3 Search Results for "Van de Weghe, Nico"

Document
DOI: 10.4230/LIPIcs.GIScience.2025.5
Analysis of Points of Interests Recommended for Leisure Walk Descriptions

Authors: Ehsan Hamzei, Thi Minh Hoai Bui, Martin Tomko, and Stephan Winter

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

Abstract
Leisure walking is a physical activity where locomotion through a natural or even urban environment is the goal in itself, e.g., in pursuit of health and wellbeing. In contrast to destination-oriented walks that are focused on navigation efficiency (i.e., shortest or simplest walk from source to destination), leisure walks emphasize experiencing the environment, engaging in activities, and discovering places that may be off route, or intermediate destinations en-route, summarily called points of interest (POIs). POIs are key for recommending leisure walks, yet a detailed analysis of POIs in the context of leisure walking is missing in the literature. This study extracts and annotates POIs of leisure walking recommendations available in WalkingMaps.com.au, creating an annotated dataset to address this research gap and provide a first analysis of leisure walking descriptions. We classify POIs using the verbal description provided in the dataset, match them with data available in OpenStreetMap (OSM), and compare the POIs with nearby alternatives in OSM. Our analysis reveals thematic and spatial patterns in POI selection, offering a machine learning approach to model POI choices for leisure walks. We further evaluate the availability of rich data in OSM for future automated leisure walking recommendation. This study contributes to automated systems for recommending leisure walks, tailoring suggestions based on available information in the spatial open data, and presents an annotated dataset to facilitate future research in this field.
Cite as

Ehsan Hamzei, Thi Minh Hoai Bui, Martin Tomko, and Stephan Winter. Analysis of Points of Interests Recommended for Leisure Walk Descriptions. In 13th International Conference on Geographic Information Science (GIScience 2025). Leibniz International Proceedings in Informatics (LIPIcs), Volume 346, pp. 5:1-5:16, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025)

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@InProceedings{hamzei_et_al:LIPIcs.GIScience.2025.5,
  author =	{Hamzei, Ehsan and Bui, Thi Minh Hoai and Tomko, Martin and Winter, Stephan},
  title =	{{Analysis of Points of Interests Recommended for Leisure Walk Descriptions}},
  booktitle =	{13th International Conference on Geographic Information Science (GIScience 2025)},
  pages =	{5:1--5:16},
  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.2025.5},
  URN =		{urn:nbn:de:0030-drops-238341},
  doi =		{10.4230/LIPIcs.GIScience.2025.5},
  annote =	{Keywords: leisure walks, points of interest, places, platial information}
}
Document
Short Paper
DOI: 10.4230/LIPIcs.COSIT.2022.16
Smart Crowd Management: The Data, the Users and the Solution (Short Paper)

Authors: Laure De Cock, Steven Verstockt, Christophe Vandeviver, and Nico Van de Weghe

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

Abstract
This research project is situated in the domain of smart crowd management, a domain that is gaining importance because of the challenges that arise from urbanization, but also the opportunities that come with smart cities. While our cities become more crowded every day, they also become smarter, for example by employing pedestrian tracking sensors. However, the datasets that are generated by these sensors do not allow smart crowd management yet, because they are sparse and not linked to the perception of the crowd. This research will tackle these issues in three steps. First, pedestrian counts will be estimated on streets that have no tracking data by use of deep learning and space syntax data. Next, the perception of crowdedness within the crowd will be linked to the objective pedestrian counts by conducting two user studies, and finally, the resulting subjective pedestrian counts will be used as weights for a routing algorithm. The last step has already been developed as a proof of concept. The routing algorithm, that uses partly simulated data and partly real-time tracking data, has been embedded in a webtool to show stakeholders the potential and goal of this innovative project.
Cite as

Laure De Cock, Steven Verstockt, Christophe Vandeviver, and Nico Van de Weghe. Smart Crowd Management: The Data, the Users and the Solution (Short Paper). In 15th International Conference on Spatial Information Theory (COSIT 2022). Leibniz International Proceedings in Informatics (LIPIcs), Volume 240, pp. 16:1-16:7, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2022)

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@InProceedings{decock_et_al:LIPIcs.COSIT.2022.16,
  author =	{De Cock, Laure and Verstockt, Steven and Vandeviver, Christophe and Van de Weghe, Nico},
  title =	{{Smart Crowd Management: The Data, the Users and the Solution}},
  booktitle =	{15th International Conference on Spatial Information Theory (COSIT 2022)},
  pages =	{16:1--16:7},
  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.2022.16},
  URN =		{urn:nbn:de:0030-drops-169013},
  doi =		{10.4230/LIPIcs.COSIT.2022.16},
  annote =	{Keywords: crowd tracking, crowd modeling, space syntax, deep learning, perception, routing}
}
Document
Short Paper
DOI: 10.4230/LIPIcs.GISCIENCE.2018.55
Novel Models for Multi-Scale Spatial and Temporal Analyses (Short Paper)

Authors: Yi Qiang, Barbara P. Buttenfield, Nina Lam, and Nico Van de Weghe

Published in: LIPIcs, Volume 114, 10th International Conference on Geographic Information Science (GIScience 2018)

Abstract
Multi-scale analysis for spatio-temporal data forms a fundamental challenge for many analytic systems. In geographic information systems, analysis and modeling at pre-defined spatial and temporal scales may miss critical relationships in other scales. Previous studies have investigated the uses of the triangle model as a multi-scale framework in analyzing temporal data. This article demonstrates the utilities of the triangle model and pyramid model for multi-scale spatial analysis through real-world analytical tasks and discusses the potential of developing a unified modeling framework that integrates the two models.
Cite as

Yi Qiang, Barbara P. Buttenfield, Nina Lam, and Nico Van de Weghe. Novel Models for Multi-Scale Spatial and Temporal Analyses (Short Paper). In 10th International Conference on Geographic Information Science (GIScience 2018). Leibniz International Proceedings in Informatics (LIPIcs), Volume 114, pp. 55:1-55:7, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2018)

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@InProceedings{qiang_et_al:LIPIcs.GISCIENCE.2018.55,
  author =	{Qiang, Yi and Buttenfield, Barbara P. and Lam, Nina and Van de Weghe, Nico},
  title =	{{Novel Models for Multi-Scale Spatial and Temporal Analyses}},
  booktitle =	{10th International Conference on Geographic Information Science (GIScience 2018)},
  pages =	{55:1--55:7},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-083-5},
  ISSN =	{1868-8969},
  year =	{2018},
  volume =	{114},
  editor =	{Winter, Stephan and Griffin, Amy and Sester, Monika},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.GISCIENCE.2018.55},
  URN =		{urn:nbn:de:0030-drops-93832},
  doi =		{10.4230/LIPIcs.GISCIENCE.2018.55},
  annote =	{Keywords: Triangle Model, Pyramid Model, multi-scale spatial and temporal analysis, GIS}
}
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