5 Search Results for "Harris, Paul"


Document
Short Paper
Smarter Than Your Average Model - Bayesian Model Averaging as a Spatial Analysis Tool (Short Paper)

Authors: Chris Brunsdon, Paul Harris, and Alexis Comber

Published in: LIPIcs, Volume 277, 12th International Conference on Geographic Information Science (GIScience 2023)


Abstract
Bayesian modelling averaging (BMA) allows the results of analysing competing data models to be combined, and the relative plausibility of the models to be assessed. Here, the potential to apply this approach to spatial statistical models is considered, using an example of spatially varying coefficient modelling applied to data from the 2016 UK referendum on leaving the EU.

Cite as

Chris Brunsdon, Paul Harris, and Alexis Comber. Smarter Than Your Average Model - Bayesian Model Averaging as a Spatial Analysis Tool (Short Paper). In 12th International Conference on Geographic Information Science (GIScience 2023). Leibniz International Proceedings in Informatics (LIPIcs), Volume 277, pp. 17:1-17:6, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2023)


Copy BibTex To Clipboard

@InProceedings{brunsdon_et_al:LIPIcs.GIScience.2023.17,
  author =	{Brunsdon, Chris and Harris, Paul and Comber, Alexis},
  title =	{{Smarter Than Your Average Model - Bayesian Model Averaging as a Spatial Analysis Tool}},
  booktitle =	{12th International Conference on Geographic Information Science (GIScience 2023)},
  pages =	{17:1--17:6},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-288-4},
  ISSN =	{1868-8969},
  year =	{2023},
  volume =	{277},
  editor =	{Beecham, Roger and Long, Jed A. and Smith, Dianna and Zhao, Qunshan and Wise, Sarah},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops-dev.dagstuhl.de/entities/document/10.4230/LIPIcs.GIScience.2023.17},
  URN =		{urn:nbn:de:0030-drops-189123},
  doi =		{10.4230/LIPIcs.GIScience.2023.17},
  annote =	{Keywords: Bayesian, Varying coefficient regression, Spatial statistics}
}
Document
Short Paper
Multiscale Spatially and Temporally Varying Coefficient Modelling Using a Geographic and Temporal Gaussian Process GAM (GTGP-GAM) (Short Paper)

Authors: Alexis Comber, Paul Harris, and Chris Brunsdon

Published in: LIPIcs, Volume 277, 12th International Conference on Geographic Information Science (GIScience 2023)


Abstract
The paper develops a novel approach to spatially and temporally varying coefficient (STVC) modelling, using Generalised Additive Models (GAMs) with Gaussian Process (GP) splines parameterised with location and time variables - a Geographic and Temporal Gaussian Process GAM (GTGP-GAM). This was applied to a Mongolian livestock case study and different forms of GTGP splines were evaluated in which space and time were combined or treated separately. A single 3-D spline with rescaled temporal and spatial attributes resulted in the best model under an assumption that for spatial and temporal processes interact a case studies with a sufficiently large spatial extent is needed. A fully tuned model was then created and the spline smoothing parameters were shown to indicate the degree of variation in covariate spatio-temporal interactions with the target variable.

Cite as

Alexis Comber, Paul Harris, and Chris Brunsdon. Multiscale Spatially and Temporally Varying Coefficient Modelling Using a Geographic and Temporal Gaussian Process GAM (GTGP-GAM) (Short Paper). In 12th International Conference on Geographic Information Science (GIScience 2023). Leibniz International Proceedings in Informatics (LIPIcs), Volume 277, pp. 22:1-22:6, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2023)


Copy BibTex To Clipboard

@InProceedings{comber_et_al:LIPIcs.GIScience.2023.22,
  author =	{Comber, Alexis and Harris, Paul and Brunsdon, Chris},
  title =	{{Multiscale Spatially and Temporally Varying Coefficient Modelling Using a Geographic and Temporal Gaussian Process GAM (GTGP-GAM)}},
  booktitle =	{12th International Conference on Geographic Information Science (GIScience 2023)},
  pages =	{22:1--22:6},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-288-4},
  ISSN =	{1868-8969},
  year =	{2023},
  volume =	{277},
  editor =	{Beecham, Roger and Long, Jed A. and Smith, Dianna and Zhao, Qunshan and Wise, Sarah},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops-dev.dagstuhl.de/entities/document/10.4230/LIPIcs.GIScience.2023.22},
  URN =		{urn:nbn:de:0030-drops-189173},
  doi =		{10.4230/LIPIcs.GIScience.2023.22},
  annote =	{Keywords: Spatial Analysis, Spatiotemproal Analysis}
}
Document
Short Paper
Geographically Varying Coefficient Regression: GWR-Exit and GAM-On? (Short Paper)

Authors: Alexis Comber, Paul Harris, Daisuke Murakami, Narumasa Tsutsumida, and Chris Brunsdon

Published in: LIPIcs, Volume 240, 15th International Conference on Spatial Information Theory (COSIT 2022)


Abstract
This paper describes initial work exploring two spatially varying coefficient models: multi-scale GWR and GAM Gaussian Process spline parameterised by observation location. Both approaches accommodate process spatial heterogeneity and both generate outputs that can be mapped indicating the nature of the process heterogeneity. However the nature of the process heterogeneity they each describe are very different. This suggests that the underlying semantics of such models need to be considered in order to refine the specificity of the questions that are asked of data: simply seeking to understand process spatial heterogeneity may be too semantically coarse.

Cite as

Alexis Comber, Paul Harris, Daisuke Murakami, Narumasa Tsutsumida, and Chris Brunsdon. Geographically Varying Coefficient Regression: GWR-Exit and GAM-On? (Short Paper). In 15th International Conference on Spatial Information Theory (COSIT 2022). Leibniz International Proceedings in Informatics (LIPIcs), Volume 240, pp. 13:1-13:10, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2022)


Copy BibTex To Clipboard

@InProceedings{comber_et_al:LIPIcs.COSIT.2022.13,
  author =	{Comber, Alexis and Harris, Paul and Murakami, Daisuke and Tsutsumida, Narumasa and Brunsdon, Chris},
  title =	{{Geographically Varying Coefficient Regression: GWR-Exit and GAM-On?}},
  booktitle =	{15th International Conference on Spatial Information Theory (COSIT 2022)},
  pages =	{13:1--13:10},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-257-0},
  ISSN =	{1868-8969},
  year =	{2022},
  volume =	{240},
  editor =	{Ishikawa, Toru and Fabrikant, Sara Irina and Winter, Stephan},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops-dev.dagstuhl.de/entities/document/10.4230/LIPIcs.COSIT.2022.13},
  URN =		{urn:nbn:de:0030-drops-168986},
  doi =		{10.4230/LIPIcs.COSIT.2022.13},
  annote =	{Keywords: Geographically weighted regression, Spatial Analysis, Process Spatial Heterogeneity, Model Semantics}
}
Document
Short Paper
A Comparison of Geographically Weighted Principal Components Analysis Methodologies (Short Paper)

Authors: Narumasa Tsutsumida, Daisuke Murakami, Takahiro Yoshida, Tomoki Nakaya, Binbin Lu, Paul Harris, and Alexis Comber

Published in: LIPIcs, Volume 240, 15th International Conference on Spatial Information Theory (COSIT 2022)


Abstract
Principal components analysis (PCA) is a useful analytical tool to represent key characteristics of multivariate data, but does not account for spatial effects when applied in geographical situations. A geographically weighted PCA (GWPCA) caters to this issue, specifically in terms of capturing spatial heterogeneity. However, in certain situations, a GWPCA provides outputs that vary discontinuously spatially, which are difficult to interpret and are not associated with the output from a conventional (global) PCA any more. This study underlines a GW non-negative PCA, a geographically weighted version of non-negative PCA, to overcome this issue by constraining loading values non-negatively. Case study results with a complex multivariate spatial dataset demonstrate such benefits, where GW non-negative PCA allows improved interpretations than that found with conventional GWPCA.

Cite as

Narumasa Tsutsumida, Daisuke Murakami, Takahiro Yoshida, Tomoki Nakaya, Binbin Lu, Paul Harris, and Alexis Comber. A Comparison of Geographically Weighted Principal Components Analysis Methodologies (Short Paper). In 15th International Conference on Spatial Information Theory (COSIT 2022). Leibniz International Proceedings in Informatics (LIPIcs), Volume 240, pp. 21:1-21:6, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2022)


Copy BibTex To Clipboard

@InProceedings{tsutsumida_et_al:LIPIcs.COSIT.2022.21,
  author =	{Tsutsumida, Narumasa and Murakami, Daisuke and Yoshida, Takahiro and Nakaya, Tomoki and Lu, Binbin and Harris, Paul and Comber, Alexis},
  title =	{{A Comparison of Geographically Weighted Principal Components Analysis Methodologies}},
  booktitle =	{15th International Conference on Spatial Information Theory (COSIT 2022)},
  pages =	{21:1--21:6},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-257-0},
  ISSN =	{1868-8969},
  year =	{2022},
  volume =	{240},
  editor =	{Ishikawa, Toru and Fabrikant, Sara Irina and Winter, Stephan},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops-dev.dagstuhl.de/entities/document/10.4230/LIPIcs.COSIT.2022.21},
  URN =		{urn:nbn:de:0030-drops-169062},
  doi =		{10.4230/LIPIcs.COSIT.2022.21},
  annote =	{Keywords: Spatial heterogeneity, Geographically weighted, Sparsity, PCA}
}
Document
Distributed Coloring of Graphs with an Optimal Number of Colors

Authors: Étienne Bamas and Louis Esperet

Published in: LIPIcs, Volume 126, 36th International Symposium on Theoretical Aspects of Computer Science (STACS 2019)


Abstract
This paper studies sufficient conditions to obtain efficient distributed algorithms coloring graphs optimally (i.e. with the minimum number of colors) in the LOCAL model of computation. Most of the work on distributed vertex coloring so far has focused on coloring graphs of maximum degree Delta with at most Delta+1 colors (or Delta colors when some simple obstructions are forbidden). When Delta is sufficiently large and c >= Delta-k_Delta+1, for some integer k_Delta ~~ sqrt{Delta}-2, we give a distributed algorithm that given a c-colorable graph G of maximum degree Delta, finds a c-coloring of G in min{O((log Delta)^{13/12}log n), 2^{O(log Delta+sqrt{log log n})}} rounds, with high probability. The lower bound Delta-k_Delta+1 is best possible in the sense that for infinitely many values of Delta, we prove that when chi(G) <= Delta-k_Delta, finding an optimal coloring of G requires Omega(n) rounds. Our proof is a light adaptation of a remarkable result of Molloy and Reed, who proved that for Delta large enough, for any c >= Delta-k_Delta deciding whether chi(G) <= c is in P, while Embden-Weinert et al. proved that for c <= Delta-k_Delta-1, the same problem is NP-complete. Note that the sequential and distributed thresholds differ by one. Our first result covers the case where the chromatic number of the graph ranges between Delta-sqrt{Delta} and Delta+1. Our second result covers a larger range, but gives a weaker bound on the number of colors: For any sufficiently large Delta, and Omega(log Delta) <= k <= Delta/100, we prove that every graph of maximum degree Delta and clique number at most Delta-k can be efficiently colored with at most Delta-epsilon k colors, for some absolute constant epsilon >0, with a randomized algorithm running in O(log n/log log n) rounds with high probability.

Cite as

Étienne Bamas and Louis Esperet. Distributed Coloring of Graphs with an Optimal Number of Colors. In 36th International Symposium on Theoretical Aspects of Computer Science (STACS 2019). Leibniz International Proceedings in Informatics (LIPIcs), Volume 126, pp. 10:1-10:15, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2019)


Copy BibTex To Clipboard

@InProceedings{bamas_et_al:LIPIcs.STACS.2019.10,
  author =	{Bamas, \'{E}tienne and Esperet, Louis},
  title =	{{Distributed Coloring of Graphs with an Optimal Number of Colors}},
  booktitle =	{36th International Symposium on Theoretical Aspects of Computer Science (STACS 2019)},
  pages =	{10:1--10:15},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-100-9},
  ISSN =	{1868-8969},
  year =	{2019},
  volume =	{126},
  editor =	{Niedermeier, Rolf and Paul, Christophe},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops-dev.dagstuhl.de/entities/document/10.4230/LIPIcs.STACS.2019.10},
  URN =		{urn:nbn:de:0030-drops-102496},
  doi =		{10.4230/LIPIcs.STACS.2019.10},
  annote =	{Keywords: Graph coloring, distributed algorithm, maximum degree}
}
  • Refine by Author
  • 4 Comber, Alexis
  • 4 Harris, Paul
  • 3 Brunsdon, Chris
  • 2 Murakami, Daisuke
  • 2 Tsutsumida, Narumasa
  • Show More...

  • Refine by Classification
  • 1 Information systems
  • 1 Information systems → Spatial-temporal systems
  • 1 Mathematics of computing → Bayesian nonparametric models
  • 1 Mathematics of computing → Graph algorithms
  • 1 Mathematics of computing → Graph coloring
  • Show More...

  • Refine by Keyword
  • 2 Spatial Analysis
  • 1 Bayesian
  • 1 Geographically weighted
  • 1 Geographically weighted regression
  • 1 Graph coloring
  • Show More...

  • Refine by Type
  • 5 document

  • Refine by Publication Year
  • 2 2022
  • 2 2023
  • 1 2019

Questions / Remarks / Feedback
X

Feedback for Dagstuhl Publishing


Thanks for your feedback!

Feedback submitted

Could not send message

Please try again later or send an E-mail