[postgis-users] Creating density map

[Aren Cambre]

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
<lists at 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
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