105 Search Results for "Wise, Sarah"

Volume

LIPIcs, Volume 277

12th International Conference on Geographic Information Science (GIScience 2023)

GIScience 2023, September 12-15, 2023, Leeds, UK

Editors: Roger Beecham, Jed A. Long, Dianna Smith, Qunshan Zhao, and Sarah Wise

Document
DOI: 10.4230/LIPIcs.ITCS.2026.98
Weighted Chairman Assignment and Flow-Time Scheduling

Authors: Siyue Liu and Victor Reis

Published in: LIPIcs, Volume 362, 17th Innovations in Theoretical Computer Science Conference (ITCS 2026)

Abstract
Given positive integers m, n, a fractional assignment x ∈ [0,1]^{m × n} and weights d ∈ ℝⁿ_{> 0}, we show that there exists an assignment y ∈ {0,1}^{m × n} so that for every i ∈ [m] and t ∈ [n], |∑_{j ∈ [t]} d_j (x_{ij} - y_{ij})| < max_{j ∈ [n]} d_j. This generalizes a result of Tijdeman (1973) on the unweighted version, known as the chairman assignment problem. This also confirms a special case of the single-source unsplittable flow conjecture with arc-wise lower and upper bounds due to Morell and Skutella (IPCO 2020). As an application, we consider a scheduling problem where jobs have release times and machines have closing times, and a job can only be scheduled on a machine if it is released before the machine closes. We give a 3-approximation algorithm for maximum flow-time minimization.
Cite as

Siyue Liu and Victor Reis. Weighted Chairman Assignment and Flow-Time Scheduling. In 17th Innovations in Theoretical Computer Science Conference (ITCS 2026). Leibniz International Proceedings in Informatics (LIPIcs), Volume 362, pp. 98:1-98:15, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2026)

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@InProceedings{liu_et_al:LIPIcs.ITCS.2026.98,
  author =	{Liu, Siyue and Reis, Victor},
  title =	{{Weighted Chairman Assignment and Flow-Time Scheduling}},
  booktitle =	{17th Innovations in Theoretical Computer Science Conference (ITCS 2026)},
  pages =	{98:1--98:15},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-410-9},
  ISSN =	{1868-8969},
  year =	{2026},
  volume =	{362},
  editor =	{Saraf, Shubhangi},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.ITCS.2026.98},
  URN =		{urn:nbn:de:0030-drops-253858},
  doi =		{10.4230/LIPIcs.ITCS.2026.98},
  annote =	{Keywords: prefix discrepancy, flow-time scheduling, unsplittable flow}
}
Document
DOI: 10.4230/OASIcs.SpaceCHI.2025.33
Digital Health for Space: Towards Prevention, Training, Empowerment, and Autonomy

Authors: Mario A. Cypko, Ulrich Straube, Russell J. Andrews, and Oliver Amft

Published in: OASIcs, Volume 130, Advancing Human-Computer Interaction for Space Exploration (SpaceCHI 2025)

Abstract
Future long-duration and deep-space missions will rely on digital health technologies to ensure the health and safety of the crew, as well as to enable the required mission autonomy. This position paper redefines the current paradigms of digital health by emphasizing prevention, self-management, and individual empowerment for health as central challenges for both space and terrestrial medicine. We focus on future mission scenarios and highlight the potential of co-evolving digital health and related technologies, particularly sensing, artificial intelligence (AI), and human-computer interaction (HCI), across the continuum of space medicine: from astronaut selection and training to prevention, diagnostics, therapy, rehabilitation, and long-term care. Future digital health technologies can respond to pressing needs arising from limited medical infrastructure, rising care costs, and increasing demands on healthcare systems in space and on Earth. To structure research and development needs, we introduce a framework with four autonomy levels based on mission distance and communication latency (Earth orbit, Lunar Gateway and Moon vicinity, Mars, and deep space) that illustrate how mission context constrains medical support and dictates system requirements. Using the Lunar Orbital Platform-Gateway as a near-future reference, we discuss how growing communication delays demand greater onboard autonomy and new telemedical strategies. Within the proposed framework, we integrate solutions built around AI-supported decision making, multimodal monitoring, and adaptive HCI, which should be co-designed through human-centered methods to form a cohesive health management ecosystem. The framework opens up synergies for proactive and trustworthy health support under isolation and limited ground contact. The paper consolidates current technological readiness and strategic challenges, offering guidance for space health research and policy, with clear translational benefits for terrestrial care delivery.
Cite as

Mario A. Cypko, Ulrich Straube, Russell J. Andrews, and Oliver Amft. Digital Health for Space: Towards Prevention, Training, Empowerment, and Autonomy. In Advancing Human-Computer Interaction for Space Exploration (SpaceCHI 2025). Open Access Series in Informatics (OASIcs), Volume 130, pp. 33:1-33:12, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025)

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@InProceedings{cypko_et_al:OASIcs.SpaceCHI.2025.33,
  author =	{Cypko, Mario A. and Straube, Ulrich and Andrews, Russell J. and Amft, Oliver},
  title =	{{Digital Health for Space: Towards Prevention, Training, Empowerment, and Autonomy}},
  booktitle =	{Advancing Human-Computer Interaction for Space Exploration (SpaceCHI 2025)},
  pages =	{33:1--33:12},
  series =	{Open Access Series in Informatics (OASIcs)},
  ISBN =	{978-3-95977-384-3},
  ISSN =	{2190-6807},
  year =	{2025},
  volume =	{130},
  editor =	{Bensch, Leonie and Nilsson, Tommy and Nisser, Martin and Pataranutaporn, Pat and Schmidt, Albrecht and Sumini, Valentina},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/OASIcs.SpaceCHI.2025.33},
  URN =		{urn:nbn:de:0030-drops-240236},
  doi =		{10.4230/OASIcs.SpaceCHI.2025.33},
  annote =	{Keywords: Digital Health in Space, AI-based Decision Support, Wearable Health Monitoring, Human-Computer Interaction (HCI), Autonomous Medical Systems}
}
Document
DOI: 10.4230/LIPIcs.GIScience.2025.13
Assessing Map Reproducibility with Visual Question-Answering: An Empirical Evaluation

Authors: Eftychia Koukouraki, Auriol Degbelo, and Christian Kray

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

Abstract
Reproducibility is a key principle of the modern scientific method. Maps, as an important means of communicating scientific results in GIScience and across disciplines, should be reproducible. Currently, map reproducibility assessment is done manually, which makes the assessment process tedious and time-consuming, ultimately limiting its efficiency. Hence, this work explores the extent to which Visual Question-Answering (VQA) can be used to automate some tasks relevant to map reproducibility assessment. We selected five state-of-the-art vision language models (VLMs) and followed a three-step approach to evaluate their ability to discriminate between maps and other images, interpret map content, and compare two map images using VQA. Our results show that current VLMs already possess map-reading capabilities and demonstrate understanding of spatial concepts, such as cardinal directions, geographic scope, and legend interpretation. Our paper demonstrates the potential of using VQA to support reproducibility assessment and highlights the outstanding issues that need to be addressed to achieve accurate, trustworthy map descriptions, thereby reducing the time and effort required by human evaluators.
Cite as

Eftychia Koukouraki, Auriol Degbelo, and Christian Kray. Assessing Map Reproducibility with Visual Question-Answering: An Empirical Evaluation. In 13th International Conference on Geographic Information Science (GIScience 2025). Leibniz International Proceedings in Informatics (LIPIcs), Volume 346, pp. 13:1-13:12, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025)

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@InProceedings{koukouraki_et_al:LIPIcs.GIScience.2025.13,
  author =	{Koukouraki, Eftychia and Degbelo, Auriol and Kray, Christian},
  title =	{{Assessing Map Reproducibility with Visual Question-Answering: An Empirical Evaluation}},
  booktitle =	{13th International Conference on Geographic Information Science (GIScience 2025)},
  pages =	{13:1--13:12},
  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.2025.13},
  URN =		{urn:nbn:de:0030-drops-238426},
  doi =		{10.4230/LIPIcs.GIScience.2025.13},
  annote =	{Keywords: map comparison, computational reproducibility, visual question answering, large language models, GeoAI}
}
Document
DOI: 10.4230/LIPIcs.GIScience.2025.10
Geovicla: Automated Classification of Interactive Web-Based Geovisualizations

Authors: Phil Hüffer, Auriol Degbelo, and Benjamin Risse

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

Abstract
The exponential growth of interactive geovisualizations on the Web has underscored the need for automated techniques to enhance their findability. In this paper, we present the Geovicla dataset (2.5K instances), constructed through the harvesting and manual labelling of webpages from a broad range of domains. The webpages are categorized into three groups: "interactive visualisation", "interactive geovisualisation" and "`no interactive visualisation". Using this dataset, we compared three approaches for interactive (geo)visualization classification: (i) a heuristic-based approach (i.e. using manually derived rules), (ii) a feature-engineering approach (i.e. hand-crafted feature vectors combined with machine learning classifiers) and (iii) an embedding-based approach (i.e. automatically generated large language model (LLM) embeddings with machine learning classifiers). The results indicate that LLM embeddings, when used in conjunction with a multilayer perceptron, form a promising combination, achieving up to 74% accuracy for multiclass classification and 75% for binary classification. The dataset and the insights gained from our empirical comparison offer valuable resources for GIScience researchers aiming to enhance the discoverability of interactive geovisualizations.
Cite as

Phil Hüffer, Auriol Degbelo, and Benjamin Risse. Geovicla: Automated Classification of Interactive Web-Based Geovisualizations. In 13th International Conference on Geographic Information Science (GIScience 2025). Leibniz International Proceedings in Informatics (LIPIcs), Volume 346, pp. 10:1-10:12, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025)

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@InProceedings{huffer_et_al:LIPIcs.GIScience.2025.10,
  author =	{H\"{u}ffer, Phil and Degbelo, Auriol and Risse, Benjamin},
  title =	{{Geovicla: Automated Classification of Interactive Web-Based Geovisualizations}},
  booktitle =	{13th International Conference on Geographic Information Science (GIScience 2025)},
  pages =	{10:1--10:12},
  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.2025.10},
  URN =		{urn:nbn:de:0030-drops-238397},
  doi =		{10.4230/LIPIcs.GIScience.2025.10},
  annote =	{Keywords: spatial information search, geovisualization search, findable interactive geovisualization, webpage classification}
}
Document
DOI: 10.4230/LIPIcs.CP.2025.29
From Prediction to Action: A Constraint-Based Approach to Predictive Policing

Authors: Younes Mechqrane and Ismail Elabbassi

Published in: LIPIcs, Volume 340, 31st International Conference on Principles and Practice of Constraint Programming (CP 2025)

Abstract
Crime prevention in urban environments demands both accurate crime forecasting and the efficient deployment of limited law enforcement resources. In this paper, we present an integrated framework that combines a machine learning module (i.e. PredRNN++ [Wang et al., 2018]) for spatiotemporal crime prediction with a constraint programming module for patrol route optimization. Our approach operates within the ICON loop framework [Bessiere et al., 2017], facilitating iterative refinement of predictions and immediate adaptation of patrol strategies. We validate our method using the City of Chicago Crime Dataset. Experimental results show that routes informed by crime predictions significantly outperform strategies relying solely on historical patterns or operational constraints. These findings illustrate how coupling predictive analytics with constraint programming can substantially enhance resource allocation and overall crime deterrence.
Cite as

Younes Mechqrane and Ismail Elabbassi. From Prediction to Action: A Constraint-Based Approach to Predictive Policing. In 31st International Conference on Principles and Practice of Constraint Programming (CP 2025). Leibniz International Proceedings in Informatics (LIPIcs), Volume 340, pp. 29:1-29:18, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025)

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@InProceedings{mechqrane_et_al:LIPIcs.CP.2025.29,
  author =	{Mechqrane, Younes and Elabbassi, Ismail},
  title =	{{From Prediction to Action: A Constraint-Based Approach to Predictive Policing}},
  booktitle =	{31st International Conference on Principles and Practice of Constraint Programming (CP 2025)},
  pages =	{29:1--29:18},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-380-5},
  ISSN =	{1868-8969},
  year =	{2025},
  volume =	{340},
  editor =	{de la Banda, Maria Garcia},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.CP.2025.29},
  URN =		{urn:nbn:de:0030-drops-238902},
  doi =		{10.4230/LIPIcs.CP.2025.29},
  annote =	{Keywords: Inductive Constraint Programming (ICON) Loop, Next Frame Prediction, PredRNN++}
}
Document
Position
DOI: 10.4230/TGDK.2.1.2
Grounding Stream Reasoning Research

Authors: Pieter Bonte, Jean-Paul Calbimonte, Daniel de Leng, Daniele Dell'Aglio, Emanuele Della Valle, Thomas Eiter, Federico Giannini, Fredrik Heintz, Konstantin Schekotihin, Danh Le-Phuoc, Alessandra Mileo, Patrik Schneider, Riccardo Tommasini, Jacopo Urbani, and Giacomo Ziffer

Published in: TGDK, Volume 2, Issue 1 (2024): Special Issue on Trends in Graph Data and Knowledge - Part 2. Transactions on Graph Data and Knowledge, Volume 2, Issue 1

Abstract
In the last decade, there has been a growing interest in applying AI technologies to implement complex data analytics over data streams. To this end, researchers in various fields have been organising a yearly event called the "Stream Reasoning Workshop" to share perspectives, challenges, and experiences around this topic. In this paper, the previous organisers of the workshops and other community members provide a summary of the main research results that have been discussed during the first six editions of the event. These results can be categorised into four main research areas: The first is concerned with the technological challenges related to handling large data streams. The second area aims at adapting and extending existing semantic technologies to data streams. The third and fourth areas focus on how to implement reasoning techniques, either considering deductive or inductive techniques, to extract new and valuable knowledge from the data in the stream. This summary is written not only to provide a crystallisation of the field, but also to point out distinctive traits of the stream reasoning community. Moreover, it also provides a foundation for future research by enumerating a list of use cases and open challenges, to stimulate others to join this exciting research area.
Cite as

Pieter Bonte, Jean-Paul Calbimonte, Daniel de Leng, Daniele Dell'Aglio, Emanuele Della Valle, Thomas Eiter, Federico Giannini, Fredrik Heintz, Konstantin Schekotihin, Danh Le-Phuoc, Alessandra Mileo, Patrik Schneider, Riccardo Tommasini, Jacopo Urbani, and Giacomo Ziffer. Grounding Stream Reasoning Research. In Special Issue on Trends in Graph Data and Knowledge - Part 2. Transactions on Graph Data and Knowledge (TGDK), Volume 2, Issue 1, pp. 2:1-2:47, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2024)

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@Article{bonte_et_al:TGDK.2.1.2,
  author =	{Bonte, Pieter and Calbimonte, Jean-Paul and de Leng, Daniel and Dell'Aglio, Daniele and Della Valle, Emanuele and Eiter, Thomas and Giannini, Federico and Heintz, Fredrik and Schekotihin, Konstantin and Le-Phuoc, Danh and Mileo, Alessandra and Schneider, Patrik and Tommasini, Riccardo and Urbani, Jacopo and Ziffer, Giacomo},
  title =	{{Grounding Stream Reasoning Research}},
  journal =	{Transactions on Graph Data and Knowledge},
  pages =	{2:1--2:47},
  ISSN =	{2942-7517},
  year =	{2024},
  volume =	{2},
  number =	{1},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/TGDK.2.1.2},
  URN =		{urn:nbn:de:0030-drops-198597},
  doi =		{10.4230/TGDK.2.1.2},
  annote =	{Keywords: Stream Reasoning, Stream Processing, RDF streams, Streaming Linked Data, Continuous query processing, Temporal Logics, High-performance computing, Databases}
}
Document
Complete Volume
DOI: 10.4230/LIPIcs.GIScience.2023
LIPIcs, Volume 277, GIScience 2023, Complete Volume

Authors: Roger Beecham, Jed A. Long, Dianna Smith, Qunshan Zhao, and Sarah Wise

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

Abstract
LIPIcs, Volume 277, GIScience 2023, Complete Volume
Cite as

12th International Conference on Geographic Information Science (GIScience 2023). Leibniz International Proceedings in Informatics (LIPIcs), Volume 277, pp. 1-740, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2023)

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@Proceedings{beecham_et_al:LIPIcs.GIScience.2023,
  title =	{{LIPIcs, Volume 277, GIScience 2023, Complete Volume}},
  booktitle =	{12th International Conference on Geographic Information Science (GIScience 2023)},
  pages =	{1--740},
  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},
  URN =		{urn:nbn:de:0030-drops-188945},
  doi =		{10.4230/LIPIcs.GIScience.2023},
  annote =	{Keywords: LIPIcs, Volume 277, GIScience 2023, Complete Volume}
}
Document
Front Matter
DOI: 10.4230/LIPIcs.GIScience.2023.0
Front Matter, Table of Contents, Preface, Conference Organization

Authors: Roger Beecham, Jed A. Long, Dianna Smith, Qunshan Zhao, and Sarah Wise

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

Abstract
Front Matter, Table of Contents, Preface, Conference Organization
Cite as

12th International Conference on Geographic Information Science (GIScience 2023). Leibniz International Proceedings in Informatics (LIPIcs), Volume 277, pp. 0:i-0:xxiv, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2023)

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@InProceedings{beecham_et_al:LIPIcs.GIScience.2023.0,
  author =	{Beecham, Roger and Long, Jed A. and Smith, Dianna and Zhao, Qunshan and Wise, Sarah},
  title =	{{Front Matter, Table of Contents, Preface, Conference Organization}},
  booktitle =	{12th International Conference on Geographic Information Science (GIScience 2023)},
  pages =	{0:i--0:xxiv},
  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.2023.0},
  URN =		{urn:nbn:de:0030-drops-188950},
  doi =		{10.4230/LIPIcs.GIScience.2023.0},
  annote =	{Keywords: Front Matter, Table of Contents, Preface, Conference Organization}
}
Document
DOI: 10.4230/LIPIcs.GIScience.2023.1
Do You Need Instructions Again? Predicting Wayfinding Instruction Demand

Authors: Negar Alinaghi, Tiffany C. K. Kwok, Peter Kiefer, and Ioannis Giannopoulos

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

Abstract
The demand for instructions during wayfinding, defined as the frequency of requesting instructions for each decision point, can be considered as an important indicator of the internal cognitive processes during wayfinding. This demand can be a consequence of the mental state of feeling lost, being uncertain, mind wandering, having difficulty following the route, etc. Therefore, it can be of great importance for theoretical cognitive studies on human perception of the environment. From an application perspective, this demand can be used as a measure of the effectiveness of the navigation assistance system. It is therefore worthwhile to be able to predict this demand and also to know what factors trigger it. This paper takes a step in this direction by reporting a successful prediction of instruction demand (accuracy of 78.4%) in a real-world wayfinding experiment with 45 participants, and interpreting the environmental, user, instructional, and gaze-related features that caused it.
Cite as

Negar Alinaghi, Tiffany C. K. Kwok, Peter Kiefer, and Ioannis Giannopoulos. Do You Need Instructions Again? Predicting Wayfinding Instruction Demand. In 12th International Conference on Geographic Information Science (GIScience 2023). Leibniz International Proceedings in Informatics (LIPIcs), Volume 277, pp. 1:1-1:16, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2023)

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@InProceedings{alinaghi_et_al:LIPIcs.GIScience.2023.1,
  author =	{Alinaghi, Negar and Kwok, Tiffany C. K. and Kiefer, Peter and Giannopoulos, Ioannis},
  title =	{{Do You Need Instructions Again? Predicting Wayfinding Instruction Demand}},
  booktitle =	{12th International Conference on Geographic Information Science (GIScience 2023)},
  pages =	{1:1--1:16},
  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.2023.1},
  URN =		{urn:nbn:de:0030-drops-188963},
  doi =		{10.4230/LIPIcs.GIScience.2023.1},
  annote =	{Keywords: Wayfinding, Navigation Instructions, Urban Computing, Gaze Analysis}
}
Document
DOI: 10.4230/LIPIcs.GIScience.2023.2
Transitions in Dynamic Point Labeling

Authors: Thomas Depian, Guangping Li, Martin Nöllenburg, and Jules Wulms

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

Abstract
The labeling of point features on a map is a well-studied topic. In a static setting, the goal is to find a non-overlapping label placement for (a subset of) point features. In a dynamic setting, the set of point features and their corresponding labels change, and the labeling has to adapt to such changes. To aid the user in tracking these changes, we can use morphs, here called transitions, to indicate how a labeling changes. Such transitions have not gained much attention yet, and we investigate different types of transitions for labelings of points, most notably consecutive transitions and simultaneous transitions. We give (tight) bounds on the number of overlaps that can occur during these transitions. When each label has a (non-negative) weight associated to it, and each overlap imposes a penalty proportional to the weight of the overlapping labels, we show that it is NP-complete to decide whether the penalty during a simultaneous transition has weight at most k. Finally, in a case study, we consider geotagged Twitter data on a map, by labeling points with rectangular labels showing tweets. We developed a prototype implementation to evaluate different transition styles in practice, measuring both number of overlaps and transition duration.
Cite as

Thomas Depian, Guangping Li, Martin Nöllenburg, and Jules Wulms. Transitions in Dynamic Point Labeling. In 12th International Conference on Geographic Information Science (GIScience 2023). Leibniz International Proceedings in Informatics (LIPIcs), Volume 277, pp. 2:1-2:19, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2023)

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@InProceedings{depian_et_al:LIPIcs.GIScience.2023.2,
  author =	{Depian, Thomas and Li, Guangping and N\"{o}llenburg, Martin and Wulms, Jules},
  title =	{{Transitions in Dynamic Point Labeling}},
  booktitle =	{12th International Conference on Geographic Information Science (GIScience 2023)},
  pages =	{2:1--2:19},
  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.2023.2},
  URN =		{urn:nbn:de:0030-drops-188971},
  doi =		{10.4230/LIPIcs.GIScience.2023.2},
  annote =	{Keywords: Dynamic labels, Label overlaps, Morphs, NP-completeness, Case study}
}
Document
DOI: 10.4230/LIPIcs.GIScience.2023.3
Reducing False Discoveries in Statistically-Significant Regional-Colocation Mining: A Summary of Results

Authors: Subhankar Ghosh, Jayant Gupta, Arun Sharma, Shuai An, and Shashi Shekhar

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

Abstract
Given a set S of spatial feature types, its feature instances, a study area, and a neighbor relationship, the goal is to find pairs <a region (r_{g}), a subset C of S> such that C is a statistically significant regional-colocation pattern in r_{g}. This problem is important for applications in various domains including ecology, economics, and sociology. The problem is computationally challenging due to the exponential number of regional colocation patterns and candidate regions. Previously, we proposed a miner [Subhankar et. al, 2022] that finds statistically significant regional colocation patterns. However, the numerous simultaneous statistical inferences raise the risk of false discoveries (also known as the multiple comparisons problem) and carry a high computational cost. We propose a novel algorithm, namely, multiple comparisons regional colocation miner (MultComp-RCM) which uses a Bonferroni correction. Theoretical analysis, experimental evaluation, and case study results show that the proposed method reduces both the false discovery rate and computational cost.
Cite as

Subhankar Ghosh, Jayant Gupta, Arun Sharma, Shuai An, and Shashi Shekhar. Reducing False Discoveries in Statistically-Significant Regional-Colocation Mining: A Summary of Results. In 12th International Conference on Geographic Information Science (GIScience 2023). Leibniz International Proceedings in Informatics (LIPIcs), Volume 277, pp. 3:1-3:18, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2023)

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@InProceedings{ghosh_et_al:LIPIcs.GIScience.2023.3,
  author =	{Ghosh, Subhankar and Gupta, Jayant and Sharma, Arun and An, Shuai and Shekhar, Shashi},
  title =	{{Reducing False Discoveries in Statistically-Significant Regional-Colocation Mining: A Summary of Results}},
  booktitle =	{12th International Conference on Geographic Information Science (GIScience 2023)},
  pages =	{3:1--3:18},
  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.2023.3},
  URN =		{urn:nbn:de:0030-drops-188985},
  doi =		{10.4230/LIPIcs.GIScience.2023.3},
  annote =	{Keywords: Colocation pattern, Participation index, Multiple comparisons problem, Spatial heterogeneity, Statistical significance}
}
Document
DOI: 10.4230/LIPIcs.GIScience.2023.4
Genetic Programming for Computationally Efficient Land Use Allocation Optimization

Authors: Moritz J. Hildemann, Alan T. Murray, and Judith A. Verstegen

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

Abstract
Land use allocation optimization is essential to identify ideal landscape compositions for the future. However, due to the solution encoding, standard land use allocation algorithms cannot cope with large land use allocation problems. Solutions are encoded as sequences of elements, in which each element represents a land unit or a group of land units. As a consequence, computation times increase with every additional land unit. We present an alternative solution encoding: functions describing a variable in space. Function encoding yields the potential to evolve solutions detached from individual land units and evolve fields representing the landscape as a single object. In this study, we use a genetic programming algorithm to evolve functions representing continuous fields, which we then map to nominal land use maps. We compare the scalability of the new approach with the scalability of two state-of-the-art algorithms with standard encoding. We perform the benchmark on one raster and one vector land use allocation problem with multiple objectives and constraints, with ten problem sizes each. The results prove that the run times increase exponentially with the problem size for standard encoding schemes, while the increase is linear with genetic programming. Genetic programming was up to 722 times faster than the benchmark algorithm. The improvement in computation time does not reduce the algorithm performance in finding optimal solutions; often, it even increases. We conclude that evolving functions enables more efficient land use allocation planning and yields much potential for other spatial optimization applications.
Cite as

Moritz J. Hildemann, Alan T. Murray, and Judith A. Verstegen. Genetic Programming for Computationally Efficient Land Use Allocation Optimization. In 12th International Conference on Geographic Information Science (GIScience 2023). Leibniz International Proceedings in Informatics (LIPIcs), Volume 277, pp. 4:1-4:15, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2023)

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@InProceedings{hildemann_et_al:LIPIcs.GIScience.2023.4,
  author =	{Hildemann, Moritz J. and Murray, Alan T. and Verstegen, Judith A.},
  title =	{{Genetic Programming for Computationally Efficient Land Use Allocation Optimization}},
  booktitle =	{12th International Conference on Geographic Information Science (GIScience 2023)},
  pages =	{4:1--4:15},
  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.2023.4},
  URN =		{urn:nbn:de:0030-drops-188996},
  doi =		{10.4230/LIPIcs.GIScience.2023.4},
  annote =	{Keywords: Land use planning, Spatial optimization, Solution encoding, Computation time reduction}
}
Document
DOI: 10.4230/LIPIcs.GIScience.2023.5
Visualizing Geophylogenies - Internal and External Labeling with Phylogenetic Tree Constraints

Authors: Jonathan Klawitter, Felix Klesen, Joris Y. Scholl, Thomas C. van Dijk, and Alexander Zaft

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

Abstract
A geophylogeny is a phylogenetic tree where each leaf (biological taxon) has an associated geographic location (site). To clearly visualize a geophylogeny, the tree is typically represented as a crossing-free drawing next to a map. The correspondence between the taxa and the sites is either shown with matching labels on the map (internal labeling) or with leaders that connect each site to the corresponding leaf of the tree (external labeling). In both cases, a good order of the leaves is paramount for understanding the association between sites and taxa. We define several quality measures for internal labeling and give an efficient algorithm for optimizing them. In contrast, minimizing the number of leader crossings in an external labeling is NP-hard. We show nonetheless that optimal solutions can be found in a matter of seconds on realistic instances using integer linear programming. Finally, we provide several efficient heuristic algorithms and experimentally show them to be near optimal on real-world and synthetic instances.
Cite as

Jonathan Klawitter, Felix Klesen, Joris Y. Scholl, Thomas C. van Dijk, and Alexander Zaft. Visualizing Geophylogenies - Internal and External Labeling with Phylogenetic Tree Constraints. In 12th International Conference on Geographic Information Science (GIScience 2023). Leibniz International Proceedings in Informatics (LIPIcs), Volume 277, pp. 5:1-5:16, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2023)

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@InProceedings{klawitter_et_al:LIPIcs.GIScience.2023.5,
  author =	{Klawitter, Jonathan and Klesen, Felix and Scholl, Joris Y. and van Dijk, Thomas C. and Zaft, Alexander},
  title =	{{Visualizing Geophylogenies - Internal and External Labeling with Phylogenetic Tree Constraints}},
  booktitle =	{12th International Conference on Geographic Information Science (GIScience 2023)},
  pages =	{5:1--5:16},
  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.2023.5},
  URN =		{urn:nbn:de:0030-drops-189004},
  doi =		{10.4230/LIPIcs.GIScience.2023.5},
  annote =	{Keywords: geophylogeny, boundary labeling, external labeling, algorithms}
}
Document
DOI: 10.4230/LIPIcs.GIScience.2023.6
Map Reproducibility in Geoscientific Publications: An Exploratory Study

Authors: Eftychia Koukouraki and Christian Kray

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

Abstract
Reproducibility is a core element of the scientific method. In the Geosciences, the insights derived from geodata are frequently communicated through maps, and the computational methods to create these maps vary in their ease of reproduction. In this paper, we present the results from a study where we tried to reproduce the maps included in geoscientific publications. Following a systematic approach, we collected 27 candidate papers and in four cases, we were able to successfully reproduce the maps they contained. We report on the approach we applied, the issues we encountered and the insights we gained while attempting to reproduce the maps. In addition, we provide an initial set of criteria to assess the success of a map reproduction attempt. We also propose some guidelines for improving map reproducibility in geoscientific publications. Our work sheds a light on the current state of map reproducibility in geoscientific papers and can benefit researchers interested in publishing maps in a more reproducible way.
Cite as

Eftychia Koukouraki and Christian Kray. Map Reproducibility in Geoscientific Publications: An Exploratory Study. In 12th International Conference on Geographic Information Science (GIScience 2023). Leibniz International Proceedings in Informatics (LIPIcs), Volume 277, pp. 6:1-6:16, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2023)

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@InProceedings{koukouraki_et_al:LIPIcs.GIScience.2023.6,
  author =	{Koukouraki, Eftychia and Kray, Christian},
  title =	{{Map Reproducibility in Geoscientific Publications: An Exploratory Study}},
  booktitle =	{12th International Conference on Geographic Information Science (GIScience 2023)},
  pages =	{6:1--6:16},
  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.2023.6},
  URN =		{urn:nbn:de:0030-drops-189015},
  doi =		{10.4230/LIPIcs.GIScience.2023.6},
  annote =	{Keywords: Reproducible Research, Reproduction Assessment, Map Making, Cartography}
}
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