[R-sig-Geo] Temporal marked point process with time-varying covariates

[Mayeul KAUFFMANN]

Dear spatial statisticians and R users,

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)