3 resultados para Hazard perception
em Digital Commons - Michigan Tech
Resumo:
This dissertation explores the viability of invitational rhetoric as a mode of advocacy for sustainable energy use in the residential built environment. The theoretical foundations for this study join ecofeminist concepts and commitments with the conditions and resources of invitational rhetoric, developing in particular the rhetorical potency of the concepts of re-sourcement and enfoldment. The methodological approach is autoethnography using narrative reflection and journaling, both adapted to and developed within the autoethnographic project. Through narrative reflection, the author explores her lived experiences in advocating for energy-responsible residential construction in the Keweenaw Peninsula of Michigan. The analysis reveals the opportunities for cooperative, collaborative advocacy and the struggle against traditional conventions of persuasive advocacy, particularly the centrality of the rhetor. The author also conducted two field trips to India, primarily the state of Kerala. Drawing on autoethnographic journaling, the analysis highlights the importance of sensory relations in lived advocacy and the resonance of everyday Indian culture to invitational principles. Based on field research, the dissertation proposes autoethnography as a critical development in encouraging invitational rhetoric as an alternative mode of effecting change. The invitational force of autoethnography is evidenced in portraying the material advocacy of the built environment itself, specifically the sensual experience of material arrangements and ambience, as well as revealing the corporeality of advocacy, that is, the body as the site of invitational engagement, emotional encounter, and sensory experience. This study concludes that vulnerability of self in autoethnographic work and the vulnerability of rhetoric as invitational constitute the basis for transformation. The dissertation confirms the potential of an ecofeminist invitational advocacy conveyed autoethnographically for transforming perceptions and use of energy in a smaller-scale residential environment appropriate for culture, climate, and ultimately part of the challenge of sustaining life on this planet.
Resumo:
The present study was conducted to determine the effects of different variables on the perception of vehicle speeds in a driving simulator. The motivations of the study include validation of the Michigan Technological University Human Factors and Systems Lab driving simulator, obtaining a better understanding of what influences speed perception in a virtual environment, and how to improve speed perception in future simulations involving driver performance measures. Using a fixed base driving simulator, two experiments were conducted, the first to evaluate the effects of subject gender, roadway orientation, field of view, barriers along the roadway, opposing traffic speed, and subject speed judgment strategies on speed estimation, and the second to evaluate all of these variables as well as feedback training through use of the speedometer during a practice run. A mixed procedure model (mixed model ANOVA) in SAS® 9.2 was used to determine the significance of these variables in relation to subject speed estimates, as there were both between and within subject variables analyzed. It was found that subject gender, roadway orientation, feedback training, and the type of judgment strategy all significantly affect speed perception. By using curved roadways, feedback training, and speed judgment strategies including road lines, speed limit experience, and feedback training, speed perception in a driving simulator was found to be significantly improved.
Resumo:
The municipality of San Juan La Laguna, Guatemala is home to approximately 5,200 people and located on the western side of the Lake Atitlán caldera. Steep slopes surround all but the eastern side of San Juan. The Lake Atitlán watershed is susceptible to many natural hazards, but most predictable are the landslides that can occur annually with each rainy season, especially during high-intensity events. Hurricane Stan hit Guatemala in October 2005; the resulting flooding and landslides devastated the Atitlán region. Locations of landslide and non-landslide points were obtained from field observations and orthophotos taken following Hurricane Stan. This study used data from multiple attributes, at every landslide and non-landslide point, and applied different multivariate analyses to optimize a model for landslides prediction during high-intensity precipitation events like Hurricane Stan. The attributes considered in this study are: geology, geomorphology, distance to faults and streams, land use, slope, aspect, curvature, plan curvature, profile curvature and topographic wetness index. The attributes were pre-evaluated for their ability to predict landslides using four different attribute evaluators, all available in the open source data mining software Weka: filtered subset, information gain, gain ratio and chi-squared. Three multivariate algorithms (decision tree J48, logistic regression and BayesNet) were optimized for landslide prediction using different attributes. The following statistical parameters were used to evaluate model accuracy: precision, recall, F measure and area under the receiver operating characteristic (ROC) curve. The algorithm BayesNet yielded the most accurate model and was used to build a probability map of landslide initiation points. The probability map developed in this study was also compared to the results of a bivariate landslide susceptibility analysis conducted for the watershed, encompassing Lake Atitlán and San Juan. Landslides from Tropical Storm Agatha 2010 were used to independently validate this study’s multivariate model and the bivariate model. The ultimate aim of this study is to share the methodology and results with municipal contacts from the author's time as a U.S. Peace Corps volunteer, to facilitate more effective future landslide hazard planning and mitigation.