[R-sig-Geo] Impute missing values along a spatial network

[Thierry Onkelinx]

Dear Tobias,

Keep in mind that you'll need multiple imputation (). If you use the
predicted values to impute, then you'll get a variance which is lower than
the variance of the complete dataset. Whereas the variance should increase
with the amount of missing data. Multiple imputation solves that problem.

Best regards,

ir. Thierry Onkelinx
Statisticus / Statistician

Vlaamse Overheid / Government of Flanders
INSTITUUT VOOR NATUUR- EN BOSONDERZOEK / RESEARCH INSTITUTE FOR NATURE AND
FOREST
Team Biometrie & Kwaliteitszorg / Team Biometrics & Quality Assurance
thierry.onkelinx using inbo.be
Havenlaan 88 bus 73, 1000 Brussel
www.inbo.be

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Op wo 24 mrt. 2021 om 16:22 schreef Tobias Ruttenauer <
tobias.ruttenauer using nuffield.ox.ac.uk>:

> Thank you very much for the hint, Roger! Completely right, a Gaussian
> process actually does not make too much sense in this case. I'll have a
> look into INLA and see if I can work with that.
>
> For now, I don't have any covariates in this. This is mainly because I'm
> specifically interested in the annual variation of traffic counts, but
> available covariates are all time-constant.
>
> Thanks again and best wishes
> Tobias
>
>
> -----Original Message-----
> From: Roger Bivand <Roger.Bivand using nhh.no>
> Sent: 24 March 2021 14:18
> To: Tobias Ruttenauer <tobias.ruttenauer using nuffield.ox.ac.uk>
> Cc: r-sig-geo using r-project.org
> Subject: Re: [R-sig-Geo] Impute missing values along a spatial network
>
> On Wed, 24 Mar 2021, Tobias Ruttenauer wrote:
>
> > Dear list members,
> >
> > I am trying to construct a road network with traffic estimates for
> > each road segment. I have count data of the traffic for a subset of
> > the segments and I have the road network as spatial lines data. For
> > those segments without count data, I would like to perform something
> > like linear imputation or some sort of interpolation / kriging along
> > the road network instead of using pure geographical distance. For
> > instance, if I have 7 road segments A-B-C-D-E and F-G (F and G are
> > unconnected to the rest), and I have data for A and D, how can I
> > impute data for B, C (and
> > E) by only using A and D, while ignoring F and G even though they
> > might be geographically close?
>
> Are there any relevant covariates associated with the road segments? I
> think that this is more of a Markov than a Gaussian random field, so a
> Poisson spatial regression with a neighbour matrix representing contiguous
> segments might be possible. Covariates, or an offset by an expected volume
> might help. INLA with a Besag model - INLA fits missing responses, or
> mgcv::gam() with an "mrf" smooth or hglm() then predict?
>
> Any other suggestions?
>
> Roger
>
> >
> > This seems fairly intuitive to me but I couldn't find a package doing
> > that. stplanr would do something related but it seems it needs
> > origin-destination data (which I don't have). I'd be grateful if
> > someone could nudge me into the right direction. I guess I'm using the
> > wrong terminology.
> >
> > Thanks a lot and best wishes
> > Tobias
> >
> > Tobias Rüttenauer
> > Nuffield College
> > University of Oxford
> > Oxford, OX1 1NF
> >
> > _______________________________________________
> > R-sig-Geo mailing list
> > R-sig-Geo using r-project.org
> > https://stat.ethz.ch/mailman/listinfo/r-sig-geo
> >
>
> --
> Roger Bivand
> Department of Economics, Norwegian School of Economics, Postboks 3490 Ytre
> Sandviken, 5045 Bergen, Norway.
> e-mail: Roger.Bivand using nhh.no
> https://orcid.org/0000-0003-2392-6140
> https://scholar.google.no/citations?user=AWeghB0AAAAJ&hl=en
>
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