R Tutorial : Simulation and testing with spatstat

Want to learn more? Take the full course at https://learn.datacamp.com/courses/spatial-statistics-in-r at your own pace. More than a video, you'll learn hands-on coding & quickly apply skills to your daily work. --- The spatstat package is the first place to go to for spatial point pattern analysis. For simplicity everything will be only in two dimensions - but there's no reason why three-dimensional data can't be analysed this way and spatstat does have some functionality for data in 3D. These two dimensions will make a flat plane. Since spatstat closely implements the published theory of point patterns, you'll need to learn some of the language. First, a "point" is any location in the two-D space. For data on a flat plane this is specified by an X,Y coordinate pair where X and Y can be anything between plus and minus infinity. There might not be anything happening at a point, its just any location in the plane. An "event", however, is where something is happening - its one of your data points. Sometimes I might call an event a "point" - and most people will do that - but strictly, in the literature, a "point" means any location in the space and an "event" refers to an observed data point. For example, consider a tree scientist examining the position of trees. Although trees can exist at any point, the locations where the trees actually are, are events. Most spatial analyses are confined to a finite study area, and this area is called the "window". Events happening outside the window are unobserved. A "spatial point pattern" is the set of observed events and the window. A "spatial point process" is a stochastic process. Its like a random number generator for events in a window. Much of spatial point pattern analysis is spent making inferences about the point process that may have generated a data set. Spatial point processes may be defined over the whole of 2D space, but are only observed in a window. In a forest, the spatial process would depend on the s