German-language culture and the Slav stranger within

The aim of this article is to delineate the symbolic position of the Slavonic, and in particular the Czech, in German-language Austrian culture of the period 1890–1940. My approach will be informed by psychoanalysis. A subsidiary aim is to try to demonstrate uses of psychoanalysis in the study of central European culture. What is at issue here is an historical set of social power relations that find their expression in culture, that is to say, in art and literature, and that can be interpreted by psychoanalysis. All too often psychoanalysis avoids the social and the political outside the framework of the individual and her or his predictable traumas emanating from domestic life.1 This article, however, constitutes an exercise in inter- and intra-cultural psychoanalysis: intra-cultural as an investigation of psychoanalytic dynamics within German-language culture; inter-cultural as an examination of the relationship between German-language and Slav cultures in psychoanalytic terms

Spatially Lagged Choropleth Display

Choropleth display of spatial information is a fundamental feature of mapping and geographic information system technologies. There has long been a desire to impart some spatial influence in the class selection and delineation process of choropleth display. This paper presents an approach for representing the spatial influence of neighboring areas in the creation of choropleth classes. The usefulness of this approach is explored using suburb level crime statistics for Brisbane, Australia.

Driving Markov chain Monte Carlo with a dependent random stream

Markov chain Monte Carlo is a widely-used technique for generating a dependent sequence of samples from complex distributions. Conventionally, these methods require a source of independent random variates. Most implementations use pseudo-random numbers instead because generating true independent variates with a physical system is not straightforward. In this paper we show how to modify some commonly used Markov chains to use a dependent stream of random numbers in place of independent uniform variates. The resulting Markov chains have the correct invariant distribution without requiring detailed knowledge of the stream's dependencies or even its marginal distribution. As a side-effect, sometimes far fewer random numbers are required to obtain accurate results.Comment: 16 pages, 4 figure