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

The PODD is a small, inexpensive solution to the gap in the available post occupancy evaluation tools. Currently architects and MEP engineers have limited access to data about their buildings once they have been occupied. Whatever data is available generally comes from building information systems which don’t offer much information about the comfort experience of the inhabitants.

The units are 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 left alone for extended periods of time, allowing for the collection of highly granular data about spaces.

The tool consists of hardware and software that allow for data collection on air quality, light, sound, and temperature. The prototype has gone through three iterations to date and the full set of 12 units has been undergoing test deployments ranging from 3 days to 3 weeks. The units can form any shape of network that best fits the space and density of data necessary. All the data collected is stored both locally on each unit and globally in a server-side database. We’ve built a simple web-app that allows us to see the data coming from all units overlaid in different configurations depending on what we are interested in studying. 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