Learning socio-organizational network structure in buildings with ambient sensing data

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. Impact Statement The structure of social and organizational relationships in commercial building workplaces is a key component of work processes. Understanding this structure—typically described as a network of relational ties—can help designers of workspaces and managers of workplaces make decisions that promote the success of organizations. These networks are complex, and as a result, our traditional means of measuring them are time and cost intensive. In this paper, we present a novel method, the Interaction Model, for learning these network structures automatically through sensing data. When we compare the learned network to network data obtained through a survey, we find statistically significant correlation, demonstrating the success of our method. Two key strengths of our proposed method are, first, that it uncovers network patterns quickly, requiring just 10 weeks of data, and, second, that it is interpretable, relying on intuitive opportunities for social interaction. Data-driven inference of the structure of human systems within our built environment will enable the design and operation of engineered built spaces that promote our human-centered objectives.