Information on the Course Project
Motivation
The lab exercises in our class teach you valuable concepts and spatial analysis techniques for conservation and land management, but they don’t quite prepare you for the challenges of gathering data and organizing a spatial analysis on your own. A central objective of the course project is to give you experience doing just that.
The course project is also an opportunity for you to demonstrate your ability to conduct a GIS analysis and effectively communicate its results. And last, but not least, the course project is also a chance to develop a product that you can add to your professional portfolio and share with others during job interviews, etc.
Project Scope & Grading
Scope
Below I outline a few topics from which you can chose - or guidelines if you want to go your own way. The work involved should be on the level of one of the projects we did earlier in the semester. Because you may have to find, obtain, and organize your own data, however, time for analysis is limited, so strive to keep it simple. You may also want to focus more time on making attractive deliverables: pretty maps or even better, a StoryMap or Dashboard.
Somewhere in your submission I should find the following:
- What was problem/issue addressed (why?)
- Short study area description
- What data were used? What software?
- What assumptions were required to execute the analysis?
- Why are these methods appropriate to your project?
- What general GIS analysis procedures did you use?
- Explain as logical flow rather than listing the ArcMap tools used…
- What analytical techniques did you use? (e.g. summary stats)
- A flow chart may be helpful for multiple step projects
- Output maps, graphs, tables as appropriate
- A brief “post mortem” explaining issues encountered along the way and suggested improvements
Again, I am more interested in your ability to conduct a spatial analysis independently than the precise values of your output. Those of you interested in visualization/communication can conduct a shorter GIS analysis and focus more of your time on the development of a story map or dashboard.
Grading rubric
| Project elements | Score |
|---|---|
| • Well defined and described project objective/question • Locates and uses best available data for the analysis • Appropriate and logical spatial analysis workflow • All assumptions clearly identified and communicated • Presentation of results conveys a clear message • Attention to detail, organization, and professionalism |
90-100 |
| • Project objective/question is more of a direction than an actionable hypothesis • Overlook some datasets discussed in class or easily found in your analysis • Spatial analysis gets the job done, but not as efficiently as possible • Presentation of results require excessive effort to understand/follow • More than a few typos or other easily fixed errors; looks rushed |
80-90 |
| • No clearly stated project objective • Poorly chosen or inappropriate data used • Application of spatial analysis reveals lack of understanding • Findings are unnecessarily hard to find and/or difficult to follow • Submission looks rushed and unchecked |
<80 |
Topics
Topic 1: Ecological Impacts of Hog Lagoons
Hog farms are an important part of North Carolina’s economy and aren’t likely going away. But they do have potential ecological and environmental justice impacts. I’ve compiled a short document here with several angles from which you can explore this topic. These are broken down into (1) aquatic impacts, (2) health impacts, (3) energy considerations. Take on one or all (e.g. if working on a team) analyses and present your results as a map, Story Map, or dashboard.
Topic 2: Deeper Exploration into Habitat Modeling
In the last few years, ESRI has added a number of additional tools to make modeling and prediction more accessible from directly within ArcGIS Pro. MaxEnt and Random Forest analysis can, theoretically, be done in a geoprocessing modeler. Be the first to explore how these tools might be applied to our pygmy salamander data or to a dataset of your own choosing. See how far these tools can take you and what they tell you about your species.
Or, develop a habitat model using the methods covered in class and apply them to a set of modified environmental rasters to predict how habitat likelihood might change under novel conditions (development, climate change, etc.).
Topic 3:
Baker et al developed a GIS-based process to more precisely model riparian buffer effectiveness and stream sediment/nutrient loading involving flow path/hydrologic connectivity and land cover analysis. Apply these methods to explore and predict water quality under actual and/or hypothetical land cover scenarios. s
Topic 4: Explore the Maps of Biodiversity Importance
One of our guests speakers, Regan Smyth, discussed the relatively Maps of Biodiversity Importance (MOBI) I’ve downloaded a number of these (located on the class drive: W:\761_data\ProjectData\NatureServe_MoBI - and via the Living Atlas).
Collect/create data on threats and protected areas with respect to the Biodiversity maps. Where are gaps in protection (national, state, municipal parks, or conservation easements)? Where/how might future development impact these areas?
Quote from client Regan: “I would love to see how students approach the threat mapping. One of the things we’ve given a bit of thought to is creating versions of the biodiversity maps for subsets of species vulnerable to particular threats, and doing a more customized analysis, so seeing whatever they come up with – nationally or locally – would be of high interest. Comparisons to other prioritizations would also be fantastic!”
Topic 4: Your own topic!
You are most welcome to craft your own course project topic. The key here, however, is to ensure that (1) it is an actionable topic and that it also is of a manageable scope.
By actionable, I mean the project has a well defined question that can be answered using GIS, that all vague terms have been (or can be) rephrased into specific ones. For example, change “identify all hog farms that could impact sensitive waterways” to “identify all hog farms within 250 m (along the flow path) of pixels classified as sensitive in the XXX dataset.” (It’s ok if you don’t know the exact distance that should be used; make an educated guess - backed up with literature if you can, but don’t let that stop your work!)
The question of manageable scope is a bit more nuanced. I recommend focusing on one, maybe two key analyses – perhaps more if you are working as a team – and remain focused on the principles of a good analysis. By that, I mean keep a tidy workspace, generate geoprocessing models that are logical and robust, and communicate results sensibly and with attention to your audience.
If you chose your own project, I strongly recommend running it by me. This can just be a quick chat, and I can possibly point you to some useful datasets and perhaps identify some potential bottlenecks you might face and how to work around them.