Your final project is composed of four parts. You will work in groups of three or four people; each person will be expected to participate in the project.
Note that in some cases, the descriptions of what you are to visualize are somewhat vague. This is in keeping with our discussion of carefully choosing the story you want to tell. You're going to be called upon in this assignment to examine the data, and then within the rough outlines of the discussion, construct a visualization that "answers" the posed question.
There are a few important notes:
- All of the source code must be provided.
- Use GitHub to version your code and your writeup. This will also be used to verify individual contributions.
- The writeup will be evaluated.
- A "visualization" does not mean a single plot; it is broadly defined to include linked visualizations, collections of plots, etc.
Concept: Tell us about the routes in the Champaign-Urbana Metro Transit District.
Your first dataset will be based on the CUMTD data. There are several feeds available. A copy of an example feed will be provided in this repository; you should also feel free to scrape at regular intervals. Data sources can be found at http://developer.cumtd.com/ . In particular, check the GTFS data feed, which is provided in zipfile form (and updated with regularity.)
Your visualization should be constructed in a procedural way, and should be suitable for static viewing. (i.e., it should be non-interactive.)
A few of the possible concepts to explore are listed below; please note that these should be considered starter points, and you should explore beyond them.
- How quickly do buses go? How does this vary based on time of day, region, route, etc?
- How many stops are there? What is their density?
- How many buses are there on each route? Does this correlate with stop density?
Generate from this a single PDF or PNG.
Concept: What is the distribution of IMLS grants, and what do these grants do?
Your second dataset will be the IMLS discretionary grants database. These grants include information about the topic of the grant, the institution that received it, its duration, and a few other pieces of data. You may also find useful and interesting data (especially related to zip codes and economic factors) at FRED.
Your visualization should be interactive. There are several frameworks you can use to do this. The three that we have discussed in detail in class have been using the IPython widgets interface to build small, exploratory visualizations, the Bokeh framework and Plotly. You may not use Tableau to build this visualization. If there is another framework you would like to use, please ask.
A few of the concepts that you may explore:
- What types of grants are supported by IMLS?
- How are these grants distributed across the United States? Are they particularly clustered in certain states?
- Is there a relationship between the economic factors in a zip code and the grants from IMLS?
Generate from this data either a notebook that can be executed, a python script that can be executed, or a plotly dashboard that can be accessed. If you utilize plotly, at lease some of the plotly setup must be in a python script.
Concept: Given limited information about a dataset, what types of all-purpose visualizations can you construct to provide an overview of the data?
You will build a function which will either produce a visualization (either static or interactive) that provides an overview of the data. This may mean applying heuristics to the data to guess at different possible interesting characteristics. This function should accept a pandas dataframe.
The data will have at least eight columns:
- Name
- Date
- Latitude, longitude
- At least 1 Categorical label
- At least 3 Quantitive columns
There will be three sample datasets provided.
This is forthcoming; it will be built on the CUMTD dataset.
You will be tasked with providing a writeup of your visualizations as well. Each should be in markdown form and in the directory with your visualizations. These should describe in some detail:
- Why you took the approach you did
- Strengths of your approach
- Weaknesses of your approach
- What you wished you had been able to do (if anything)
- Who in the group contributed each part of the visualization (from code, data management, data cleaning, writeup, and so on.)