| id |
caadria2020_240 |
| authors |
Stojanovic, Djordje and Vujovic, Milica |
| year |
2020 |
| title |
How to Share a Home - Towards Predictive Analysis for Innovative Housing Solutions |
| doi |
https://doi.org/10.52842/conf.caadria.2020.1.547
|
| source |
D. Holzer, W. Nakapan, A. Globa, I. Koh (eds.), RE: Anthropocene, Design in the Age of Humans - Proceedings of the 25th CAADRIA Conference - Volume 1, Chulalongkorn University, Bangkok, Thailand, 5-6 August 2020, pp. 547-556 |
| summary |
Renewed interest in cohousing solutions is driven by the rapid population growth and a lack of affordable housing in many cities across the world. The home share has become more prevalent in recent years due to the cost benefits and social gains it provides. While it involves challenges primarily concerned with the usage of communal areas, the viability of this housing model increases with the advancement of technology enabling new tools for analysis and optimisation of spatial usage. This paper introduces a method of sensor application in the occupancy analysis to provide grounding for future studies and the implementation of advanced computational methods. The study focuses on the underexplored potential of the communal spaces and provides a method for the measuring of specific aspects of their usage. The study applies principles of mathematical set theory, to give a more conclusive understanding of how communal areas are used, and therefore contributes to the improvement of housing design. Presented outcomes include an algorithmic chart and a blueprint of a behavioural model. |
| keywords |
Cohousing; Housing share; Post Occupancy Evaluation; Machine Learning ; Predictive Analysis |
| series |
CAADRIA |
| email |
|
| full text |
 file.pdf (2,486,892 bytes) |
| references |
Content-type: text/plain
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Aschwanden, G (2016)
 Neighbourhood Detection with Analytical Tools
, Proceedings of CAADRIA 2016, pp. 13-22
|
|
|
|
|
Bhatt, M, Suchan, J, Schultz, C, Kondyli, V and Goyal, S (2016)
Artificial intelligence for predictive and evidence-based architecture design
, Thirtieth AAAI Conference on Artificial Intelligence, Phoenix, Arizona, pp. 4339-4350
|
|
|
|
|
Cali, D, Matthes, P, Huchtemann, K, Streblow, R and Müller, D (2015)
CO2 based occupancy detection algorithm: Experimental analysis and validation for office and residential buildings
, Building and Environment, 86, pp. 39-49
|
|
|
|
|
Chang, WK and Hong, T (2013)
Statistical analysis and modelling of occupancy patterns in open-plan offices using measured lighting-switch data
, Building Simulation, 6, pp. 23-32
|
|
|
|
|
Davis, D (2016)
Evaluating Buildings with Computation and Machine Learning
, Proceedings OF ACADIA 209, pp. 116-123
|
|
|
|
|
Gomez-Zamora, P, Bafna, S, Zimring, C, Do, E and Vega, MR (2019)
Spatiotemporal Occupancy for Building Analytics
, Proceedings of eCAADe 2019, pp. 111-120
|
|
|
|
|
K, Hashimoto, Kawaguchi, C, Matsueda, S, Morinaka, K and Yoshiike, N (1998)
People-counting system using multisession application
, Sensors and Actuators, 66(1-3), pp. 50-55
|
|
|
|
|
McCamant, K and Durrett, C (1994)
Cohousing: A Contemporary Approach to Housing Ourselves
, Ten Speed Press, Berkeley
|
|
|
|
|
Panagoulia, E (2017)
The role of Open Data in identifying and evaluating the Livability of Urban Space
, Proceedings of eCAADe 2017, Rome, pp. 495-504
|
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| last changed |
2022/06/07 07:56 |
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