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Post-occupancy evaluation (POE) is a user-focused evaluation method, centered around understanding how dwellers occupy and use built environments. Collecting big data on user behavior has become essential for POE. In this paper, the limitations of current POE protocol data adoption and analysis methods are analyzed, and a quantitative research method based on machine vision (MV) to quantify users’ spatio-temporal behavior for POE is developed, which can acquire high-resolution data more efficiently and accurately than the common methods in this field. In particular, the method of calculating user activity using frame difference algorithms fills the gap of quantifying user activity in POE research. The outdoor space of a kindergarten and the indoor space of a university library are selected to validate the method. After analyzing 4.5 million frames of data accumulated over 50 consecutive hours in the outdoor space, and 8.82 million frames of data accumulated over 98 consecutive hours in the indoor space, our method successfully analyzed usage of indoor and outdoor spaces and generated multiple quantitative indices of POE from high-resolution spatiotemporal behavior variables. The robustness, convenience, and practicability of this method in practical applications are verified. This method is simple to use, can adapt to a variety of built environments, and has a low cost. It has potential competitiveness for large-scale application. Quantitative POE research methods enhance the flow of information from space usage to spatial design and provide a data-driven basis for improving spatial sustainability, planning, design, and decision making.

A machine vision–based, quantitative method of capturing spatiotemporal activity for post-occupancy evaluation research