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<p>Fine-grained spatial utilization enhances post-occupancy evaluation (POE) precision. Traditional methods are limited by lower spatiotemporal resolution and smaller datasets, whereas indoor positioning systems offer high-precision occupancy data. The proposed indoor space utilization index combing spatial scale, occupancy points, and duration of stay to evaluate the distribution of spatio-temporal behavior and utilization rates within functional zones. Among a two-month period, a dataset of over 200,000 unlabeled behavioral data was collected in an open-office building using Wi-Fi and Bluetooth positioning systems. Through data processing and point projection, it is found that: (1) Point data shows spatio-temporal variations across floors, weekdays versus weekends, and different times of day. (2) High-density, long-duration workstation areas are highly utilized, while low-density, short-duration public spaces are underutilized. (3) Multi-functional atriums, open discussion areas, and entrance-linked elevators are most utilized, reflecting employee work patterns. Analysis of Kullback–Leibler divergence across different spatio-temporal units confirmed the reliability of conclusions, demonstrating that 20 weekdays of valid mobile phone data yield consistent results irrespective of grid sizes. This paradigm leverages long-term, non-intrusive, high-precision positioning data from Wi-Fi and Bluetooth systems to accurately track space utilization dynamics in real time across various scales, supporting human-centered POE.</p>

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A new space utilization assessment paradigm from the perspective of post-occupancy evaluation based on Wi-Fi and Bluetooth positioning systems

  • 期刊论文

  • 发表日期：

    2025年9月8日

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  • Xinting Gao,
  • Toshihiro Osaragi,
  • Jiazhi Ni,
  • Zhaoyang Luo,
  • Yang Geng,
  • Weimin Zhuang

摘要

Fine-grained spatial utilization enhances post-occupancy evaluation (POE) precision. Traditional methods are limited by lower spatiotemporal resolution and smaller datasets, whereas indoor positioning systems offer high-precision occupancy data. The proposed indoor space utilization index combing spatial scale, occupancy points, and duration of stay to evaluate the distribution of spatio-temporal behavior and utilization rates within functional zones. Among a two-month period, a dataset of over 200,000 unlabeled behavioral data was collected in an open-office building using Wi-Fi and Bluetooth positioning systems. Through data processing and point projection, it is found that: (1) Point data shows spatio-temporal variations across floors, weekdays versus weekends, and different times of day. (2) High-density, long-duration workstation areas are highly utilized, while low-density, short-duration public spaces are underutilized. (3) Multi-functional atriums, open discussion areas, and entrance-linked elevators are most utilized, reflecting employee work patterns. Analysis of Kullback–Leibler divergence across different spatio-temporal units confirmed the reliability of conclusions, demonstrating that 20 weekdays of valid mobile phone data yield consistent results irrespective of grid sizes. This paradigm leverages long-term, non-intrusive, high-precision positioning data from Wi-Fi and Bluetooth systems to accurately track space utilization dynamics in real time across various scales, supporting human-centered POE.