ENVT 4408 : Automating Modelling Methods in Python Lecture

Spatial Habitat Models with Python

• Make a Macroalgae model for Rottnest
• Generate rugosity covariates form Rottnest multibeam depth data using Kernels (focalstats function)
• Join to towed video data with gps records of Macroalgae
•  Exploratory regression to find the best covariates for Macroalgae model (full subsets analysis)
• Ordinary least squares (OLS) regression to generate regression coefficients
• Apply the OLS regression coefficients to the Rottnest and predict Macroalgae occurrence using the map calculator function. Threshold at > 70% probability of occurrence
• Apply the Rottnest model to another site - Two Rocks
• Work on your project - Applying the Macroalgae model to your own site (there are 5 and which one you choose is based on the last 2 digits of your student ID). Answer questions.

Marking Rubric

New and clean Jupyter notebook with functioning code, updated with just relevant code to run the model at with no errors for Rottnest, Two Rocks and your new site.

Your own conceptual flow chart, study site map, and final area graph added as markdown cells. Data and notebook handed in

Aim, objective, and method described to show steps, included as markdown cell.

Function correct and detailed to get area estimate of the size in area (metres) of predicted Macroalgae for all 3 sites and the proportion of the sites with Macroalgae present. Answers correct

Lab assessment model questions answered coherently, correctly and demonstrate understanding of programming for GIS

Assessment Requirements Spatial Habitat Models with Python

The purpose of this assessment is to develop a Macroalgae habitat model using Python for spatial prediction and GIS analysis. The key objectives and requirements are:

Key Pointers:

1. Model Development: Create a Macroalgae model for Rottnest using towed video data and rugosity covariates generated from multibeam depth data.

2. Data Processing: Generate rugosity covariates using the focalstats function and join them with GPS records from Macroalgae observations.

3. Exploratory Analysis: Conduct regression analysis to select the best covariates for the Macroalgae model (full subsets analysis).

4. OLS Regression: Generate Ordinary Least Squares (OLS) regression coefficients.

5. Prediction Mapping: Apply OLS coefficients to predict Macroalgae occurrence for Rottnest using map calculator; threshold >70% probability.

6. Model Transferability: Apply the developed Rottnest model to a second site – Two Rocks.

7. Student-Specific Site Analysis: Apply the Macroalgae model to a personal site based on student ID digits.

8. Documentation: Include a conceptual flow chart, study site maps, final area graphs, and a description of aim, objectives, and methods in markdown cells.

9. Calculation Accuracy: Correctly estimate the area (in meters) of predicted Macroalgae and the proportion of sites with Macroalgae presence.

10. Code Quality: Submit a clean, functioning Jupyter Notebook with relevant code, no errors, and clear outputs.

Assessment Approach

The Academic Mentor guided the student through the assessment in a structured manner, ensuring understanding of GIS programming, Python functions, and spatial modeling. The step-by-step process included:

1. Understanding the Aim and Objectives

• Mentor explained the importance of modeling Macroalgae habitats for ecological monitoring.

• Discussed the objectives: spatial prediction, habitat mapping, and area estimation.

• Students were asked to document aim and objectives as markdown cells in the notebook.

2. Data Preparation

• Provided guidance on joining GPS-recorded Macroalgae data with rugosity covariates derived from Rottnest multibeam depth data.

• Introduced the focalstats function in Python for generating rugosity covariates.

• Ensured students handled missing data and aligned spatial datasets accurately.

3. Exploratory Regression Analysis

• Mentor explained full subsets regression analysis to select the best covariates.

• Students explored relationships between environmental variables and Macroalgae presence.

• Key teaching included interpreting regression outputs and selecting statistically significant predictors.

4. OLS Regression and Coefficient Extraction

• Demonstrated running Ordinary Least Squares (OLS) regression in Python.

• Guided students to extract coefficients and understand their ecological relevance.

• Students documented regression results and coefficients in markdown cells for clarity.

5. Prediction and Mapping

• Students applied OLS coefficients using Python’s map calculator to generate probability maps for Rottnest.

• Mentor emphasized thresholding (>70% probability) to identify areas with predicted Macroalgae presence.

• Generated maps were visualized and interpreted, with graphs summarizing area estimates.

6. Model Transfer to Two Rocks

• Explained the process of applying Rottnest model coefficients to a second site (Two Rocks).

• Ensured students understood assumptions of model transferability.

• Produced a map and area estimate for Two Rocks, demonstrating model generalization.

7. Personal Site Application

• Students applied the model to a site assigned based on student ID digits.

• Mentor guided students to generate maps, calculate area coverage, and proportion of predicted Macroalgae.

8. Documentation and Submission

• Students compiled conceptual flow chart, study site maps, and final area graphs in markdown cells.

• Clear description of methods, stepwise analysis, and outcomes was added.

• Emphasis on clean, error-free Jupyter Notebook containing only relevant code for reproducibility.

Final Outcome

• Clean Jupyter Notebook: Functioning code for Rottnest, Two Rocks, and personal site, producing accurate predictions.

• Visual Outputs: Probability maps, area graphs, and conceptual flow charts included.

• Accurate Calculations: Area of predicted Macroalgae and site proportions computed correctly for all three sites.

• Demonstrated Understanding: Students showed comprehension of spatial modeling, regression analysis, and GIS programming.

• Learning Objectives Covered:

  1. Technical skills in Python for GIS-based habitat modeling.

  2. Spatial data handling and covariate generation.

  3. Application of statistical models (OLS regression) for ecological predictions.

  4. Critical thinking and interpretation of model outputs.

  5. Effective documentation and presentation of computational workflows.

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