


At 17:10 05/05/2011, Roman Hornung wrote:
>Thank you both very much! Some more details on 
>the data: The individual roads are most of the 
>time represented by more than two points. The 
>road data consists of about 800000 points 
>representing around 75000 roads. I tried to 
>convert it to a linnet-object, but got the error 
>(translated): "error in matrix(FALSE, np, np): 
>too many elements specified", even when I set 
>the memory limit to the maximal possible value 
>(on a 32-bit computer). So I must probably give 
>up that approach. I must admit, I wasn't really 
>aware, that the intensity is not defined at 
>locations with no roads passing through. However 
>maybe, it is negligible, that the events can 
>only occur on roads and that there are "gaps" in 
>the prediction, since the network of roads is 
>very dense. What to you think? All the other 
>covariate are proper spatial covariates, so it 
>would be approriate to use a spatial poisson 
>process. Marcelino, you wrote: "By the way, 
>instead of a quadratureÂ  scheme you could also 
>use rsyst to generate directly a grided ppp with 
>"real" observations and then mark the 
>pointsÂ  at will." Does that mean, it is 
>irrelevant for estimation, which marks points 
>have, that aren't at locations, where the target variable is observed?




No, I didnt mean that. I was suggesting I 
possible way to create  the covariate " type of 
roads" as an extensive im object covering all 
your window. Then ppm would look up the pixel 
values of this image at the required locations (quadrature points).


>  In the latter case, I would be able to use the 
> quadrature scheme nevertheless, with dummy 
> points on the roads and random marks, thereby 
> circumventing the problem of converting the 
> road data to an image. However I noticed, that 
> ppm doesn't allow continuous marks in the trend formula.

  ppm does allow  continuous covariates in the 
trend formula, thats the convenience of covariates as im objects

Cheers,

Marcelino



>  Is there any other package that could handle 
> this? Perhaps I could use the code of the 
> corresponding function of such a package to 
> modify the ppm-function in spatstat. Otherwise, 
> if unavoidable, I would have to discretize the 
> daytime. Regards Roman -----UrsprÃ¼ngliche 
> Nachricht----- Von: "Rolf Turner" 
> <r.turner@auckland.ac.nz> Gesendet: 05.05.2011 
> 02:08:26 An: "Roman Hornung" 
> <romanhornung@web.de> Betreff: Re: [R-sig-Geo] 
> marked poisson process using a quadrature 
> scheme and covariates in 'spatstat' >On 
> 04/05/11 23:33, Roman Hornung wrote: >> Hello 
> everyone, >> >> My data consists of marked 
> points and several covariates, whereby the >> 
> marks are the time points of the observations. 
> The problem is, that >> one of the covariates 
> is hard to handle as an image. This 
> covariate >> represents the type of roads. As 
> there aren't roads at every location >> of the 
> map, one cannot specify the value of the 
> covariate at any point >> on a grid, which is 
> necessary to specify an image. To tackle this, 
> I >> wanted to use a quadrature scheme and use 
> points on the roads as dummy >> points instead 
> of a data point pattern as the outcome 
> variable. The >> problem I now encounter is, 
> that I can't give marks to the dummy >> points, 
> since they are no "real" observations (and 
> omitting the marks >> gives an error). >> Does 
> somebody know, what to do in such a 
> situation? >> >> Thank you very much in 
> advance! Any tips are appreciated much! > >I am 
> somewhat puzzled by what you are trying to do. 
> Covariates may be >used to >model the intensity 
> of a point pattern. The intensity is defined 
> at >every point of >the domain of definition of 
> the pattern; in practice it must be defined >at 
> every >point of the observation window. > >So 
> in your case the objective would be to specify 
> the intensity >"lambda(u)" in terms >of your 
> covariates at every point "u" of your 
> observation window. You >say that >one of your 
> covariates is ``type of road''. Obviously if no 
> road passes >through "u" >then there is no 
> ``type of road'' ***at*** "u". So how does 
> the >intensity at u depend >on ``type of 
> road''? Clarify that idea, and all of your 
> problems will >disappear. > >One idea, which 
> you may be subconsciously entertaining, is that 
> the >intensity at >"u" depends on the type of 
> the road nearest to u. Or it might depend on 
> the >types of all roads weighted in some way by 
> the distance of the roads >from "u". >Get it 
> clear in your mind first how you want to model 
> the intensity. Then >build your image, or 
> images, in terms of your roads data in a 
> manner >consistent >with your modelling 
> criteria. > >Note that the distmap() and 
> distfun() functions in "spatstat" will 
> allow >you to determine the >distance from a 
> given point to the nearest road (if the roads 
> are >represented as "psp" >objects). > > 
> cheers, > > Rolf Turner 
> _______________________________________________ 
> R-sig-Geo mailing list R-sig-Geo@r-project.org 
> https://stat.ethz.ch/mailman/listinfo/r-sig-geo

____________________________________

Marcelino de la Cruz Rot
Depto. Biologia Vegetal
EUIT Agricola
Universidad Politecnica de Madrid

tel: 34 + 913365435
____________________________________  
	[[alternative HTML version deleted]]

