5 Search Results for "Harris, Sarah"

Document
Short Paper
DOI: 10.4230/LIPIcs.GIScience.2023.17
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)

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@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.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
DOI: 10.4230/LIPIcs.GIScience.2023.22
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)

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@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.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
DOI: 10.4230/LIPIcs.GIScience.2023.39
A Hierarchical and Geographically Weighted Regression Model and Its Backfitting Maximum Likelihood Estimator (Short Paper)

Authors: Yigong Hu, Richard Harris, Richard Timmerman, and Binbin Lu

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

Abstract
Spatial heterogeneity is a typical and common form of spatial effect. Geographically weighted regression (GWR) and its extensions are important local modeling techniques for exploring spatial heterogeneity. However, when dealing with spatial data sampled at a micro-level but the geographical locations of them are only known at a higher level, GWR-based models encounter several problems, such as difficulty in establishing the bandwidth. Because data with this characteristic exhibit spatial hierarchical structures, such data can be suitably handled using hierarchical linear modeling (HLM). This model calibrates random effects for sample-level variables in each group to address spatial heterogeneity. However, it does not work when exploring spatial heterogeneity in some group-level variables when there is insufficient variance in each group. In this study, we therefore propose a hierarchical and geographically weighted regression (HGWR) model, together with a back-fitting maximum likelihood estimator, that can be applied to examine spatial heterogeneity in the regression relationships of data where observations nest into high-order groupings and share the same or very close coordinates within those groups. The HGWR model divides coefficients into three types: local fixed effects, global fixed effects, and random effects. Results of a simulation experiment show that HGWR distinguishes local fixed effects from others and also global effects from random effects. Spatial heterogeneity is reflected in the estimates of local fixed effects, along with the spatial hierarchical structure. Compared with GWR and HLM, HGWR produces estimates with the lowest deviations of coefficient estimates. Thus, the ability of HGWR to tackle both spatial and group-level heterogeneity simultaneously suggests its potential as a promising data modeling tool for handling the increasingly common occurrence where data, in secure settings for example, remove the specific geographic identifiers of individuals and release their locations only at a group level.
Cite as

Yigong Hu, Richard Harris, Richard Timmerman, and Binbin Lu. A Hierarchical and Geographically Weighted Regression Model and Its Backfitting Maximum Likelihood Estimator (Short Paper). In 12th International Conference on Geographic Information Science (GIScience 2023). Leibniz International Proceedings in Informatics (LIPIcs), Volume 277, pp. 39:1-39:6, Schloss Dagstuhl - Leibniz-Zentrum für Informatik (2023)

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@InProceedings{hu_et_al:LIPIcs.GIScience.2023.39,
  author =	{Hu, Yigong and Harris, Richard and Timmerman, Richard and Lu, Binbin},
  title =	{{A Hierarchical and Geographically Weighted Regression Model and Its Backfitting Maximum Likelihood Estimator}},
  booktitle =	{12th International Conference on Geographic Information Science (GIScience 2023)},
  pages =	{39:1--39:6},
  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.dagstuhl.de/entities/document/10.4230/LIPIcs.GIScience.2023.39},
  URN =		{urn:nbn:de:0030-drops-189342},
  doi =		{10.4230/LIPIcs.GIScience.2023.39},
  annote =	{Keywords: spatial modelling, hierarchical data, spatial heterogeneity, geographically weighted regression}
}
Document
Short Paper
DOI: 10.4230/LIPIcs.GIScience.2023.40
Introducing a General Framework for Locally Weighted Spatial Modelling Based on Density Regression (Short Paper)

Authors: Yigong Hu, Binbin Lu, Richard Harris, and Richard Timmerman

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

Abstract
Traditional geographically weighted regression and its extensions are important methods in the analysis of spatial heterogeneity. However, they are based on distance metrics and kernel functions compressing differences in multidimensional coordinates into one-dimensional values, which rarely consider anisotropy and employ inconsistent definitions of distance in spatio-temporal data or spatial line data (for example). This article proposes a general framework for locally weighted spatial modelling to overcome the drawbacks of existing models using geographically weighted schemes. Underpinning it is a multi-dimensional weighting scheme based on density regression that can be applied to data in any space and is not limited to geographic distance.
Cite as

Yigong Hu, Binbin Lu, Richard Harris, and Richard Timmerman. Introducing a General Framework for Locally Weighted Spatial Modelling Based on Density Regression (Short Paper). In 12th International Conference on Geographic Information Science (GIScience 2023). Leibniz International Proceedings in Informatics (LIPIcs), Volume 277, pp. 40:1-40:7, Schloss Dagstuhl - Leibniz-Zentrum für Informatik (2023)

Copy BibTex To Clipboard

@InProceedings{hu_et_al:LIPIcs.GIScience.2023.40,
  author =	{Hu, Yigong and Lu, Binbin and Harris, Richard and Timmerman, Richard},
  title =	{{Introducing a General Framework for Locally Weighted Spatial Modelling Based on Density Regression}},
  booktitle =	{12th International Conference on Geographic Information Science (GIScience 2023)},
  pages =	{40:1--40:7},
  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.dagstuhl.de/entities/document/10.4230/LIPIcs.GIScience.2023.40},
  URN =		{urn:nbn:de:0030-drops-189354},
  doi =		{10.4230/LIPIcs.GIScience.2023.40},
  annote =	{Keywords: Spatial heterogeneity, Multidimensional space, Density regression, Spatial statistics}
}
Document
Experience Paper
DOI: 10.4230/LIPIcs.ECOOP.2023.39
Rust for Morello: Always-On Memory Safety, Even in Unsafe Code (Experience Paper)

Authors: Sarah Harris, Simon Cooksey, Michael Vollmer, and Mark Batty

Published in: LIPIcs, Volume 263, 37th European Conference on Object-Oriented Programming (ECOOP 2023)

Abstract
Memory safety issues are a serious concern in systems programming. Rust is a systems language that provides memory safety through a combination of a static checks embodied in the type system and ad hoc dynamic checks inserted where this analysis becomes impractical. The Morello prototype architecture from ARM uses capabilities, fat pointers augmented with object bounds information, to catch failures of memory safety. This paper presents a compiler from Rust to the Morello architecture, together with a comparison of the performance of Rust’s runtime safety checks and the hardware-supported checks of Morello. The cost of Morello’s always-on memory safety guarantees is 39% in our 19 benchmark suites from the Rust crates repository (comprising 870 total benchmarks). For this cost, Morello’s capabilities ensure that even unsafe Rust code benefits from memory safety guarantees.
Cite as

Sarah Harris, Simon Cooksey, Michael Vollmer, and Mark Batty. Rust for Morello: Always-On Memory Safety, Even in Unsafe Code (Experience Paper). In 37th European Conference on Object-Oriented Programming (ECOOP 2023). Leibniz International Proceedings in Informatics (LIPIcs), Volume 263, pp. 39:1-39:27, Schloss Dagstuhl - Leibniz-Zentrum für Informatik (2023)

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@InProceedings{harris_et_al:LIPIcs.ECOOP.2023.39,
  author =	{Harris, Sarah and Cooksey, Simon and Vollmer, Michael and Batty, Mark},
  title =	{{Rust for Morello: Always-On Memory Safety, Even in Unsafe Code}},
  booktitle =	{37th European Conference on Object-Oriented Programming (ECOOP 2023)},
  pages =	{39:1--39:27},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-281-5},
  ISSN =	{1868-8969},
  year =	{2023},
  volume =	{263},
  editor =	{Ali, Karim and Salvaneschi, Guido},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.ECOOP.2023.39},
  URN =		{urn:nbn:de:0030-drops-182322},
  doi =		{10.4230/LIPIcs.ECOOP.2023.39},
  annote =	{Keywords: Compilers, Rust, Memory Safety, CHERI}
}
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