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CumInCAD is a Cumulative Index about publications in Computer Aided Architectural Design
supported by the sibling associations ACADIA, CAADRIA, eCAADe, SIGraDi, ASCAAD and CAAD futures

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_id	acadia22pr_64
id	acadia22pr_64
authors	Davis, Michael; Hurley, Daniel; Lawrence, Ben; Liu, Yinan; Print, Cristin; Rieger, Uwe; Robb, Tamsin; Windahl, Charlotta; Woodhouse, Braden
year	2022
title	XR Tumor Evolution Project - A Hybrid Architectural Space for Cancer Research
source	ACADIA 2022: Hybrids and Haecceities [Projects Catalog of the 42nd Annual Conference of the Association of Computer Aided Design in Architecture (ACADIA) ISBN 979-8-9860805-7-4]. University of Pennsylvania Stuart Weitzman School of Design. 27-29 October 2022. edited by M. Akbarzadeh, D. Aviv, H. Jamelle, and R. Stuart-Smith. 64-69.
summary	The Extended Reality Tumor Evolution Project (XRTEP) is a unique, real-world application of extended reality technology in cancer research. It is enabled by a rare inter-disciplinary collaboration between the School of Architecture and Planning, the Faculty of Medical and Health Sciences, and the Centre for E-Research at the University of Auckland
series	ACADIA
type	project
email
last changed	2024/02/06 14:04

_id	caadria2022_458
id	caadria2022_458
authors	Gong, Pixin, Huang, Xiaoran, Huang, Chenyu and White, Marcus
year	2022
title	Machine Learning-Based Walkability Modeling in Urban Life Circle
source	Jeroen van Ameijde, Nicole Gardner, Kyung Hoon Hyun, Dan Luo, Urvi Sheth (eds.), POST-CARBON - Proceedings of the 27th CAADRIA Conference, Sydney, 9-15 April 2022, pp. 645-654
doi	https://doi.org/10.52842/conf.caadria.2022.1.645
summary	With China's fast urbanization, the study of the walkability of residents' life circles has become critical to improve people's quality of life. Traditional walkability calculations are based on Lawrence Frank's theory. However, the weighted calculation method cannot be adapted to ever-changing and complicated scenarios as the scope and topic of research transforming. This study investigated walkability at the community level by combining machine learning techniques with multi-source data. Feature indicators affecting walkability were estimated from multi-source data. Machine learning was used to refine the weighting calculation under the previous indicator framework. We compared the performance of 20 regression models from 6 different machine learning algorithms for estimating the walkability of 14578 communities in downtown Shanghai. It is concluded that the Bagged Tree Model (R2=0.86, RMSE=0.36862) achieves the best performance, which is used to revise the initial walkability index values. The workflow proposed in this paper allows for rapid application of expert empirical consensus to comprehensive urban design and detailed urban governance in the future.
keywords	Life Circle, Walkability Indicator, Multi-source Data, Machine Learning, Refined Urban Design, SDG 3, SDG 10, SDG 11
series	CAADRIA
email
last changed	2022/07/22 07:34

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