planetwater

ground- water, geo- statistics, environmental- engineering, earth- science

Day 1 at #spatialstatistics2017

without comments

Peter Atkinson opened the conference with pointing out the broad scope of the conference: “one health” (e.g., CDC, UC Davis) that relates to human, veterinary, and environmental health. I was glad that my talk with interpolating groundwater quality data fit right into that scope.

I saw too many interesting talks and met too many interesting and nice people, to list everything here. Instead, this is a small selection.

Connections

First off, it’s nice to encounter similarly minded work. Particularly, I was happy to see the following presentations:

  • Emilie Chautru presented a poster entitled “Cokriging of Nonnegative Data on the L1 Sphere”, on Cokriging compositional data;
  • Svenia Behm from the University of Passau presented a talk entitled “Statistical Inference in the RIO Model – the Detrending Step Revisited”. She calculates something similar to my “locally mixed distributions”;
  • A. Lawson pointed out the importance of properly taking censored measurements and true zeros into account, both in his keynote (“One Health: Spatial Statistics at the Border of Human and Veterinary Health”) and in his talk (“Bayesian Cure-Rate Survival Model With Spatially Structured Censoring”). I didn’t talk about it at this conference, but it is dear to my heart;

Cool Stuff

  • M. Pereira showed cool images of road crash density estimates based on data from Paris, France. Benedikt Gräler showed a poster with the Envirocar initiative. Data related to driving patterns and fuel consumption is collected while driving, is analysed, and can be viewed online.
  • Samir Bhatt gave a great keynote presentationon mapping malaria endemicity. Besides the interesting issues related directly to malaria, this talk raised some interesting questions on modelling philosophies. Samir Bhatt proposed “richer models” as a way forward beyond his current practice of using multivariate models. Alternatively, he phrased it as models that “include mechanisms”. Peter Diggle asked how his approach relates to the concept of parsimonity. It is interesting to me that Samir Batt suggests to include mechanistic models in his data driven models, whereas for the groundwater quality mapping project I am working on, I have moved to a stochastic model. On the scale of the state, I see that deterministic, pde-based models are not feasible (too many unknown parameters and processes).

Written by Claus

July 6th, 2017 at 8:44 am

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