, with sections separated by h2 tags.

Please structure it into the following sections

1. Motivation.

   • What is your dataset?

   • Why did you choose this/these particular dataset(s)?

   • What was your goal for the end user’s experience?

2. Basic stats. Let’s understand the dataset better

   • Write about your choices in data cleaning and preprocessing

   • Write a short section that discusses the dataset stats, containing key points/plots from your exploratory data analysis.

3. Genre. Which genre of data story did you use?

   • Which tools did you use from each of the 3 categories of Visual Narrative (Figure 7 in Segal and Heer). Why?

   • Which tools did you use from each of the 3 categories of Narrative Structure (Figure 7 in Segal and Heer). Why?

4. Visualizations.

   • Explain the visualizations you’ve chosen.

   • Why are they right for the story you want to tell?

5. Discussion. Think critically about your creation

   • What went well?,

   • What is still missing? What could be improved?, Why?

6. Contributions. Who did what?

   • You should write (just briefly) which group member was the main responsible for which elements of the assignment. (I want you guys to understand every part of the assignment, but usually there is someone who took lead role on certain portions of the work. That’s what you should explain).

   • It is not OK simply to write "All group members contributed equally".

Some additional notes: * Make sure that you use references when they’re needed and follow academic standards.

Handing in Project Assignment B

• Simply add the link to your page to http://peergrade.io

• VERY IMPORTANT!! The data-story page should contain a link to a GitHub Repo with your code for the data story page and explainer page. The reason is that GitHub is automatically time-stamped, so that way we can check that you haven’t altered the page after the hand-in time.

• If you do not include a github link, it will negatively impact your grade!