Refined Project Proposal

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

Due: Oct 23rd @ 5pm

Refer to the document on the course project as a reminder about the course project and what I’m looking for.

The refined project proposal is an opportunity to clarify your needs and objectives of your course project. It’s also another chance to get feedback from me on the scope of your analysis, on any useful datasets you may have overlooked, and on potential improvements in your workflow or end products.

There is no set format for what to submit here, though I offer a template if you want to follow that. What I am interested in seeing is that you’ve given serious thought to your project:

Have you narrowed in on a specific objective? One that spatial analysis can answer?
Do you have or are you certain to get the data needed to drive your analysis?
Can you sketch out, at least generally, an analytical workflow to go from data and inputs to a meaningful output?
Is the project of a reasonable scope?

Tips if you are still fumbling for a topic or just need more focus:

►Geospatial tools

In the past, many students have proposed a geospatial tool for their project, and I think it suits the class well. We’ve seen how the ArcGIS model builder can be adapted to handle inputs to make an analysis more dynamic and interesting. We’re moving towards how Python can do this as well. A tidy project idea could be one that takes a spatial analysis (perhaps relevant to other work you are doing) and “tool-ifying” it.

The format of your tool could be an ArcGIS Pro workspace & toolbox or you can provide a Jupyter notebook that reveals your code. An R-Markdown document or Shiny App is another option, but be warned that we are not likely to go into much depth into how to construct those, and what we will cover will be very late in the semester.

►Data clean up/preparation/visualization

The examples we’ve covered in class show how Python is great at transforming raw data in formats we can use in GIS. If you have one or a series of raw datasets that you want to incorporate into an analysis or just visualize, that could be the central theme of your project.

Soon, we will learn how to access on-line data using Python: most data that you can see on a web page is “scrapable”, meaning that you can pull it into a spatial (or other) analysis - or just map it. *We will cover how to compose maps from features in a Jupyter notebook.*

Some examples of topics to come in this class

► Insight workbook/Dashboard

►Story maps

We don’t cover story maps in this class because I feel it’s a technology you could probably learn just fine on your own (i.e., the learning curve is as steep as other topics we cover). However, if you wish to create a story map using your ArcGIS Online account, that’s fine too.