The Maritime Spatial Planning (MSP) Challenge simulation platform helps planners and stakeholders understand and manage the complexity of MSP. In the interactive simulation, different data layers covering an entire sea region can be viewed to make an assessment of the current status. Users can create scenarios for future uses of the marine space over a period of several decades. Changes in energy infrastructure, shipping, and the marine environment are then simulated, and the effects are visualized using indicators and heat maps. The platform is built with advanced game technology and uses aspects of role-play to create interactive sessions; it can thus be referred to as serious gaming. To calculate and visualize the effects of planning decisions on the marine ecology, we integrated the Ecopath with Ecosim (EwE) food web modeling approach into the platform. We demonstrate how EwE was connected to MSP, considering the range of constraints imposed by running scientific software in interactive serious gaming sessions while still providing cascading ecological feedback in response to planning actions. We explored the connection by adapting two published ecological models for use in MSP sessions. We conclude with lessons learned and identify future developments of the simulation platform.
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Exposure data available to developers of earthquake loss models are often very crudely aggregated spatially, and in such cases very considerable effort can be required to refine the geographical resolution of the building stock inventory. The influence of the geographical resolution of the exposure data for the Sea of Marmara region in Turkey is explored using several different levels of spatial aggregation to estimate the losses due to a single earthquake scenario. The results show that the total damage over an urban area, expressed as a mean damage ratio (MDR), is rather insensitive to the spatial resolution of the exposure data if a sufficiently large number of ground-motion simulations are used. However, the variability of the MDR estimates does reduce as the spatial resolution becomes higher, reducing the number of simulations required, although there appears to be a law of diminishing returns in going to very high exposure data resolution. This is largely due to the inherent and irreducible spatial variability of ground motion, which suggests that if only mean MDR estimates are needed, the effort required to refine the spatial definition of exposure data is not justified.
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Recent advancements in mobile sensing and wearable technologies create new opportunities to improve our understanding of how people experience their environment. This understanding can inform urban design decisions. Currently, an important urban design issue is the adaptation of infrastructure to increasing cycle and e-bike use. Using data collected from 12 cyclists on a cycle highway between two municipalities in The Netherlands, we coupled location and wearable emotion data at a high spatiotemporal resolution to model and examine relationships between cyclists' emotional arousal (operationalized as skin conductance responses) and visual stimuli from the environment (operationalized as extent of visible land cover type). We specifically took a within-participants multilevel modeling approach to determine relationships between different types of viewable land cover area and emotional arousal, while controlling for speed, direction, distance to roads, and directional change. Surprisingly, our model suggests ride segments with views of larger natural, recreational, agricultural, and forested areas were more emotionally arousing for participants. Conversely, segments with views of larger developed areas were less arousing. The presented methodological framework, spatial-emotional analyses, and findings from multilevel modeling provide new opportunities for spatial, data-driven approaches to portable sensing and urban planning research. Furthermore, our findings have implications for design of infrastructure to optimize cycling experiences.
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