| id |
ecaade2024_191 |
| authors |
Chaskopoulou, Margarita; Varoudis, Tasos |
| year |
2024 |
| title |
Reversing Urban Food Deserts: Data-driven adaptive food networks for urban resilience |
| 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. 97–106 |
| doi |
https://doi.org/10.52842/conf.ecaade.2024.2.097
|
| summary |
Dense urbanization highlights the need to explore metabolic processes and mechanisms for developing resilient and adaptive solutions to ecological challenges. The recent pandemic intensified the pressure to re-evaluate the existing urban foodscapes by revealing disparities in food accessibility. Studies indicate that food deserts are present even in the centre of metropoles, bringing forth the question of the relation between food, segregation and urban morphology. This research introduces a Machine Learning-assisted computational tool that evaluates food networks and identifies optimal new spatial configurations based on curated data analytics, unsupervised machine learning models and space syntax. Its primary focus is the creation of a unified model connecting urban morphology, socioeconomic and temporal data. The output provides the planners and local authorities with a set of possible intervention patterns for food-related functions aiming to assist decision-making processes. |
| keywords |
Computational Design, Machine Learning, Urban Analytics, Food Accessibility, Design Tool |
| series |
eCAADe |
| email |
|
| full text |
 file.pdf (3,036,762 bytes) |
| references |
Content-type: text/plain
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| last changed |
2024/11/17 22:05 |
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