Predicting Window View Preferences Using the Environmental Information Criteria

ABSTRACT

Daylighting standards provide an assessment method that can be used to evaluate the quality of window views. As part of this evaluation process, designers must achieve five environmental information criteria (location, time, weather, nature, and people) to obtain an excellent view. To the best of our knowledge, these criteria have not yet been verified and their scientific validity remains conjectural. In a two-stage experiment, a total of 451 persons evaluated six window view images. Using machine learning models, we found that the five criteria could provide accurate predictions for window view preferences. When one view was largely preferred over the other, the accuracy of decision tree models ranged from 83% to 90%. For smaller differences in preference, the accuracy was 67%. As ratings given to the five criteria increased, so did evaluations for psychological restoration and positive affect. Although causation was not established, the role of most environmental information criteria was important for predicting window view preferences, with nature generally outweighed the others. We recommend the use of the environmental information criteria in practice, but suggest some alterations to these standards to emphasize the importance of nature within window view design. Instead of only supporting high-quality views, nature should be promoted across all thresholds dictating view quality.

KEYWORDS:

• Window view
• design
• standards
• machine learning
• nature

Acknowledgments

This study was supported by the Center for the Built Environment (CBE), at the University of California, Berkeley; and the Republic of Singapore’s National Research Foundation through a grant to the Berkeley Education Alliance for Research in Singapore (BEARS) for the Singapore-Berkeley Building Efficiency and Sustainability in the Tropics (SinBerBEST) Program.

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

This research is funded by the Republic of Singapore’s National Research Foundation through a grant to the Berkeley Education Alliance for Research in Singapore (BEARS) for the Singapore-Berkeley Building Efficiency and Sustainability in the Tropics (SinBerBEST) Program. BEARS has been established by the University of California, Berkeley as a center for intellectual excellence in research and education in Singapore.