5 Day 5 (June 5)

5.1 Announcements

  • Today is national donut day and the only opportunity for two magic extra credit points that will be applied to the journal assignments.

  • Comment about next week!

  • Recommended reading

    • Chapters 1 and 2 (pgs 1 - 27) in Linear Models with R

  • Questions and comments from journal

    • “What I learnt from today’s class is that most data we observe are often noisy measurements of an underlying process that we care about.”
    • “I’m struggling to understand what it means when the location is a random variable but time is usually fixed, and how these changes when time can also be random.”
    • “I do not understand how the different statistical models, such as polynomial regression or neural networks, are estimating the runner’s true trajectory and velocity.”
    • “I would like to understand where the concept of needing a sample size of at least 30 comes from.”
    • “Something that I am struggling with a little bit is how fast this class is going and being worried that I will not be ready to complete my class project at the end of the class. Today during class I was considering my project being something to do with my dog or in relation to the walks we go on. I’m just wanting to make sure that I am properly preparing.”

5.2 Intro to statistical modelling: human movement

  • The goal of this activity is to show you how cool spatio-temporal statistics is!

  • Human movement modeling with the linear regression model and other fancy tools!

  • Trajectories are a time series of the spatial location of an object (or animal).

    • We can usually pick the object and the time that we obtain its spatial location (i.e., time is fixed)
    • The location is a random variable in most cases, but time can also be a random variable.

  • In-class marathon example (Download R script here)

  • Further reading/learning

    • Hooten, M.B, D.S. Johnson, D.S., B.T. McClintock, J.M. Morales (2017) Animal Movement: Statistical Models for Telemetry Data. CRC press
    • Brost, B.M., Hooten, M.B., Hanks, E.M. and Small, R.J., 2015. Animal movement constraints improve resource selection inference in the presence of telemetry error. Ecology (link)