This paper presents an early-stage process to track communication patterns in office spaces using a real-time location system with sub-meter accuracy. A custom frontend application is used to assess and map the positi...
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ISBN:
(纸本)9781713873280
This paper presents an early-stage process to track communication patterns in office spaces using a real-time location system with sub-meter accuracy. A custom frontend application is used to assess and map the positioning data, with the goal of gathering empirical evidence on how spatial intervention can improve workplace communication and productivity. The case-study involved equipping an architectural firm's office and employees with tracking devices to gather data on their communicative behavior. Unsupervised machine learning algorithms are compared to identify location, quantity, and duration of tracked meeting events, providing insights into participants' communication behavior. While initial results are promising, this is a work in progress. The effect of tracking on individuals is being investigated through self-assessment questionnaires. Further research is needed to understand the potential impact of spatial intervention on workplace communication and productivity. The paper aims to contribute to the development of evidence-based design strategies in architecture and related fields.
This paper presents an early-stage process to track communication patterns in office spaces using a real-time location system with sub-meter accuracy. A custom frontend application is used to assess and map the positi...
This paper presents an early-stage process to track communication patterns in office spaces using a real-time location system with sub-meter accuracy. A custom frontend application is used to assess and map the positioning data, with the goal of gathering empirical evidence on how spatial intervention can improve workplace communication and productivity. The case-study involved equipping an architectural firm’s office and employees with tracking devices to gather data on their communicative behavior. Unsupervised machine learning algorithms are compared to identify location, quantity, and duration of tracked meeting events, providing insights into participants’ communication behavior. While initial results are promising, this is a work in progress. The effect of tracking on individuals is being investigated through self-assessment questionnaires. Further research is needed to understand the potential impact of spatial intervention on workplace communication and productivity. The paper aims to contribute to the development of evidence-based design strategies in architecture and related fields.
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