Document Type

Paper

Start Date

15-10-2024 1:50 PM

End Date

15-10-2024 2:50 PM

Abstract

Our increasingly connected world is faced with complex socio-environmental problems (e.g., biodiversity loss, climate change, and food insecurity). Tackling these problems requires cross- disciplinary approaches that examine the problems based on synergistic spatial and system thinking. Spatial Agent-Based Models (SABMs) represent a powerful approach to understanding complex socio-environmental systems. However, research on SABMs and associated complex problem solving face grand challenges that must be overcome to effectively unleash the power of SABMs enabled by cyber-based geographic information science and systems (cyberGIS). This paper describes four such grand challenges —reproducibility, scalability, communication, and accessibility. Resolving these challenges will enable new spatial computing frontiers to model complex socio-environmental systems at unprecedented spatiotemporal scales for tackling associated real-world problems.

DOI

10.5703/1288284317804

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Oct 15th, 1:50 PM Oct 15th, 2:50 PM

Understanding Complex Socio-Environmental Systems with Spatial Agent-Based Models

Our increasingly connected world is faced with complex socio-environmental problems (e.g., biodiversity loss, climate change, and food insecurity). Tackling these problems requires cross- disciplinary approaches that examine the problems based on synergistic spatial and system thinking. Spatial Agent-Based Models (SABMs) represent a powerful approach to understanding complex socio-environmental systems. However, research on SABMs and associated complex problem solving face grand challenges that must be overcome to effectively unleash the power of SABMs enabled by cyber-based geographic information science and systems (cyberGIS). This paper describes four such grand challenges —reproducibility, scalability, communication, and accessibility. Resolving these challenges will enable new spatial computing frontiers to model complex socio-environmental systems at unprecedented spatiotemporal scales for tackling associated real-world problems.