Mythologizing Greed and Betrayal in the Strait of Magellan in Juan de Miramontes' Armas Antarticas

Taking as a starting point Antonio Sanchez Jimenez's recent work on the myth of Jason in Lope de Vega's El Vellocino de Oro, this article examines the myths of Jason and Leander in Juan de Miramontes Zuazola's epic, Armas antarticas (1608-1609). After exploring the explicit allusions to the myths in the epic, I analyse an implicit reference to Leander in the portrayal of Tome Hernandez, the only survivor of the failed sixteenth-century Spanish settlements in the Strait of Magellan. As it turns out, Hernandez was rescued by the English pirate, Thomas Cavendish. In evoking the myth of Leander on the southern coast of Chile, Armas antarticas recalls Alonso de Ercilla's self-description in La Araucana (1569, 78, 89) as analysed by Ricardo Padron. However, I contend that the representation of Hernandez is best understood in comparison to Juan Boscan's Leandro (1543), the fullest and most widely disseminated Spanish version of the tale in the sixteenth century. Appealing to the Leander myth allows Armas antarticas to turn away from a focus on the role of greed in colonization. Yet shadows of Jason still lurk behind the portrayal of Hernandez, which raise other serious ethical questions for the Spanish Empire concerning piracy and loyalty as these play out in the Strait of Magellan. This essay shows that the poetic portrayal of Hernandez and Cavendish ends up exhibiting the same ambiguities associated with piracy as analysed by Daniel Heller-Roazen.

PROBABILISTIC MODELING OF RAINFALL INDUCED LANDSLIDE HAZARD ASSESSMENT IN SAN JUAN LA LAGUNA, SOLOLÁ, GUATEMALA

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.

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