Project Website

[dt-woods]

The purpose of this project is for you to showcase spatial data on a public website (https://spatial-data-discovery.github.io). The task is to find (or create) spatial data that interests you; explain why it is interesting; describe what it is, where it came from, and any processes you used to create it; and produce a visualization of the data that expresses your interest to others.

• must include a graphic
• must include code used to create the dataset
• must include a summary for a general audience that explains the graphic, why it is important, how it was created, and what you learned along the way

The project is worth 25 points and is broken down into categories as described below.

• font-end (60%)
  • summary, visuals, methods, and attribution
• back-end (40%)
  • data files and scripts

Deliverables

• Project script (15%)
  • Upload your python script that reads/writes/processes your data to the ay1920-1 repository under the project folder (name it lastName.py).
  • Tag the Project issue with your commit.
  • Check that your python script includes the following:
    • author and date updated (in comments at the top)
    • clear purpose statement (in comments at the top)
    • runs on a given folder or local directory (no hardcoded paths)
    • runs without errors
• Project website (45%)
  • Upload your markdown file to the spatial-data-discovery.github.io repository root directory (name it project_lastName.Rmd)
  • Tag the Project Website issue with your commit
  • Check that your markdown file includes the following:
    • link to your visualization
    • summary statement: what the data you are showing represents and why you think it is important
    • data methods: summarize how you created your dataset
    • attribution where appropriate
  • Make certain the page looks the way you want it to after it is rendered to HTML
• Data file(s) (15%)
  • Email your data files (or links to your data files) to your professor
  • Compress your files before emailing
  • Make certain your data files include the following:
    • data origin or provider
    • variable definitions
    • units
    • appropriate nodata/missing value/fillvalue
    • contact info
    • institution name
    • date created
  • Variables and attributes should be included with data files (.nc and .hdf) or in a separate README file for .asc or .txt files.
  • Additional data sets, such as state/country boundaries should be cited and include a URL link to where you downloaded them.
• Visualization (20%)
  • Email your visualization file(s) to your professor.
  • Export media to a common file format: .png, .jpg, .gif, .mp4
  • Link to it in your markdown file (assume that the media file(s) are in the same directory as your markdown file).
• Creativity (5%)
  • May be applied to interesting visualizations (such as video), unique processing techniques (in your python script), or stylized web layout.

[dt-woods]

Well folks, I think that about settles it. Videos should now scale with screen size. Let me know if you have any issues with your page. I appreciate all your hard work and meaningful feedback this semester. Good luck next semester. Have a safe and restful break.