We explore a wearables and sensors centric approach for collecting data in built environments. In addition, we propose a design methodology as a focus to assist in the design of applications and experiments for affective computing. The implications of such systems is to aid in the reproducibility of experiments and better intelligent systems.

The drive toward sustainable wastewater management is challenging the conventional paradigm of linear end-of-pipe solutions. A shift toward more sustainable solutions requires that information about new ideas, systems, and technologies be more readily accessible for addressing wastewater problems. It is commonly argued that decision-making needs to involve engineers and other community representatives to define values and brainstorm solutions. This paper describes a decision support system (DSS) prototype that is designed to help community planners identify solutions which balance environmental, economic, and social goals. The system is designed to be scalable, adaptable, and flexible to allow fair assessment of new ideas and technologies. It supports the exploration of consequences of various alternatives and visualizes the tradeoffs between them. Our DSS takes in modular descriptions of components and a description of a community context, automates the design of alternative wastewater systems, and facilitates evaluating how well each design satisfies the given context. It provides an adaptable platform from which new solutions can be designed without having to predefine how a single component fits within a specific system. Our DSS facilitates the exploration of alternative solutions by visualizing the effect of various tradeoffs and their consequences in relation to the community's sustainability goals.

Visual quality and impact assessments have historically relied on experts to formally evaluate the visual properties of a landscape. In contrast, environmental psychologists have studied subjective landscape preferences using ratings and surveys. These two approaches represent, respectively, the “objectivist” and “subjectivist” paradigms within visual landscape research. A gap, however, exists between these approaches: actual observation behaviors. In this paper, we argue for the inclusion of eye-tracking research in visual landscape assessments as a critical bridge between objective landscape qualities and subjective visual experiences. We describe the basics of eye-tracking methods and data types to introduce the role of eye movements in landscape preference formation. Three-dimensional immersive virtual environments are particularly useful for collecting these types of data, as they allow for quantification of the viewed environment’s spatial and scene metrics in addition to providing eye-tracking capabilities at sufficient resolutions. These environmental and behavioral data can then be consolidated and analyzed within existing GIS platforms to draw conclusions about environmental influences on observation behaviors. While eye tracking may eventually contribute directly to the practice of visual quality or impact assessments, the near-term benefits of this work will most likely center around contributing to the objectivity and defensibility of assessments through validation and methodological recommendations.