Exploring the Relationships between Land Use, Deer Population, and Lyme Cases in Four U.S. States
Audience
Course module will be deployed at Saint Anselm in PS/CS 211 “Remote Sensing” during Spring 2023.
The expected class size is around 20 students.
Students are expected to have knowledge of ArcGIS and its data, but not necessarily data analysis.
Course module will be deployed at Goucher College in BIO 102 “Explorations in Biology I: Life in Context” during Fall 2023.
The expected class size is around 70 students.
Students are expected to have knowledge of biological systems, but not necessarily data or analysis.
Other applicable courses that may benefit from this module:
This module could be useful in other ArcGIS classes or perhaps general geography classes. In addition, other Biology/Ecology classes, especially those that look at urbanization with ecology, might benefit from this module.
Project Summary
Primary Objective
The primary objective of this module is to provide students with an opportunity to create and test a hypothesis about their area of study in land-use or biology without requiring technical data skills.
Goals
Interpret and describe data detailing biological factors in nature to understand what each value and its units mean
Create and evaluate a hypothesis about an interaction between land-use data and population data using Principal Component Analysis to reduce factors and then a k-means test to classify groups of data
Interpret and draw conclusions from plots of data to make decisions about the trend of a biological interaction, and whether it is beneficial or detrimental
Content Outline
This module allows students to explore data on lyme disease cases, deer population, and land use and environmental factors for four different states, Connecticut, Maryland, New Hampshire, and Massachusetts using various data analysis techniques. Six Canvas quizzes with mainly short answers and a few multiple choice questions guide students through a Google Colab application as outlined below.
Google Colab
Table with sort capabilities
Interactive maps for all four states (Connecticut, Maryland, New Hampshire, Massachusetts)
Principal Component Analysis (PCA) Results
K-means Plots
Canvas Quizzes
PreLab Assignment
Familiarize students with datasets
Provide background information on PCA and k-means
Part 1: Launching the Colab
Part 2: Understanding the Data with the Table View
Part 3: Mapping the Data
Part 4: Analyzing the Data with PCA and K-Means
Part 5: Reflections and Conclusions
For more information email: difuse-pi-group@dartmouth.edu
