| id |
ecaade2024_46 |
| authors |
Talmor-Blaistain, Anat; Merhav, Maayan; Fisher-Gewirtzman, Dafna |
| year |
2024 |
| title |
Grid to Star Network Transformation: Developing a Topological Assessment and Transformation Model to Enhance Spatial Memory and Route Learning for Wayfinding |
| doi |
https://doi.org/10.52842/conf.ecaade.2024.2.329
|
| source |
Kontovourkis, O, Phocas, MC and Wurzer, G (eds.), Data-Driven Intelligence - Proceedings of the 42nd Conference on Education and Research in Computer Aided Architectural Design in Europe (eCAADe 2024), Nicosia, 11-13 September 2024, Volume 2, pp. 329–338 |
| summary |
Wayfinding is the cognitive process of determining and following a path from one location to another. During navigation, a route-learning process occurs in which individuals encode spatial information. Older populations and individuals with cognitive difficulties face challenges in spatial learning and navigating complex environments.
This study builds on Merhav and Fisher-Gewirtzman (2023), which suggests that star-shaped pedestrian paths, containing a distinct center through which all paths pass between origins and destinations, improve spatial memory and learning abilities for wayfinding compared to grid networks, benefiting all age groups.
The research aims to bridge the gap in the analysis of pedestrian network topological shapes by developing a quantitative analytical model to evaluate how close each network is to a grid-type or a star-type and potentially transform these networks, from a grid-type into star-type topology. The proposed model suggests a methodology for assessing and modifying network topologies through spatial manipulations. The model utilizes a combination of open-source components (such as Space Syntax axial analysis and the Galapagos optimization plugin) and combines novel computational tools (python code) to rank nodes in the network and identify networks where isolated areas were created during the optimization process. |
| keywords |
Spatial Computing, Spatial memory, Route learning, Wayfinding, Grid and star network’s Topological Shape, Space Syntax, analytical model |
| series |
eCAADe |
| email |
anatb@technion.ac.il |
| full text |
 file.pdf (6,116,566 bytes) |
| references |
Content-type: text/plain
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| last changed |
2024/11/17 22:05 |
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