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SUMMARY:Alan Gelfand (Department of Statistical Science\, Duke University)
DTSTART:20230316T121500Z
DTEND:20230316T130000Z
DTSTAMP:20260422T155213Z
UID:gbgstats/11
DESCRIPTION:Title: <a href="https://researchseminars.org/talk/gbgstats/11/
 ">Three Spatial Data Fusion Vignettes</a>\nby Alan Gelfand (Department of 
 Statistical Science\, Duke University) as part of Gothenburg statistics se
 minar\n\nLecture held in MVL14.\n\nAbstract\nWith increased collection of 
 spatial (and spatio-temporal) datasets\, we often find multiple sources th
 at are capable of informing about features of a process of interest. Throu
 gh suitable fusion of the data sources\, we can learn at least as much abo
 ut the process features of interest than from any individual source.  For 
 three different illustrative ecological/environmental applications\, this 
 talk will propose suitable coherent stochastic modeling to implement a fus
 ion of these sources. We focus exclusively on approaches that arise throug
 h generative hierarchical modeling\; the specification could produce the d
 ata sources that have been observed.  Such modeling enables full inference
  both with regard to estimation and prediction\, with implicit incorporati
 on of uncertainty.  \n\nWe consider the general setting of points and mark
 s\, modeled as $[points][marks|points]$\, points in $\\mathcal{D}$\, marks
  in $\\mathcal{Y}$.  The process can model the points themselves\, the mar
 ks themselves (ignoring any randomness in the points)\, or the points and 
 marks jointly.  This results in four data types: (i) a point pattern\, $\\
 mathcal{S}= (\\textbf{s}_{1}\, \\textbf{s}_{2}\,\\ldots\,\\textbf{s}_{n})$
 \, (ii) a vector of counts for sets\, $\\{N(B_{k})\, k=1\,2\,\\ldots\,K\\}
 $\, (iii) a vector of observations at points\, $\\{Y(\\textbf{s}_{i})\,i=1
 \,2\,\\ldots\,n\\}$\, (iv) a vector of averages for sets\, $\\{Y(B_{1})\, 
 Y(B_{2})\,\\ldots\,Y(B_{k})\\}$.  We illustrate with two data sources\; ea
 ch can be any one of the four data types.  Regardless of how the data are 
 observed\, we imagine the process operates at point level. Further\, we im
 agine a stochastic process over $\\mathcal{D}$ which links the two data so
 urces.\n\nThe first vignette considers presence/absence data over $\\mathc
 al{D}$ with one dataset being presence/absence of a species collected at a
  set of chosen locations.  The other data source is in the form of museum/
 citizen science data\, recording random locations where the species was ob
 served.  The goal is to better understand the probability of presence surf
 ace over $\\mathcal{D}$. The second vignette considers zooplankton abundan
 ce data gathered through two different $\\it{towing}$ mechanisms.  One mec
 hanism is calibrated while the other is not.  The goal is to better unders
 tand zooplankton abundance over $\\mathcal{D}$.  The third\, and most chal
 lenging vignette seeks to learn about whale abundance.  Here\, the two sou
 rces are aerial distance sampling data for whale sightings and passive aco
 ustic monitoring data (using monitors on the ocean floor) for whale calls.
  \n\nThis is joint work with Shin Shirota\, Jorge Castillo-Mateo\, Erin Sc
 hliep\, and Rob Schick.\n
LOCATION:https://researchseminars.org/talk/gbgstats/11/
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