Project Motivation

ENV 859 - Geospatial Data Analytics   |   Fall 2024   |   Instructor: John Fay  

Description

You are applying for your first job out of the Nicholas School. The job application asks you to submit some work demonstrating your ability to work with geospatial data. The people reading your application will have a firm understanding of GIS (ArcGIS Pro, Python, ArcGIS Online, etc.) and are eager to see your command of these technologies as well as your ability to present geospatial analyses in a clear, transparent, and reproducible fashion. Your course project is construct some project that could be included in this job application.

The following rubric will help you focus your efforts to produce a clear, coherent, and impressive item for your geospatial portfolio.

Rubric

Presentation/Documentation

A sloppy or confusing workspace is an instant turnoff. It’s important that you take some time to consider your audience when packaging and delivering your hard work. Pay attention to the following:

  • Provide your work in an easy to navigate workspace

    • Ensure your file organization is clear and logical; use folders and subfolders as appropriate
    • Remove any files or folders that aren’t required for your analysis, including temporary files
    • Give items informative names as much as possible

  • Your workspace should have an intuitive starting point

    • Provide a ReadMe.txt or other document that includes a brief description of your project, your name, and the date
    • If it is not obvious what the user should do next (e.g. an ArcGIS Pro project file, a link to start Jupyter or Spyder, etc.), consider providing an overview document that leads the reviewer to the next step, and following steps, if necessary.

  • When documents are open, the material should appear in a thoughtful and presentable fashion

    • Layers in ArcGIS Pro Maps should have reasonable names and symbology, not just the defaults
    • ArcGIS Pro geoprocessing models should tidy and made as readable as possible
    • ArcGIS Pro geoprocessing tools should have well-named and documented inputs
    • Jupyter notebooks and Python scripts should make use of formatting abilities (e.g. markdown cells and code blocks) and should be written for readability and easy comprehension. Ample comments should be included.

  • Datasets used should be adequately attributed and documented

    • Where did you get the data? What does the dataset include? Is it time sensitive?
    • Do the data used represent exactly what you need for your analysis, or do they serve as a proxy? What assumptions or biases are there that might affect your analysis.

Execution

Your reviewers will want to run your analysis to ensure it executes smoothly and produces the intended results. You can expect that they’ll have access to the same software we use in class (an up to date version of ArcGIS Pro, including Conda environments that we’ve used in class – the default ArcGIS Pro environment as well as one that runs VSCode or Spyder and Geopandas). Beyond that, your analytical workspace should open without error, nor should any easily avoidable glitches occur when repeating your analysis.

  • If you use ArcGIS Pro in your project, ensure that the project files opens without error and can locate all the required files

  • If your projects executes an ArcGIS Pro geoprocessing model, ensure the model runs without error

  • If you use Jupyter/VSCode/Spyder, include a shortcut that opens Jupyter/VSCode/Spyder linked to the appropriate Conda environment

    • If you use Python packages not covered in class, include instructions how to create the Conda environment required
    • Notebooks should be “reset”, with all code cells cleared. Consider providing HTML versions of fully run notebooks to demonstrate what successful execution should look like.

  • Provide all the data required to execute your analyses. If files are excessively large, consider packaging them on a shared drive (e.g. Box) and provide clear instructions how to download these files.

  • If your project is an interactive visualization (e.g. dashboard):

    • Does it engage the user? It is clear what data are being shown? Is interacting with the data intuitive?
    • Are the visualizations meaningful? Do they describe something about the data that’s not apparent otherwise?
    • Might the visualization aid in making a decision? (or at least show that the information can NOT support a decision either way)