# How to test whether or not a category (month) affects a continuous distribution (latitude, longitude)?

I am trying to answer a question as to whether or not the month of the year affects the geographical distribution of hurricanes in the Atlantic ocean.

I know for example about hurricane season, and that time affects the frequency of hurricanes. But does it affect the geographic distribution of hurricanes?

Would this be a good use of ANOVA, where month is the category and location is the continuous variable? And if so, how would you apply ANOVA when there are two dependent variables that you care about (latitude and longitude)? Would you just do it twice, once for latitude and once for longitude?

• I am not sure it makes sense to "hold the number of hurricanes fixed". Also, look into spatial statistics for modeling locations. Regarding months, don't treat months as a factor. That would amount to treating April 1 and April 30 as identical, but May 1 as completely different from either one. That doesn't make sense. Also, you will need to expend 11 degrees of freedom to model 12 months. Much better to use a (possibly periodic) spline transform of the date. You will get a smooth relationship with enough flexibility for a fraction of the dfs. – Stephan Kolassa Apr 1 at 16:27
• Hi @StephanKolassa, I'm afraid you just spoke a lot of Greek as far as I'm concerned. I understand only the very basics of probability distributions and statistical tests, so you'll have to dumb it down a bit. Sorry about that :/ – rocksNwaves Apr 1 at 16:57
• No problem. I recommend you read up on splines. Frank Harrell's Regression Modeling Strategies has a very readable introduction. However, if you want to do spatial modeling, you should really dig rather deeply into the appropriate statistics, or find a statistician as a collaborator. – Stephan Kolassa Apr 1 at 17:03
• It is definitely a good question, not shallow at all, and a great place to start! The problem I see is that the treatment of time (monthly dummies vs. spline transform) is much easier than a reasonable spatial model, so your underlying challenge is really not the one you started with here. Unfortunately, while I could discuss the time transform, I am not qualified to opine on spatial statistics. We do have experts on spatial statistics here, it's just that it sounds like you would most profit from a textbook first. – Stephan Kolassa Apr 1 at 17:15
• @kjetilbhalvorsen That's a good idea. I have visualized the data via geopandas, but I'm not sure if the difference in distributions month to month is due to the sparsity of the data after aggregation by month or some actual relationship. That's why I want to find an appropriate statistical test. – rocksNwaves Apr 2 at 13:12