Skip to main content

LUP Student Papers

LUND UNIVERSITY LIBRARIES

Reconstruction of past European land cover from pollen data: using spatial statistics and Crank-Nicolson Monte Carlo

Svensson, Lovisa LU (2019) In Master's Theses in Mathematical Sciences FMSM01 20191
Mathematical Statistics
Abstract
Given a pollen data set from Europe over a time period, the aim is to reconstruct the past land cover by interpolating from the pollen data values to a continuous map. The data is on compositional form with three vegetation categories; coniferous forest, broadleaved forest and open land. Reconstruction will be based on a Gaussian Markov random field with separable spatio-temporal structure for the covariance matrix. The spatio-temporal covariance matrix is constructed by Kronecker products which simplifies many matrix computations. The field and parameters for the model are estimated by Markov Chain Monte Carlo, with a Crank Nicolson Langevin proposal to estimate the spatio-temporal field. Crank Nicolson Langevin method works well,... (More)
Given a pollen data set from Europe over a time period, the aim is to reconstruct the past land cover by interpolating from the pollen data values to a continuous map. The data is on compositional form with three vegetation categories; coniferous forest, broadleaved forest and open land. Reconstruction will be based on a Gaussian Markov random field with separable spatio-temporal structure for the covariance matrix. The spatio-temporal covariance matrix is constructed by Kronecker products which simplifies many matrix computations. The field and parameters for the model are estimated by Markov Chain Monte Carlo, with a Crank Nicolson Langevin proposal to estimate the spatio-temporal field. Crank Nicolson Langevin method works well, although implementation could be technical with a lot of details. Convergence for some of the model parameters is slow with bad mixing. The average compositional distance for the reconstruction and the validation set was 0.71. The model was better at finding temporal structure rather than spatial. Reconstructions from this model could be used as input to other models such as \citep{Strandberg} to investigate how anthropogenic deforestation, and other changes in nature, impacts climate change. (Less)
Please use this url to cite or link to this publication:
author
Svensson, Lovisa LU
supervisor
organization
course
FMSM01 20191
year
type
H2 - Master's Degree (Two Years)
subject
keywords
Pollen data, compositional data, Dirichlet distribution, spatio-temporal reconstruction, Kronecker product, Gaussian Markov random field (GMRF), Markov Chain Monte Carlo (MCMC), Metropolis Hastings (MH), Metropolis adjusted Langevin algorithm (MALA)
publication/series
Master's Theses in Mathematical Sciences
report number
LUTFMS-3382-2019
ISSN
1404-6342
other publication id
2019:E56
language
English
id
8994989
date added to LUP
2019-09-19 13:46:34
date last changed
2019-09-19 13:46:34
@misc{8994989,
  abstract     = {{Given a pollen data set from Europe over a time period, the aim is to reconstruct the past land cover by interpolating from the pollen data values to a continuous map. The data is on compositional form with three vegetation categories; coniferous forest, broadleaved forest and open land. Reconstruction will be based on a Gaussian Markov random field with separable spatio-temporal structure for the covariance matrix. The spatio-temporal covariance matrix is constructed by Kronecker products which simplifies many matrix computations. The field and parameters for the model are estimated by Markov Chain Monte Carlo, with a Crank Nicolson Langevin proposal to estimate the spatio-temporal field. Crank Nicolson Langevin method works well, although implementation could be technical with a lot of details. Convergence for some of the model parameters is slow with bad mixing. The average compositional distance for the reconstruction and the validation set was 0.71. The model was better at finding temporal structure rather than spatial. Reconstructions from this model could be used as input to other models such as \citep{Strandberg} to investigate how anthropogenic deforestation, and other changes in nature, impacts climate change.}},
  author       = {{Svensson, Lovisa}},
  issn         = {{1404-6342}},
  language     = {{eng}},
  note         = {{Student Paper}},
  series       = {{Master's Theses in Mathematical Sciences}},
  title        = {{Reconstruction of past European land cover from pollen data: using spatial statistics and Crank-Nicolson Monte Carlo}},
  year         = {{2019}},
}