License: Creative Commons Attribution 4.0 International license (CC BY 4.0)
When quoting this document, please refer to the following
DOI: 10.4230/LIPIcs.GIScience.2023.60
URN: urn:nbn:de:0030-drops-189559
URL: http://dagstuhl.sunsite.rwth-aachen.de/volltexte/2023/18955/
Go to the corresponding LIPIcs Volume Portal

Polous, Nina

Toward Causally Aware GIS: Events as Cornerstones (Short Paper)

pdf-format:
LIPIcs-GIScience-2023-60.pdf (0.6 MB)

Abstract

Over the last 50 years, Geographic Information Systems (GIS) have become a vital tool for decision-making. Yet, the increasing volume and complexity of geographical data pose challenges for real-time integration and analysis. To address these, we suggest a causally aware GIS that represents causal relationships. This system uses causality to analyze events and geographical impacts, aiming to offer a more comprehensive understanding of the geographic world. It integrates causality into design and operations, applying robust algorithms and visualization tools for scenario analysis. Unlike traditional GIS, our approach prioritizes an event-based model, emphasizing change as the core concept. This model moves beyond object-oriented models' limitations by considering events as primary entities. The proposed system adopts an event-oriented approach within a Spatio-Temporal Information System, with objects in space and time viewed as event components linked through processes. We introduce an innovative event-based ontology model that enriches GIS by focusing on modeling changes and their interconnections. Lastly, we suggest an IT implementation of this ontology to enhance GIS capabilities further.

BibTeX - Entry

@InProceedings{polous:LIPIcs.GIScience.2023.60,
  author =	{Polous, Nina},
  title =	{{Toward Causally Aware GIS: Events as Cornerstones}},
  booktitle =	{12th International Conference on Geographic Information Science (GIScience 2023)},
  pages =	{60:1--60:8},
  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/opus/volltexte/2023/18955},
  URN =		{urn:nbn:de:0030-drops-189559},
  doi =		{10.4230/LIPIcs.GIScience.2023.60},
  annote =	{Keywords: Causal Aware GIS, Events, Event-Oriented GIS, Causality}
}

Keywords: Causal Aware GIS, Events, Event-Oriented GIS, Causality
Collection: 12th International Conference on Geographic Information Science (GIScience 2023)
Issue Date: 2023
Date of publication: 07.09.2023

DROPS-Home | Fulltext Search | Imprint | Privacy Published by LZI