Akademik Veri Yönetim Sistemi
ACM Transactions on Spatial Algorithms and Systems, cilt.10, sa.2, 2024 (ESCI)
Successfully tackling many urgent challenges in socio-economically critical domains, such as public health and sustainability, requires a deeper understanding of causal relationships and interactions among a diverse spectrum of spatio-Temporally distributed entities. In these applications, the ability to leverage spatio-Temporal data to obtain causally based situational awareness and to develop informed forecasts to provide resilience at different scales is critical. While the promise of a causally grounded approach to these challenges is apparent, the core data technologies needed to achieve these are in the early stages and lack a framework to help realize their potential. In this article, we argue that there is an urgent need for a novel paradigm of spatio-causal research built on computational advances in spatio-Temporal data and model integration, causal learning and discovery, large scale data-and model-driven simulations, emulations, and forecasting, as well as spatio-Temporal data-driven and model-centric operational recommendations, and effective causally driven visualization and explanation. We thus provide a vision, and a road map, for spatio-causal situation awareness, forecasting, and planning.