OK so I now know my point data is Random. Brilliant. But now what?
How does it add anything to the understanding of the data?
Sorry if that's a basic question but I honestly don't know what Point Pattern Analysis is good other than comparing the result with the answers in the book.
If a point process is found to be completely random, then the events don't depend on each other and don't depend on external variables. These are strong statements. For example if shark attacks were completely random in this sense, then it would be pointless to stay out of the water after an attack, since one attack has no predictive value for other attacks; and attacks would be equally likely at all times of day and all kinds of weather.
You are correct in thinking that the null hypothesis of (Complete Spatial) Randomness is rarely of much scientific interest, in any direct sense. CSR usually functions only as a benchmark against which to measure the behaviour of the point process under study.
As with any statistical procedure, point pattern analysis serves to provide insight into the natural mechanism generating the observed data. It may help to anwer questions about this mechanism or to suggest precisely focussed questions that should be asked. What these questions might be depends upon the particular area of application. Likewise how such analysis adds to the understanding of the data depends upon the area of application. What do you want to know about the underlying mechanism? What do you want the data to tell you? What conjectures are you interested in verifying or falsifying?
Point pattern analysis will add to your understanding of a data set only if you have some interest in the data set and care about the information that might be contained in that data set.
Read "Spatial Point Patterns: Methodology and Applications with R" by Baddeley, Rubak and Turner (see http://spatstat.github.io) for many examples of ways in which point patterns may be fruitfully analysed.
