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 R?ttenauer Nuffield College University of Oxford Oxford, OX1 1NF
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Roger Bivand Department of Economics, Norwegian School of Economics, Postboks 3490 Ytre Sandviken, 5045 Bergen, Norway. e-mail: Roger.Bivand at nhh.no https://orcid.org/0000-0003-2392-6140 https://scholar.google.no/citations?user=AWeghB0AAAAJ&hl=en