The project only applies to WATS 4931/6921 students. Your project is worth 100% of your grade
and is designed to help you synthesize all of the skills developed in the WATS 4930/6920 course into a research question or practical application of interest to you.
This page and the project discussion forum
are here to help explain the different components of the project and help you see them through to successful completion.
The primary objective of the project is to give you an opportunity to showcase your new GIS skills by applying them to interesting practical problem(s) and/or research question(s). A secondary objective is to give you experience conceiving of a problem, developing a method to address that problem that requires spatial analyses, and presenting your findings in an effective and convincing manner.
For undergraduates, this project is an opportunity to tackle just about any topic with some spatial flavor you can conjure up and is (most importantly) interesting to you. The projects are a lot of work, so you will want to choose something you are unlikely to grow bored of. For undergraduates in the Watershed & Earth Systems
Program, this project constitutes a capstone experience in which you are expected to synthesize information and techniques you learned in other courses into a professional quality project that requires you to undertake spatial analyses. You are welcome to double dip (i.e. choose a project that you can use to help you for another class project or a senior thesis).
For graduate students, you may use this project to a) help you tackle some aspect of your graduate research in which you need spatial analyses to address the research question, or b) use it as an excuse to learn about another research topic of potential interest to you, but that you would not otherwise have time to explore.
This project helps fulfill Primary Learning Outcomes
2, 3, 4 and 5 (particularly 4)
All students will conceive their own project and undertake the analyses necessary to address the question they define. Every Project Must:
- Clearly identify a problem or topic that you will address with some type of spatial analyses. You have complete freedom in your choice of the problem, which may be an applied problem or a fundamental research question. Past examples of projects covered a multitude of topics (e.g. tracking bee populations in North America, to reconstructing geomorphic history of alluvial fans; predicting avalanche susceptibility for back-country skiers; creating an interactive map and information portal on the distribution of whirling disease for anglers). You do need to identify a problem that is tractable for you to address within the confines of this semester and the resources available to you (i.e. you likely won't be leading a field expedition to the Himalayas to collect field data for your project).
- The objectives of the study must be clearly defined and you need to articulate how your methods will help you achieve the objectives
- Every student must prepare their own original research, analyses and written manuscript and submit something for each of the seven components of the project to receive credit
- Report some form of error or uncertainty analysis associated with some of your analyses
Components of Project:
- Submit a Project Proposal - Due by April 3nd, 2017 before Midnight (on Canvas)
- Data Prep Vignette - Due by April 7th, 2017 before Midnight (on Canvas)
- Data Analysis Vignette 1 - Due by April 14th, 2017 before Midnight (on Canvas)
- Data Analysis Vignette 2 - Due by April 21st, 2017 before Midnight (on Canvas)
- Project Poster Presentation - Presented Tuesday April 25, 2017 (submitted on Canvas prior to presentation)
- Project Peer Review - Due April 26, 2017 (to Shannon)2
- Final Project Report or Manuscript - Due no later than May 1, 2017 by noon (on Canvas)
Due dates for the project components and the entire organization of the course syllabus is timed to pace you with deadlines and milestones that will help you see your project through to completion.
The grading criteria for the individual assignments are specified in Canvas. See here for weights
Making Nice Figures
Fuzzy Inference Systems