Fall 2020 - REM 225 D100

Quantitative Toolkit for Social-Ecological Systems (3)

Class Number: 8303

Delivery Method: Remote

Overview

  • Course Times + Location:

    Tu 8:30 AM – 10:20 AM
    REMOTE LEARNING, Burnaby

  • Prerequisites:

    18 units.

Description

CALENDAR DESCRIPTION:

Develops a basic understanding of the breadth and role of quantitative models in social-ecological systems. Introduces skills, methods, and software typically used in data analysis, quantitative modelling, and research for environmental professionals. Quantitative.

COURSE DETAILS:

Effective decision-makers use quantitative data and models to summarize, clarify, and solve complex challenges in a wide range of fields. Resource and environmental managers, especially, need a strong foundation in these methods and how they can be used to inform decision-making given the complexity and uncertainty in social-ecological systems. A toolkit of methods, techniques, and software skills helps turn abstract quantitative ideas and data into practical tools for decision-making.

Quantitative Toolkit uses lectures, short chapter projects, class discussion, and tutorials to develop basic quantitative thinking and skills typically encountered in environmental problem-solving. These skills, combined with hands-on training in MS Excel and R statistical software, form a quantitative toolkit for applied research and decision-making within the environmental profession.

REM 225 will have synchronous lecture and tutorial sessions.

Outline of Topics (chapter project marks in parentheses)

Part I: Quantitative Concepts
1. Scales of Measurement (5)
2. Ratios, Percents, and Proportions (7.5)
3. Logarithms

Part II: Elementary Modelling
1. Linear models and regression (7.5)
2. Exponential models of growth and decay (7.5)
3. Power models for biological, social, and physical processes (7.5)
4. Difference equation models for dynamic processes (7.5)
5. Systems of difference equations for structured and complex systems (7.5)

Part III: Data Analysis and Applied Statistics
1. Descriptive statistics: centre, spread, intervals, correlation (7.5)
2. Probability distributions and randomness
3. Data analysis for beginners: Project in R statistical software
4. Sampling: a guide to studying populations (7.5)
5. Hypothesis testing and statistical inference

COURSE-LEVEL EDUCATIONAL GOALS:

This course assumes no prior experience with quantitative modelling, spreadsheets, or statistical software. Upon completing the course, students will be able to:

  • Apply basic quantitative concepts to convert physical quantities among measurement systems and estimate real-world quantities
  • Make appropriate comparisons based on absolute and relative scales of measurement using ratios, percents, and proportions
  • Identify linear, exponential, and power models and their roles in physical, biological, and social systems
  • Estimate and interpret properties of real-world phenomena (e.g., climate, fisheries, natural hazards, infectious diseases) using linear, exponential, and power functions in MS Excel
  • Use simple difference equation models to describe dynamic patterns of change over time (e.g., of populations, global carbon cycling)
  • Develop basic environmental management models, analyses, and graphics in MS Excel
  • Use R statistical software to summarize, interpret, and graph large data sets.

Grading

  • Chapter Project Assignments: each chapter project is a combination of selected quantitative problem-solving exercises and a short question/answer project. Detailed instructions, data, and software templates are provided for most chapter project assignments. 65%
  • Class Participation: participation is defined as productive contributions to class and tutorial discussions. Contributions range from questions, clarification, sharing ideas, offering helpful tips to others, etc. 10%
  • Term project: a major project that applies course concepts and skills to analyzing a real-world problem. Detailed instructions, data, and software templates are provided for the term project assignment. 25%

NOTES:

Evaluation is based on chapter project assignments (approx. weekly), class participation, and one major term project. The chapter assignments combine selected short exercises aimed at establishing competence with a particular topic with slightly larger projects that apply those skills to a real-world problem.

Materials

MATERIALS + SUPPLIES:

1. All students will require a laptop in class and tutorials. The SFU library has free, 4-hr loans of laptops:
a. https://www.lib.sfu.ca/borrow/borrowmaterials/laptops-equipment/borrow-laptop.
b. The campus-wide demand for library laptops is high and the library often runs out quickly. If you need to use the library laptops, you should show-up early at the library check-out desk.
2. MS Excel installed on laptop prior to first class
3. R statistical programming software (r-project.org) installed prior to Part III of the course. Note: detailed instructions and one tutorial session dedicated to downloading, installing and getting started with R.

REQUIRED READING:

Little, J.B. 2019. Modeling and data analysis: an introduction with environmental applications. American Mathematical Society. Providence, RI.
Note: this textbook is specifically aimed at quantitative methods for arts and humanities students.

PDF Books provided:
Zuur, A.F., Ieno, E.N., and Meesters, E.H.W.G. 2009. A Beginner’s Guide to R. Springer, New York.

REM 225 Weekly Vodcast: Weekly video-podcasts (vodcasts) supplement in-class and tutorial sessions. Each week, students can submit an unlimited number of questions relevant to the current class topic, as well as questions on chapter projects, major projects, or other theoretical, practical, and/or technical issues in quantitative methods and software. The downloadable video format is used so that visual demonstrations of selected topics can be provided where necessary and students can follow along at their own pace.

Registrar Notes:

ACADEMIC INTEGRITY: YOUR WORK, YOUR SUCCESS

SFU’s Academic Integrity web site http://www.sfu.ca/students/academicintegrity.html is filled with information on what is meant by academic dishonesty, where you can find resources to help with your studies and the consequences of cheating.  Check out the site for more information and videos that help explain the issues in plain English.

Each student is responsible for his or her conduct as it affects the University community.  Academic dishonesty, in whatever form, is ultimately destructive of the values of the University. Furthermore, it is unfair and discouraging to the majority of students who pursue their studies honestly. Scholarly integrity is required of all members of the University. http://www.sfu.ca/policies/gazette/student/s10-01.html

TEACHING AT SFU IN FALL 2020

Teaching at SFU in fall 2020 will be conducted primarily through remote methods. There will be in-person course components in a few exceptional cases where this is fundamental to the educational goals of the course. Such course components will be clearly identified at registration, as will course components that will be “live” (synchronous) vs. at your own pace (asynchronous). Enrollment acknowledges that remote study may entail different modes of learning, interaction with your instructor, and ways of getting feedback on your work than may be the case for in-person classes. To ensure you can access all course materials, we recommend you have access to a computer with a microphone and camera, and the internet. In some cases your instructor may use Zoom or other means requiring a camera and microphone to invigilate exams. If proctoring software will be used, this will be confirmed in the first week of class.

Students with hidden or visible disabilities who believe they may need class or exam accommodations, including in the current context of remote learning, are encouraged to register with the SFU Centre for Accessible Learning (caladmin@sfu.ca or 778-782-3112).