Thank you! I'll contact them. Clearly I have more reading to do.
Aren On Sun, Jan 9, 2011 at 10:10 PM, Ben Madin <li...@remoteinformation.com.au>wrote: > Aren, > > You might have more luck on the R-sig-geo list. This looks like a mix of > network analysis and point processes - > > check out pgRouting, or look for routing algorithms > check out the sp package and then spatstat, network etc. > > An approach (by no means the right or even a good one) is to think of it as > a graph or MCMC problem, and consider the relationship between the events as > probabilities that can be affected by distance. You are of course trying to > create a complex spatio-temporal model like everyone, so there is a fair bit > of literature out there. It probably pays to look at relevant articles in > your field. > > Once you have this clearer, I think you should be able to work out how to > get the data into R - but it could either be as a data frame using spatial > SQL to return attributes, or using readOGR if you need spatial objects in R > > cheers > > Ben > > > > > On 10/01/2011, at 2:50 PM, Aren Cambre wrote: > > I have three datasets: > > 1. Routes > 2. Event type A that occurs along the routes (points) > 3. Event type B that occurs along the routes (points) > > Both event types have several attributes, including a date/timestamp, > sub-classes of each event type, and other meaningful attributes. > > I'm trying to use statistical methods to check for certain relationships > between event types A and B. They may influence each other (A may affect B > and B may affect A). I also want to see if there's a relationship between > subtypes. E.g., do events A.X or A.Y have a stronger impact on event type B? > > I'd like to make heat density maps to help interpret the data, but I have > two conceptual problems. > > *First problem is how to make the map.* The programmatically easy but slow > way is to create a greedy algorithm to traverse every route. During > traversal, create a point at each increment of distance X. An attribute of > that point may be the number of qualifying events no more than distance Y > from that point. > > I may need to limit to events along the route I am traversing. E.g., if > traversing route M looking for event type B, and I come across route N, the > heat map for route M probably should not include events of type B along > route N event if they are within Y distance from my current point. > > *Second problem is how to deal with all the permutations. *I could muck > through the simple algorithm and make spiffy point maps, and with a little > graphical wizardry, I could make the maps pretty. However, I need to do > analysis over different time periods. E.g., does the relative intensity of > week I's event type As along route M affect the occurrence of event type B > on week I+1? How about event type A.X? A.Y? Do they have different effects > over the same time period? I have between 3 and 9 years of event types A and > B... > > All the permutations (not simply combinations) of factors can really > explode the complexity of this project. > > To prevent wheel reinventing, are there already well-tread solutions to > this problem? I've done some Google searches and am not coming up with much, > so I guess I may not be using the correct lingo? > > I know that I need to incorporate R into this at some point; my objective > now is to get the data to a point where I *could* use R to analyze it. > > Aren > _______________________________________________ > postgis-users mailing list > postgis-users@postgis.refractions.net > http://postgis.refractions.net/mailman/listinfo/postgis-users > > > > _______________________________________________ > postgis-users mailing list > postgis-users@postgis.refractions.net > http://postgis.refractions.net/mailman/listinfo/postgis-users > >
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