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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	caadria2023_84
id	caadria2023_84
authors	Chen, Bowen, Lao, Pui Kuan, Dou, Zhiyi, Qiu, Wai-Shan and Luo, Dan
year	2023
title	Analyst Patterns of Influence Between a Commercial Distribution and Neighbourhood Dynamic in a Residential Neighbourhood
doi	https://doi.org/10.52842/conf.caadria.2023.1.525
source	Immanuel Koh, Dagmar Reinhardt, Mohammed Makki, Mona Khakhar, Nic Bao (eds.), HUMAN-CENTRIC - Proceedings of the 28th CAADRIA Conference, Ahmedabad, 18-24 March 2023, pp. 525–534
summary	The spatial distribution of urban commercial spaces significantly impacts the overall efficiency and vibrancy of adjacent neighbourhoods. As such, it is an important factor to consider during urban development. This study aims to examine the patterns of impact between commercial distributions and neighbourhood dynamics in a residential neighbourhood, based on the case study of a highly populated, thriving commercial, and culturally rich area situated in Mong Kok, Hong Kong. In this research, a series of numeric evaluations and statistical analyses of liveability and vibrancy metrics are presented, uncovering the tension created by existing commercial forms and local living patterns. This research started with multi-dimensional data mining, such as accessing planning data using Geographic Information Systems (GIS), perception data using Street View Images (SVI), and business performance data from Google; secondly, analysing the data via machine learning (ML) algorithm and statistical correlation to identify correlations overlaid with a mapping of spaces of measurable characteristics. The goal is to establish a measurable evaluation of the relationship between commercial vibrance and urban features that can further inform the impact of urban design strategies on fostering the vitality of community commercial centres.
keywords	Mong Kok, Model Learning Machine (ML), SVM, PSPnet, MaskRCNN, POI, Commercial vibrance, Heatmap correlation, Visualization, QGIS, Google Maps Information
series	CAADRIA
email
last changed	2023/06/15 23:14

_id	architectural_intelligence2023_1
id	architectural_intelligence2023_1
authors	Qiwei Song, Zhiyi Dou, Waishan Qiu, Wenjing Li, Jingsong Wang, Jeroen van Ameijde & Dan Luo
year	2023
title	The evaluation of urban spatial quality and utility trade-offs for Post-COVID working preferences: a case study of Hong Kong
doi	https://doi.org/https://doi.org/10.1007/s44223-022-00020-x
source	Architectural Intelligence Journal
summary	The formation of urban districts and the appeal of densely populated areas reflect a spatial equilibrium in which workers migrate to locations with greater urban vitality but diminished environmental qualities. However, the pandemic and associated health concerns have accelerated remote and hybrid work modes, altered people's sense of place and appreciation of urban density, and transformed perceptions of desirable places to live and work. This study presents a systematic method for evaluating the trade-offs between perceived urban environmental qualities and urban amenities by analysing post-pandemic urban residence preferences. By evaluating neighbourhood Street View Imagery (SVI) and urban amenity data, such as park sizes, the study collects subjective opinions from surveys on two working conditions (work-from-office or from-home). On this basis, several Machine Learning (ML) models were trained to predict the preference scores for both work modes. In light of the complexity of work-from-home preferences, the results demonstrate that the method predicts work-from-office scores with greater precision. In the post-pandemic era, the research aims to shed light on the development of a valuable instrument for driving and evaluating urban design strategies based on the potential self-organisation of work-life patterns and social profiles in designated neighbourhoods.
series	Architectural Intelligence
email
last changed	2025/01/09 15:00

_id	cdrf2022_223
id	cdrf2022_223
authors	Zhiyi Dou, Waishan Qiu, Wenjing Li, Dan Luo
year	2022
title	Evaluation Process of Urban Spatial Quality and Utility Trade-Off for Post-COVID Working Preferences
doi	https://doi.org/https://doi.org/10.1007/978-981-19-8637-6_19
source	Proceedings of the 2022 DigitalFUTURES The 4st International Conference on Computational Design and Robotic Fabrication (CDRF 2022)
summary	The formation of cities, and the relocation of workers to densely populated areas reflect a spatial equilibrium, in which the higher real consumption levels of urban areas are offset by lower non-monetary amenities [1]. However, as the society progress toward a post-COVID stage, the prevailing decentralized delivery systems and location-based services, the growing trend of working from home, with citizens’ shifting preference of de-appreciating densities and gathering, have not only changed the possible spatial distribution of opportunities, resources, consumption and amenities, but also transformed people’s preference regarding desirable urban spatial qualities, value of amenities, and working opportunities [2, 3]. This research presents a systematic method to evaluate the perceived trade-off between urban spatial qualities and urban utilities such as amenities, transportation, and monetary opportunities by urban residence in the post-COVID society. The outcome of the research will become a valid tool to drive and evaluate urban design strategies based on the potential self-organization of work-life patterns and social profiles in the designated neighbourhood. To evaluate the subjective perception of the urban residence, the study started with a comparative survey by asking residence to compare two randomly selected urban contexts in a data base of 398 contexts sampled across Hong Kong and state their living preference under the presumption of following scenarios: 1. working from home; 2. working in city centre offices. Core information influencing the spatial equilibrium are provided in the comparable urban context such as street views, housing price, housing space, travel time to city centre, adjacency to public transport and amenities, etc. Each context is given a preference score calculated with Microsoft TrueSkill Bayesian ranking algorithm [4] based on the comparison survey of two scenarios. The 398 contexts are further analysed via GIS and image processing, to be deconstructed into numerical values describing main features for each of the context that influence urban design strategies such as composition of spatial features, amenity allocation, adjacency to city centre and public transportations. Machine learning models are trained with the numerical values of urban features as input and two preference scores for the two working scenarios as the output. The correlation heat maps are used to identify main urban features and its p-value that influence residence’s preference under two working scenarios in post–COVID era. The same model could also be applied to inform the direction of urban design strategies to construct a sustainable community for each type of working population and validate the design strategies via predicting its competitiveness in attracting residence and developing target industries.
series	cdrf
email
last changed	2024/05/29 14:02

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