On Tue, 17 Aug 2010, Mayeul KAUFFMANN wrote:
Dear spatial statisticians and R users,
The following is posted for David Harte: ---------------------------------------------------- Attached is my response to a question raised on your list. My response to the list was rejected as I am not a member. David Harte Dear Mayeul The structure of the PtProcess package is based on the conditional intensity function (i.e. is history dependent), and if the user can specify their ground intensity function, then they can use the provided framework within the package to fit the model, simulate, and do various other things. Processes that are just time varying, with no explicit dependence on the history, can obviously be included with a null history. I understand that the models fitted by spatstat are somewhat different, and have no natural definition of past history and are based on a Papangelou conditional intensity (i.e. conditional on the spatial locations of other points, with no time ordering). A marked point process has a structure where the conditional intensity function is the product of the ground intensity function and the mark distributions. What sort of functional relationship are you proposing for your data? Can you write an equation for your ground intensity function? Can you specify a spatial mark distribution? In particular, can the spatial distribution be separated from the ground intensity as a mark? There is no reason why other covariates cannot be part of the defined ground intensity function, or the mark distribution. I do not think that the ETAS model will have any relevance for your situation. The spatial ETAS model is slightly peculiar as it does not satisfy the simple product of the ground intensity and spatial mark required for it to be a marked point process. It is a "compounded" mark point processes, i.e. I mean here the sum of many marked point processes; which makes it rather interesting. See the cited paper below for further information. If you can write an equation that represents your ground intensity function in terms of your covariates and any other information, and also for a spatial mark distribution, then I suggest that you look at the code for the simple time varying conditional intensity functions (see manual page for simple_gif, it will list the various functions). These are just non-homogeneous Poisson processes that are various functions of time. However, given your specification of your conditional intensity as a function of the covariates, you should be able to mimic the required structure, and write your own ground intensity function. Similarly, you will also need to specify your spatial mark distribution. You also mentioned the magnitude of the event. Is this independent of space? If not, then it will be part of a spatial-magnitude mark. If independent, then the spatial-magnitude density would be the product of the two densities. I have recently published further explanation about the package in the Journal of Statistical Software, see: http://www.jstatsoft.org/v35/i08/ Good luck David Harte -- David Harte Statistics Research Associates Ltd PO Box 12 649 Thorndon Wellington NEW ZEALAND Tel: +64-4-473 1760 Email: da...@statsresearch.co.nz Web: www.statsresearch.co.nz ----------------------------------------------------
I am trying to model a temporal marked point process with time-varying covariates and I am looking for the most appropriate function among several ones. (The events are violent events, such as fightings, in African countries). I want to model both the time/location of the events and at least one of the mark (the intensity of the event, measured for example by the number of persons killed). Events are collected over a few years. The time-resolution of the events is the day, while the covariates vary more slowly. The events have latitude and longitude, while the covariates are raster data (1km x 1km grid). I had a look at the following packages but I'm not sure I found the right solution yet: spatstat splancs PtProcess spatstat seems to have the correct object to handle my dependant variables (the ppx class: 2D space + time) but if I'm correct the ppm() model fitting function cannot handle this (it only works with ppp). Am I missing something? I saw at http://www.spatstat.org/ that this branch is in development. Any news / schedule on that? PtProcess does allow to estimate a time dependent marked point process (using etas_spatial() ). However, apparently, only the history of the point process itself can be taken into account (there are marks but they are no covariates: there is no data for locations without events). One workaround might be to include in the point process dummy events with (near-)zero intensity for all (or sampled) time-space cells and to attach the covariates as marks. What do you think? splancs does not seem to support this but has a nice space-time kernel smoothing function (kernel3d) and ability to display the result (kerview). I could transform the point process into a time-varying surface, but do not know how to model it either. The main aim is to measure the impact of the covariates on the point process. Ideally, the model should allow for time and space autocorelation among events (clustering is likely), similar to what the etas_spatial() function permits. Thanks for any comment! Regards, Mayeul KAUFFMANN PS: for reference, some messages I found close to my problem: https://stat.ethz.ch/pipermail/r-sig-geo/2010-March/007909.html https://stat.ethz.ch/pipermail/r-sig-geo/2009-September/006438.html https://stat.ethz.ch/pipermail/r-sig-geo/attachments/20091023/b33321bb/attachmen t.pl _____________________________________________________ Dr. Mayeul KAUFFMANN, Conflict Specialist European Commission, Joint Research Centre (JRC) Institute for the Protection and Security of the Citizen (IPSC) Global Security and Crisis Management - ISFEREA Via E. Fermi 2749 - I-21027 Ispra (VA), ITALY Phone: (+39) 033278 5071 http://isferea.jrc.ec.europa.eu/Staff/Pages/Kauffmann-Mayeul.aspx (Office: building 48c, 1st floor, room 123. TP: 483) _______________________________________________ R-sig-Geo mailing list R-sig-Geo@stat.math.ethz.ch https://stat.ethz.ch/mailman/listinfo/r-sig-geo
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