Abstract
The research described in this article integrated objective and subjective human analyses of the built environment. These were conducted from two perspectives: that of an individual and that of the built environment. The development of the research design over the course of this study involved 11 phases. A research design using an integrated method with intelligent and biometrical technologies served as the basis for developing the Affect-Based Built Environment Video Analytics (BEST), the application used in this research. BEST supplies stakeholders with the required data on the built environment and can assist them in analyzing and making decisions. The authors of this article attempt to integrate human emotions into the context of the maintenance and management of the built environment. One of the key aims is the indirect participation of inhabitants in maintenance and management procedures relating to the built environment. Consequently, BEST gathers and analyzes data on human affective attitudes and emotional and physiological states in the built environment and delivers these to city planners and stakeholders in maintenance and management. This is performed by applying neuro decision tables and an inhabitant-centered method for mining human emotion data from the built environment under analysis. Finally, this article illustrates the practical capabilities of BEST by employing case studies within Vilnius City as examples. The application of BEST proves that this system is valuable in conducting successful research and practical activities. • Neuro decision tables were used in the integrated analysis of the crowd in a built environment (BE). • BEST was used to analyze affective attitudes, emotional and physiological states, pollution and weather in a BE. • Six layers of data were analyzed during this research (over 200 million depersonalized data points). • This served as the basis for establishing over 20,000 of average and strong correlations. • In this way, the BE scientific problem was broadened and deepened compared with prior research.
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