5 Search Results for "Brunsdon, Chris"

Document
DOI: 10.4230/LIPIcs.GIScience.2025.14
Guiding Geospatial Analysis Processes in Dealing with Modifiable Areal Unit Problems

Authors: Guoray Cai and Yue Hao

Published in: LIPIcs, Volume 346, 13th International Conference on Geographic Information Science (GIScience 2025)

Abstract
Geospatial analysis has been widely applied in different domains for critical decision making. However, the results of spatial analysis are often plagued with uncertainties due to measurement errors, choice of data representations, and unintended transformation artifacts. A well known example of such problems is the Modifiable Areal Unit Problem (MAUP) which has well documented effects on the outcome of spatial analysis on area-aggregated data. Existing methods for addressing the effects of MAUP are limited, are technically complex, and are often inaccessible to practitioners. As a result, analysts tend to ignore the effects of MAUP in practice due to lack of expertise, high cognitive loads, and resource limitations. To address these challenges, this paper proposes a machine-guidance approach to augment the analyst’s capacity in mitigating the effect of MAUP. Based on an analysis of practical challenges faced by human analysts, we identified multiple opportunities for the machine to guide the analysts by alerting to the rise of MAUP, assessing the impact of MAUP, choosing mitigation methods, and generating visual guidance messages using GIS functions and tools. For each of the opportunities, we characterize the behavior patterns and the underlying guidance strategies that generate the behavior. We illustrate the behavior of machine guidance using a hotspot analysis scenario in the context of crime policing, where MAUP has strong effects on the patterns of crime hotspots. Finally, we describe the computational framework used to build a prototype guidance system and identify a number of research questions to be addressed. We conclude by discussing how the machine guidance approach could be an answer to some of the toughest problems in geospatial analysis.
Cite as

Guoray Cai and Yue Hao. Guiding Geospatial Analysis Processes in Dealing with Modifiable Areal Unit Problems. In 13th International Conference on Geographic Information Science (GIScience 2025). Leibniz International Proceedings in Informatics (LIPIcs), Volume 346, pp. 14:1-14:18, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025)

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@InProceedings{cai_et_al:LIPIcs.GIScience.2025.14,
  author =	{Cai, Guoray and Hao, Yue},
  title =	{{Guiding Geospatial Analysis Processes in Dealing with Modifiable Areal Unit Problems}},
  booktitle =	{13th International Conference on Geographic Information Science (GIScience 2025)},
  pages =	{14:1--14:18},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-378-2},
  ISSN =	{1868-8969},
  year =	{2025},
  volume =	{346},
  editor =	{Sila-Nowicka, Katarzyna and Moore, Antoni and O'Sullivan, David and Adams, Benjamin and Gahegan, Mark},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.GIScience.2025.14},
  URN =		{urn:nbn:de:0030-drops-238433},
  doi =		{10.4230/LIPIcs.GIScience.2025.14},
  annote =	{Keywords: Machine Guidance, Geo-Spatial Analysis, Modifiable Areal Unit Problem (MAUP)}
}
Document
DOI: 10.4230/LIPIcs.GIScience.2025.15
Accommodating Space-Time Scaling Issues in GAM-Based Varying Coefficient Models

Authors: Alexis Comber, Paul Harris, and Chris Brunsdon

Published in: LIPIcs, Volume 346, 13th International Conference on Geographic Information Science (GIScience 2025)

Abstract
The paper describes modifications to spatial and temporal varying coefficient (STVC) modelling, using Generalized Additive Models (GAMs). Previous work developed tools using Gaussian Process (GP) thin plate splines parameterised with location and time variables, and has presented a space-time toolkit in the stgam R package, providing wrapper functions to the mgcv R package. However, whilst thin plate smooths with GP bases are acceptable for working with spatial problems they are not for working with space and time combined. A more robust approach is to use a tensor product smooth with GP basis. However, these in turn require correlation function length scale or range parameters (ρ) to be defined. These are distances (in space or time) at which the correlation function falls below some value, and can be used to indicate the scale of spatial and temporal dependencies between response and predictor variables (similar to geographically weighted bandwidths). The paper describes the problem in detail, illustrates an approach for optimising ρ and methods for determining model specification.
Cite as

Alexis Comber, Paul Harris, and Chris Brunsdon. Accommodating Space-Time Scaling Issues in GAM-Based Varying Coefficient Models. In 13th International Conference on Geographic Information Science (GIScience 2025). Leibniz International Proceedings in Informatics (LIPIcs), Volume 346, pp. 15:1-15:9, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025)

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@InProceedings{comber_et_al:LIPIcs.GIScience.2025.15,
  author =	{Comber, Alexis and Harris, Paul and Brunsdon, Chris},
  title =	{{Accommodating Space-Time Scaling Issues in GAM-Based Varying Coefficient Models}},
  booktitle =	{13th International Conference on Geographic Information Science (GIScience 2025)},
  pages =	{15:1--15:9},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-378-2},
  ISSN =	{1868-8969},
  year =	{2025},
  volume =	{346},
  editor =	{Sila-Nowicka, Katarzyna and Moore, Antoni and O'Sullivan, David and Adams, Benjamin and Gahegan, Mark},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.GIScience.2025.15},
  URN =		{urn:nbn:de:0030-drops-238440},
  doi =		{10.4230/LIPIcs.GIScience.2025.15},
  annote =	{Keywords: Spatial Analysis, Spatiotemproal Analysis}
}
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.COSIT.2022.13
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)

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@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.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}
}
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