How to predict demand from historical "continuous" event data (date, lat, lon)? I am attempting to predict demand for our service, both quantity but maybe more important, location (hotspots).
I am by no means an experienced statistician, so I need some help :)
I have all the historic data for our service, date, latitude and longitude.
As far as I understand, the first thing to do is not to deal with the latitude and longitude: somehow they need to be converted into a single dimension right?
After that what type of analysis should be done to the data?
I think dealing with the date directly might also be the wrong way to go. My idea here is to deal only with week days, so I can predict the demand for a type of day (any Tuesday) instead of a specific Tuesday.
I am looking for some guidance as to how to achieve this. I am a good programmer, but I do need some help finding the right way.
 A: First of all, you need a programming language for predictive modeling.  I like the caret package for R a lot, but the scikits-learn project for python is also excellent if you are a python person.
Your data has 2 components: geography and time.  Geography is easier, so lets start there.  Most linear models are going to fail to find hotspots, particularly if those hotspots are defined by the interaction of 2 variables (latitude and longitude).  Lets pretend your data are constant, and hotspots don't change over time.  Many non-linear model will serve you well, in particular decision-tree based models (such as a single decision tree, a random forests, or a boosted forest).  Decision trees are able to identify regional hotspots.
Time is a little trickier.  Think about which components of time primarily effect demand for your product.  Hour-of-day? Day-of-week? Month-of-year? Holidays?  Create dummy variables to capture each of these effects, and include them in your model.
If you would like some example R code for any of the above, I'd be happy to provide it.
A: I would recommend first looking at your data within some visualisation package - eg Tableau.  This can cover map data easily too. "Heatmaps" come to mind for looking at hotspots!
