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I have recently posted a "very newie to R" question about the correct way of doing this, if you are interested in it you can find it [here].1

I have now managed to develop a simple R script that does the job, but now the results are what troubles me.

Long story short I'm using R to analyze lpp (Linear Point Pattern) with mad.test.That function performs an hypothesis test where the null hypothesis is that the points are randomly distributed. Currently I have 88 lpps to analyze, and according to the p.value 86 of them are randomly distributed and 2 of them are not.

These are the two not randomly distributed lpps. enter image description here enter image description here

Looking at them you can see some kind of clusters in the first one, but the second one only has three points, and seems to me that there is no way one can assure only three points are not corresponding to a random distribution. There are other tracks with one, two, three points but they all fall into the "random" lpps category, so I don't know why this one is different.

So here is the question: how many points are too little points for CSR testing?

I have also noticed that these two lpps have a much lower $statistic$rank than the others. I have tried to find what that means but I'm clueless righ now, so here is another newie question: Is the $statistic$rank some kind of quality analysis indicator, and thus can I use it to group my lpp analysis into "significant ones" and "too little points" ones?

My R script and all the shp files can be downloaded from here(850 Kb).

Thank you so much for your help.

Alberto Mnemon
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  • Thanks I too have the same question. How many are too little. I understand this depends on the context, but even within a context, there are sometimes too little points. So, for how many points or for how many points per unit length is worth considering for point pattern analysis. – BKS Aug 28 '17 at 06:27
  • Sorry, adding to your question because I am also looking for the same answer. Now, assume if you combine all your day events into one ppp and a daily lpp, it would give you different number of total points. If you make it combined for a week, you will have different number of total points. In my case, if I make these combinations ( hour, day, week, month), in my case all combinations are valid and still in my context, but I will get random or cluster pattern based on time aggregation I use. Some aggregations have very few points, so I wonder if it still valid to study them as point patterns. – BKS Aug 28 '17 at 07:11

2 Answers2

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It is impossible to give an universal answer to the question about how many points is needed for an analysis. Usually 0, 1 and 2 are too few for a standalone analysis. However, if they are part of repeated measurements of the same thing they might be interesting still. Also, I would normally say that your example with 3 points is too few to say anything interesting. However, an extreme example would be if you have a single long line segment where one point occurs close to one end and two other occur close to each other at the other end. This is not so likely to happen for CSR and you may be inclined to not believe that hypothesis. This appears to be what happened in your case.

Regarding your question about the rank you might want to read a bit more up on the Monte Carlo test you are preforming. Basically, you summarise the point pattern by a single number (maximum absolute deviation of linear K) and then you look at how extreme this number is compared to numbers generated at random from CSR. Assuming you use 99 simulations of CSR you have 100 numbers in total. If your data ranks as the most extreme ($statistic$rank==1) among these it has p-value 1%. If it ranks as the 50th number the p-value is 50%. If you used another number of simulations you have to calculate accordingly. I.e. with 199 simulations rank 1 is 0.5%, rank 2 is 1%, etc.

Ege Rubak
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  • Thank you so much @EgeRubak for explaining me what rank is. Seems to me that there is no way to obtain a "quality indicator", so maybe i'll restric my analysis to tracks with 4+ points on them. – Alberto Mnemon Aug 28 '17 at 11:58
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There is a fundamental problem here with multiple testing. You are applying a hypothesis test 88 times. The test is (by default) designed to give a false positive in 5 percent (1 in 20) of applications, so if the null hypothesis is true, you should expect 88 /20 = 4.4 false positives to have occurred your 88 tests. So getting only 2 positive results ("non-random") is entirely consistent with the null hypothesis that ALL of the patterns are random. My conclusion is that the patterns are random.

Adrian Baddeley
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