2026-07-26T02:54:53Z https://drops.dagstuhl.de/oai

oai:drops-oai.dagstuhl.de:18955 2024-03-06T11:03:03Z ddc:004 open_access

Toward Causally Aware GIS: Events as Cornerstones (Short Paper) Polous, Nina Causal Aware GIS Events Event-Oriented GIS Causality 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. Schloss Dagstuhl – Leibniz-Zentrum für Informatik Nina Polous 2023 Is Part Of LIPIcs, Volume 277, 12th International Conference on Geographic Information Science (GIScience 2023) InProceedings Text doc-type:ResearchArticle publishedVersion application/pdf doi:10.4230/LIPIcs.GIScience.2023.60 urn:nbn:de:0030-drops-189559 https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.GIScience.2023.60 eng https://creativecommons.org/licenses/by/4.0/legalcode