Lauren Brideau
Imagine you love baking cookies and invite your friends over for a cookie party. You want to know how many cookies you should make so you ask your friends about how many cookies they think they will each eat. They respond:
- Francesca: 5 cookies
- Sydney: 3 cookies
- Noelle: 1 cookie
- James: 7 cookies
- Brooke: 2 cookies
We take these numbers and add all of them together to estimate that about 18 cookies will be eaten in total at our party.
The spread of disease, politics, the movement of animals, regions vulnerable to earthquakes and where people are most likely to buy frosted flakes are all informed by spatial data. Spatial data links information to specific positions on earth and can tell us about patterns that play out from location to location. We can use spatial data to uncover processes over space and tackle complex problems.
Real world problems and data are complex and there are often situations where we want to simultaneously look at relationships between more than one variable. We call these analyses multivariate statistics. Non-metric multidimensional scaling, or NMDS, is one multivariate technique that allows us to visualize these complex relationships in less dimensions. In other words, NMDS takes complex, multivariate data and represents the relationships in a way that is easier for interpretation.