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Evaluating user-system interaction is important to develop effective and acceptable systems. Traditionally, geospatial systems are evaluated in terms of usability with emphasis on completing application tasks and goals. This focus ignores the nonfunctional aspects that separate systems with similar usability and functionality, and complete a user's experience. An alternative and holistic user experience (UX) concept that encompasses functional (e.g. usability, ergonomics, etc.) and nonfunctional (e.g. aesthetics, emotions, pleasure, cognitive stimulation, etc.) dimensions was adopted to evaluate an innovative Experiential GIS (EGIS). The EGIS is an immersive geospatial system that disengages the user from the real-world and renders the user present in 3D geovirtual scenes with real-time sensorimotor feedback. This system was assessed in a soil mapping application involving four collaborating soil scientists. The scientists had very positive reactions, common viewpoints and occasionally varying perceptions of EGIS and the geovirtual soil mapping technique. They viewed the system as intuitive, enjoyable and capable of improving the speed and quality of soil mapping, and identified the system's strengths as including co-experiential knowledge construction and a 'go anywhere' capability that enabled access to physically inaccessible and trespass prohibited areas. The scientists' views were more varied about the role of EGIS in minimizing soil mapping labor and costs.

The scope of wildfires over the previous decade has brought these natural hazards to the forefront of risk management. Wildfires threaten human health, safety, and property, and there is a need for comprehensive and readily usable wildfire simulation platforms that can be applied effectively by wildfire experts to help preserve physical infrastructure, biodiversity, and landscape integrity. Evaluating such platforms is important, particularly in determining the platforms’ reliability in forecasting the spatiotemporal trajectories of wildfire events. This study evaluated the predictive performance of a wildfire simulation platform that implements a Monte Carlo-based wildfire model called WyoFire. WyoFire was used to predict the growth of 10 wildfires that occurred in Wyoming, USA, in 2017 and 2019. The predictive quality of this model was determined by comparing disagreement and agreement areas between the observed and simulated wildfire boundaries. Overestimation–underestimation was greatest in grassland fires (>32) and lowest in mixed-forest, woodland, and shrub-steppe fires (<−2.5). Spatial and statistical analyses of observed and predicted fire perimeters were conducted to measure the accuracy of the predicated outputs. The results indicate that simulations of wildfires that occurred in shrubland- and grassland-dominated environments had the tendency to over-predict, while simulations of fires that took place within forested and woodland-dominated environments displayed the tendency to under-predict.

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