Advancing spatial cognition analysis: Integrating agent-based modelling with space syntax in the NetLogo framework

This study aims to advance the application of Space Syntax by integrating it with Agent- Based System (ABM (if referring to Agent-Based Modeling)) features within the NetLogo environment. Building upon the foundational work done using UCL DepthMap, the research replicates Turner’s experiments at the Tate Britain to validate the results within this new framework, establishing a baseline using the original DepthMap outputs from Turner ad Penn. The methodology progresses by constructing a parallel simulation framework in NetLogo, initially confirming functional equivalence through integration diagrams before introducing key ABM (if referring to Agent-Based Modeling) elements—agent communication and dynamic attractors. Findings will show the proposed NetLogo framework is validated by the same correlation, 0.89, that Turner obtained along his research on Through-vision mathematical definition. Implemented Agent-based-system features, message passing and goals/attractors, will show a clear influence on agents’ behavior as a priori guessed by Turner in 2011 along his evolved automaton proposal.