The geography of reported domestic violence in Brisbane: a social justice perspective

Police call data and court data are used to map the incidence of reported domestic violence in Brisbane. These data are correlated with low family income, unemployment and a measure of multiple disadvantage (an Index of Relative Socio-Economic Disadvantage) for each Statistical Local Area (suburb) in Brisbane. Only the Index of Relative Socio-Economic Disadvantage is a statistically significant predictor of reported domestic violence. The finding of a significantly higher incidence of reported domestic violence among relatively worse-off families is investigated within a social justice context. A measure of multiple relative disadvantage is shown to better reflect the negative impacts of structural inequalities on families in explaining the reported occurrence of domestic violence.

Geography's emerging cross-disciplinary links: Process, causes, outcomes and challenges

In Australian universities the discipline of Geography has been the pace-setter in forging cross-disciplinary links to create multidisciplinary departments and schools, well ahead of other disciplines in humanities, social sciences and sciences, and also to a greater extent than in comparable overseas university systems. Details on all cross-disciplinary links and on immediate outcomes have been obtained by surveys of all heads of departments/schools with undergraduate Geography programs. These programs have traced their own distinctive trajectories, with ramifying links to cognate fields of enquiry, achieved through mergers, transfers, internal initiatives and, more recently, faculty-wide restructuring to create supradisciplinary schools. Geography's `exceptionalism' has proved short-lived. Disciplinary flux is now extending more widely within Australian universities, driven by a variety of internal and external forces, including: intellectual questioning and new ways of constituting knowledge; technological change and the information revolution; the growth of instrumentalism and credentialism, and managerialism and entre-preneurial imperatives; reinforced by a powerful budgetary squeeze. Geographers are proving highly adaptive in pursuit of cross-disciplinary connections, offering analytical tools and selected disciplinary insights useful to non-geographers. However, this may be at cost to undergraduate programs focussing on Geography's intellectual core. Whereas formerly Geography had high reproductive capacity but low instrumental value it may now be in a phase of enhanced utility but perilously low reproductive capacity.

Hyperspace geography: Visualizing fitness landscapes beyond 4D

Human perception is finely tuned to extract structure about the 4D world of time and space as well as properties such as color and texture. Developing intuitions about spatial structure beyond 4D requires exploiting other perceptual and cognitive abilities. One of the most natural ways to explore complex spaces is for a user to actively navigate through them, using local explorations and global summaries to develop intuitions about structure, and then testing the developing ideas by further exploration. This article provides a brief overview of a technique for visualizing surfaces defined over moderate-dimensional binary spaces, by recursively unfolding them onto a 2D hypergraph. We briefly summarize the uses of a freely available Web-based visualization tool, Hyperspace Graph Paper (HSGP), for exploring fitness landscapes and search algorithms in evolutionary computation. HSGP provides a way for a user to actively explore a landscape, from simple tasks such as mapping the neighborhood structure of different points, to seeing global properties such as the size and distribution of basins of attraction or how different search algorithms interact with landscape structure. It has been most useful for exploring recursive and repetitive landscapes, and its strength is that it allows intuitions to be developed through active navigation by the user, and exploits the visual system's ability to detect pattern and texture. The technique is most effective when applied to continuous functions over Boolean variables using 4 to 16 dimensions.