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-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.展开更多
基金supported by the National Key R&D Program of China(Grant No.2022YFC3801302)Young Scientists Fund of the National Natural Science Foundation of China(Grant No.52208113).
文摘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.
