PODD: Post Occupancy Data Device
Project Credits: LMN (Team: Plamena Milusheva, Belal Abboushi (early concept), Chris Savage, Kjell Anderson, Stephen VanDyck (partner in charge)
Photo Credits: LMN/Adam Hunter
WORKFLOWS
Arduino/C++
electronics fabrication
3D printer (Objet)
laser cutter
PCB design/KiCAD
Python
AWS EC2 & RDS
CHALLENGE:
Architects and Engineers don’t have good ways to learn from the buildings they design once those buildings are occupied by the users.
DESIGN QUESTION:
How can we create a more data-informed user-centered design process?
RESEARCH QUESTIONS:
How is building data collected currently?
What do designers want to know about their buildings?
What are major barriers to getting the data we want?
How do designers want to engage with the data?
SOLUTON:
The PODDs are small, inexpensive units designed to be networked together in large numbers, storing and uploading data from seven sensors that have been identified as most relevant for occupant comfort in buildings. The PODDs can be placed close to where occupants spend most of their time and left alone for extended periods of time, allowing for the collection of highly granular data about spaces.
The data collected by the PODD networks provides information about air quality, light, sound, and temperature in occupied areas. This data can be used to evaluate the building’s actual performance against the analysis performance used in the design process. Understanding the gap between the two can provide valuable information on how the analytical models can be adjusted to better inform the design of buildings.
Both the hardware and software for the project are open source.You can find more information on GitHub.
The PODDs have received an Award of Merit from the AIA Seattle Chapter and an R + D Award Honorable Mention from Architect Magazine. They have been featured in Metropolis, Architect Magazine, The Architect's Newspaper, Autodesk Redshift, and Building Design + Construction.
The notable issues with current sensing tools are price point, consolidation of sensors, limitations in data points, and lack of co-location of sensing and inhabitants. With the PODDs, the small losses in precision due to our push for low-cost and portability are more than offset by gaining continuity in readings over time as well as the ability to study spaces as a field condition of data points, rather than a single point in time and space.
Being able to study patterns for the comfort proxies over time allows for a more complete understanding of how a space is performing from the perspective of the people using it. In addition, seeing gradients or discontinuities of data throughout a space offers information about how some of the spatial aspects of a design might be affecting certain localized conditions that would be hard to capture with traditions data collection tools.
Image by Chris Savage / LMN
