4 Final Project Draft Submission
4.1 Submission Instructions
You will submit a reproducible report to the D2L Dropbox. Your report should include code, outputs, and explanations for each section outlined below. The goal of this draft is to ensure that your project is on the right track and to receive feedback before finalizing your storyboard of three figures. Please try to turn this in by April 4th so that you have time to incorporate feedback, as needed. I will provide feedback sooner, as I can, on a rolling basis so the earlier you submit something the better.
4.2 Graded Components
| Component | Description | Points |
|---|---|---|
| Data Selection (10 pts) | Clearly states which dataset(s) will be used and the research question being asked (or what you are exploring if it is not hypothesis-driven). Includes data source details such as DOI (if relevant) and the variables of interest. | 10 |
| Data Merging & Pre-Processing (15 pts) | Shows how datasets were merged/harmonized, if applicable. Explains and demonstrates any cleaning steps taken (handling missing values, removing duplicates, CRS transformations, etc.). Describes sample sizes, spatial/temporal extent, and any grain size, and how you are approaching these landscape ecology considerations | 15 |
| Data Exploration (15 pts) | Provides at least one visualization that helps understand the dataset (e.g., map, histogram, scatterplot, spatial distributions). This is purely exploratory and does NOT need to be aesthetically pleasing, but you should appropriately transform the data for visualization if for example your histogram doesn’t show the data distribution properly | 15 |
| Methods Selection (20 pts) | Clearly describes the planned analysis techniques. Explains why these methods are suitable for the dataset and research question. IMPORTANTLY: at least one method applied has to be something we have either not covered in class or a significant extension of one of our previous methods. For example, suitable extensions would include (and you may do these): (1) doing a MaxEnt with a bias file, (2) extending the Mann-Kendall trend test to a regional Mann-Kendall analysis, (3) explore how bandwidth affects a KDE, (4) applying a point process model to a different spatial type such as linear features or a non-box-shaped shapefile. Note that this one extension doesn’t have to be related to anything we discussed in class, and you may use class methods for the remainder without extensions | 20 |
| Validation of Methods (15 pts) | Provides an initial attempt at applying the chosen methods to the data to check feasibility. I.e., are your data going to actually run with the method you are choosing or are you missing something critical to the methods? | 15 |
| Next Steps & Missing Components (15 pts) | Lists components still to be completed. Identifies gaps or uncertainties where feedback from the instructor would be helpful. This is not just a thought question and I will specifically try to give as detailed feedback as I can in direct response here. If you prefer/are comfortable working independently, you may also specify that you don’t need anything, but I’ll still look things over for gaps/suggestions. Outlines any additional datasets, analyses, or refinements needed before the final project. | 15 |
| Clarity & Organization (10 pts) | Report is structured logically and easy to follow. Code is clearly commented and organized for readability. Includes appropriate explanations to help interpret the outputs. | 10 |