TR242: Learning Socio-organizational Network Structure in Buildings with Ambient Sensing Data
Abstract/Contents
- Abstract
We develop a model that successfully learns social and organizational human network structure using ambient sensing data from distributed plug load energy sensors in commercial buildings. A key goal for the design and operation of commercial buildings is to support the success of organizations within them. In modern workspaces, a particularly important goal is collaboration, which relies on physical interactions among
individuals. Learning the true socio-organizational relational ties among workers can therefore help managers of buildings and organizations make decisions that improve collaboration. In this paper, we introduce the Interaction Model, a method for inferring human network structure that leverages data from distributed plug load energy sensors. In a case study, we benchmark our method against network data obtained through a survey and compare its performance to other data-driven tools. We find that unlike
previous methods, our method infers a network that is correlated with the survey network to a statistically significant degree (graph correlation of 0.46, significant at the 0.01 confidence level). We additionally find that our method requires only 10 weeks of sensing data, enabling dynamic network measurement. Learning human network structure through data-driven means can enable the design and operation of spaces that encourage, rather than inhibit, the success of organizations.
Description
Type of resource | text |
---|---|
Date created | November 2020 |
Creators/Contributors
Author | Sonta, Andrew | |
---|---|---|
Author | Jain, Rishee |
Subjects
Subject | Building design |
---|---|
Subject | sensing |
Subject | social networks |
Subject | statistical inference |
Subject | design |
Genre | Technical report |
Bibliographic information
Related Publication |
Journal Article
|
---|---|
Location | https://purl.stanford.edu/ss773tz5948 |
Access conditions
- Use and reproduction
- User agrees that, where applicable, content will not be used to identify or to otherwise infringe the privacy or confidentiality rights of individuals. Content distributed via the Stanford Digital Repository may be subject to additional license and use restrictions applied by the depositor.
Preferred citation
- Preferred Citation
Sonta, Andrew and Jain, Rishee. (2020). TR242: Learning Socio-organizational Network Structure in Buildings
with Ambient Sensing Data
. Stanford Digital Repository. Available at: https://purl.stanford.edu/ss773tz5948
Collection
CIFE Publications
View other items in this collection in SearchWorksContact information
- Contact
- cife-email@stanford.edu
Also listed in
Loading usage metrics...