Queering disasters: on the need to account for LGBTI experiences in natural disaster contexts

This article seeks a queering of research and policy in relation to natural disasters, their human impacts, management and response. The human impacts of natural disasters vary across different social groups. We contend that one group largely absent from scholarly and policy agendas is sexual and gender minorities, or lesbian, gay, bisexual, transgender/transsexual and intersex (LGBTI) populations. To demonstrate that these minorities have particular experiences that need to be addressed, we critically review five case studies that comprise the limited scholarly and policy research on LGBTI populations in disasters to date. Building on this, we offer some specific ways forward for queer disaster research that accounts for the vulnerabilities, needs and resilient capacities of LGBTI populations. In doing so, we recognise and urge researchers, policy-makers and aid agencies to acknowledge that LGBTI populations are not homogeneous and have different needs wrought by intersections of socio-economic resources, gender, race/ethnicity, age and regional or national location.     

The fractal geometry of ancient Maya settlement

Ancient Maya settlement patterns exhibit fractal geometry both within communities and across regions. Fractals are self-similar sets of fractional dimension. In this paper, we show how Maya settlement patterns are logically and statistically self-similar. We demonstrate how to measure the fractal dimensions (or Hausdorff–Besicovitch dimensions) of several data sets. We describe nonlinear dynamical processes, such as chaotic and self-organized critical systems, that generate fractal patterns. As an illustration, we show that the fractal dimensions calculated for some Maya settlement patterns are similar to those produced by warfare, supporting recent claims that warfare is a significant factor in Maya settlement patterning.     

Spatial Autocorrelation in Ecological Studies: A Legacy of Solutions and Myths

A major aim of including the spatial component in ecological studies is to characterize the nature and intensity of spatial relationships between organisms and their environment. The growing awareness by ecologists of the importance of including spatial structure in ecological studies (for hypothesis development, experimental design, statistical analyses, and spatial modeling) is beneficial because it promotes more effective research. Unfortunately, as more researchers perform spatial analysis, some misconceptions about the virtues of spatial statistics have been carried through the process and years. Some of these statistical concepts and challenges were already presented by Cliff and Ord in 1969. Here, we classify the most common misconceptions about spatial autocorrelation into three categories of challenges: (1) those that have no solutions, (2) those where solutions exist but are not well known, and (3) those where solutions have been proposed but are incorrect. We conclude in stressing where new research is needed to address these challenges.