6 Search Results for "Bartlett, Roger"


Document
Short Paper
Achieving Least Relocation of Existing Facilities in Spatial Optimisation: A Bi-Objective Model (Short Paper)

Authors: Huanfa Chen and Rongbo Xu

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


Abstract
Spatial optimisation models have been widely used to support locational decision making of public service systems (e.g. hospitals, fire stations), such as selecting the optimal locations to maximise the coverage. These service systems are generally the product of long-term evolution, and there usually are existing facilities in the system. These existing facilities should not be neglected or relocated without careful consideration as they have financial or management implications. However, spatial optimisation models that account for the relocation or maintenance of existing facilities are understudied. In this study, we revisit a planning scenario where two objectives are adopted, including the minimum number of sites selected and the least relocation of existing facilities. We propose and discuss three different approaches that can achieve these two objectives. This model and the three approaches are applied to two case studies of optimising the retail stores in San Francisco and the large-scale COVID-19 vaccination network in England. The implications of this model and the efficiency of these approaches are discussed.

Cite as

Huanfa Chen and Rongbo Xu. Achieving Least Relocation of Existing Facilities in Spatial Optimisation: A Bi-Objective Model (Short Paper). In 12th International Conference on Geographic Information Science (GIScience 2023). Leibniz International Proceedings in Informatics (LIPIcs), Volume 277, pp. 19:1-19:5, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2023)


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@InProceedings{chen_et_al:LIPIcs.GIScience.2023.19,
  author =	{Chen, Huanfa and Xu, Rongbo},
  title =	{{Achieving Least Relocation of Existing Facilities in Spatial Optimisation: A Bi-Objective Model}},
  booktitle =	{12th International Conference on Geographic Information Science (GIScience 2023)},
  pages =	{19:1--19:5},
  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.2023.19},
  URN =		{urn:nbn:de:0030-drops-189144},
  doi =		{10.4230/LIPIcs.GIScience.2023.19},
  annote =	{Keywords: spatial optimisation, location set cover problem, multiple objective}
}
Document
Short Paper
Uncertainty Quantification in the Road-Level Traffic Risk Prediction by Spatial-Temporal Zero-Inflated Negative Binomial Graph Neural Network(STZINB-GNN) (Short Paper)

Authors: Xiaowei Gao, James Haworth, Dingyi Zhuang, Huanfa Chen, and Xinke Jiang

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


Abstract
Urban road-based risk prediction is a crucial yet challenging aspect of research in transportation safety. While most existing studies emphasize accurate prediction, they often overlook the importance of model uncertainty. In this paper, we introduce a novel Spatial-Temporal Zero-Inflated Negative Binomial Graph Neural Network (STZINB-GNN) for road-level traffic risk prediction, with a focus on uncertainty quantification. Our case study, conducted in the Lambeth borough of London, UK, demonstrates the superior performance of our approach in comparison to existing methods. Although the negative binomial distribution may not be the most suitable choice for handling real, non-binary risk levels, our work lays a solid foundation for future research exploring alternative distribution models or techniques. Ultimately, the STZINB-GNN contributes to enhanced transportation safety and data-driven decision-making in urban planning by providing a more accurate and reliable framework for road-level traffic risk prediction and uncertainty quantification.

Cite as

Xiaowei Gao, James Haworth, Dingyi Zhuang, Huanfa Chen, and Xinke Jiang. Uncertainty Quantification in the Road-Level Traffic Risk Prediction by Spatial-Temporal Zero-Inflated Negative Binomial Graph Neural Network(STZINB-GNN) (Short Paper). In 12th International Conference on Geographic Information Science (GIScience 2023). Leibniz International Proceedings in Informatics (LIPIcs), Volume 277, pp. 33:1-33:6, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2023)


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@InProceedings{gao_et_al:LIPIcs.GIScience.2023.33,
  author =	{Gao, Xiaowei and Haworth, James and Zhuang, Dingyi and Chen, Huanfa and Jiang, Xinke},
  title =	{{Uncertainty Quantification in the Road-Level Traffic Risk Prediction by Spatial-Temporal Zero-Inflated Negative Binomial Graph Neural Network(STZINB-GNN)}},
  booktitle =	{12th International Conference on Geographic Information Science (GIScience 2023)},
  pages =	{33:1--33:6},
  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.2023.33},
  URN =		{urn:nbn:de:0030-drops-189286},
  doi =		{10.4230/LIPIcs.GIScience.2023.33},
  annote =	{Keywords: Traffic Risk Prediction, Uncertainty Quantification, Zero-Inflated Issues, Road Safety}
}
Document
Short Paper
Exascale Agent-Based Modelling for Policy Evaluation in Real-Time (ExAMPLER) (Short Paper)

Authors: Alison Heppenstall, J. Gary Polhill, Mike Batty, Matt Hare, Doug Salt, and Richard Milton

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


Abstract
Exascale computing can potentially revolutionise the way in which we design and build agent-based models (ABM) through, for example, enabling scaling up, as well as robust calibration and validation. At present, there is no exascale computing operating with ABM (that we are aware of), but pockets of work using High Performance Computing (HPC). While exascale computing is expected to become more widely available towards the latter half of this decade, the ABM community is largely unaware of the requirements for exascale computing for agent-based modelling to support policy evaluation. This project will engage with the ABM community to understand what computing resources are currently used, what we need (both in terms of hardware and software) and to set out a roadmap by which to make it happen.

Cite as

Alison Heppenstall, J. Gary Polhill, Mike Batty, Matt Hare, Doug Salt, and Richard Milton. Exascale Agent-Based Modelling for Policy Evaluation in Real-Time (ExAMPLER) (Short Paper). In 12th International Conference on Geographic Information Science (GIScience 2023). Leibniz International Proceedings in Informatics (LIPIcs), Volume 277, pp. 38:1-38:5, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2023)


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@InProceedings{heppenstall_et_al:LIPIcs.GIScience.2023.38,
  author =	{Heppenstall, Alison and Polhill, J. Gary and Batty, Mike and Hare, Matt and Salt, Doug and Milton, Richard},
  title =	{{Exascale Agent-Based Modelling for Policy Evaluation in Real-Time (ExAMPLER)}},
  booktitle =	{12th International Conference on Geographic Information Science (GIScience 2023)},
  pages =	{38:1--38:5},
  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.2023.38},
  URN =		{urn:nbn:de:0030-drops-189334},
  doi =		{10.4230/LIPIcs.GIScience.2023.38},
  annote =	{Keywords: Exascale computing, Agent-Based Modelling, Policy evaluation}
}
Document
Short Paper
Agent-Based Modelling and Disease: Demonstrating the Role of Human Remains in Epidemic Outbreaks (Short Paper)

Authors: Huixin Liu and Sarah Wise

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


Abstract
Hemorrhagic fever viruses present a high risk to humans, given their associated high fatality rates, extensive care requirements, and few relevant vaccines. One of the most famous such viruses is the Ebola virus, which first came to international attention during an outbreak in 1976. Another is Marburg virus, cases of which are being reported in Equatorial Guinea at the time of writing. Researchers and governments all over the world share a goal in seeking effective ways to reduce or prevent the influence or spreading of such diseases. This study introduces a prototype agent-based model to explore the epidemic infectious progression of a simulated fever virus. More specifically, this work seeks to recreate the role of human remains in the progression of such an epidemic, and to help gauge the influence of different environmental conditions on this dynamic.

Cite as

Huixin Liu and Sarah Wise. Agent-Based Modelling and Disease: Demonstrating the Role of Human Remains in Epidemic Outbreaks (Short Paper). In 12th International Conference on Geographic Information Science (GIScience 2023). Leibniz International Proceedings in Informatics (LIPIcs), Volume 277, pp. 48:1-48:7, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2023)


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@InProceedings{liu_et_al:LIPIcs.GIScience.2023.48,
  author =	{Liu, Huixin and Wise, Sarah},
  title =	{{Agent-Based Modelling and Disease: Demonstrating the Role of Human Remains in Epidemic Outbreaks}},
  booktitle =	{12th International Conference on Geographic Information Science (GIScience 2023)},
  pages =	{48:1--48:7},
  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-dev.dagstuhl.de/entities/document/10.4230/LIPIcs.GIScience.2023.48},
  URN =		{urn:nbn:de:0030-drops-189435},
  doi =		{10.4230/LIPIcs.GIScience.2023.48},
  annote =	{Keywords: Disease modelling, agent-based model, hemorrhagic fever virus, epidemiology, safe burial practices}
}
Document
Short Paper
Calibration in a Data Sparse Environment: How Many Cases Did We Miss? (Short Paper)

Authors: Robert Manning Smith, Sarah Wise, and Sophie Ayling

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


Abstract
Reported case numbers in the COVID-19 pandemic are assumed in many countries to have underestimated the true prevalence of the disease. Deficits in reporting may have been particularly great in countries with limited testing capability and restrictive testing policies. Simultaneously, some models have been accused of over-reporting the scale of the pandemic. At a time when modeling consortia around the world are turning to the lessons learnt from pandemic modelling, we present an example of simulating testing as well as the spread of disease. In particular, we factor in the amount and nature of testing that was carried out in the first wave of the COVID-19 pandemic (March - September 2020), calibrating our spatial Agent Based Model (ABM) model to the reported case numbers in Zimbabwe.

Cite as

Robert Manning Smith, Sarah Wise, and Sophie Ayling. Calibration in a Data Sparse Environment: How Many Cases Did We Miss? (Short Paper). In 12th International Conference on Geographic Information Science (GIScience 2023). Leibniz International Proceedings in Informatics (LIPIcs), Volume 277, pp. 50:1-50:7, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2023)


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@InProceedings{manningsmith_et_al:LIPIcs.GIScience.2023.50,
  author =	{Manning Smith, Robert and Wise, Sarah and Ayling, Sophie},
  title =	{{Calibration in a Data Sparse Environment: How Many Cases Did We Miss?}},
  booktitle =	{12th International Conference on Geographic Information Science (GIScience 2023)},
  pages =	{50:1--50:7},
  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-dev.dagstuhl.de/entities/document/10.4230/LIPIcs.GIScience.2023.50},
  URN =		{urn:nbn:de:0030-drops-189452},
  doi =		{10.4230/LIPIcs.GIScience.2023.50},
  annote =	{Keywords: Agent Based Modelling, Infectious Disease Modelling, COVID-19, Zimbabwe, SARS-CoV-2, calibration}
}
Document
Use of Self Organizing Maps in Technique Analysis

Authors: Roger Bartlett, Peter Lamb, and Anthony Robbins

Published in: Dagstuhl Seminar Proceedings, Volume 8372, Computer Science in Sport - Mission and Methods (2008)


Abstract
This study looked at the coordination patterns of four participants performing three different basketball shots from different distances. The shots selected were the three-point shot, the free throw shot and the hook shot; the latter was included to encourage a phase transition between shots. We hypothesised lower variability between the three-point and free throw shots compared to the hook shot. The study uses Self-Organizing Maps (SOM) to expose the non-linearity of the movement and to try to explain more specifically what it is about the coordination patterns that make them different or similar. The SOM proved to draw the researcher's attention to aspects of the movement that were not obvious from a visual analysis of the original movement either viewed from video or as computer animation. A speculative link between the observational learning literature on the importance of the kinematics of distal segments in skill acquisition and the visual information a coach or analyst may rely on for qualitative technique analysis was made. Although making the distinction between the three shooting conditions was meant to be a trivial exercise, in many cases for this dataset the SOM output and the natural inclination of the movement analyst did not agree: the SOM may provide a more objective method for explaining movement patterning.

Cite as

Roger Bartlett, Peter Lamb, and Anthony Robbins. Use of Self Organizing Maps in Technique Analysis. In Computer Science in Sport - Mission and Methods. Dagstuhl Seminar Proceedings, Volume 8372, pp. 1-8, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2008)


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@InProceedings{bartlett_et_al:DagSemProc.08372.8,
  author =	{Bartlett, Roger and Lamb, Peter and Robbins, Anthony},
  title =	{{Use of Self Organizing Maps in Technique Analysis}},
  booktitle =	{Computer Science in Sport - Mission and Methods},
  pages =	{1--8},
  series =	{Dagstuhl Seminar Proceedings (DagSemProc)},
  ISSN =	{1862-4405},
  year =	{2008},
  volume =	{8372},
  editor =	{Arnold Baca and Martin Lames and Keith Lyons and Bernhard Nebel and Josef Wiemeyer},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops-dev.dagstuhl.de/entities/document/10.4230/DagSemProc.08372.8},
  URN =		{urn:nbn:de:0030-drops-17738},
  doi =		{10.4230/DagSemProc.08372.8},
  annote =	{Keywords: Artificial neural networks, basketball shooting, movement coordination, movement variability, self-organizing maps.}
}
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