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
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