Geographically weighted spatial statistical methods are a family of spatial statistical methods developed to address the presence of non-stationarity in geographical processes, the so-called spatial heterogeneity. While these methods have recently become popular for analysis of spatial data, one of their characteristics is that they produce outputs that in themselves form complex multi-dimensional spatial data sets. Interpretation of these outputs is therefore not easy, but is of high importance, since spatial and non-spatial patterns in the results of these methods contain clues to causes of underlying non-stationarity. In this article, we focus on one of the geographically weighted methods, the geographically weighted discriminant analysis (GWDA), which is a method for prediction and analysis of categorical spatial data. It is an extension of linear discriminant analysis (LDA) that allows the relationship between the predictor variables and the categories to vary spatially. This produces a very complex data set of GWDA results, which include on top of the already complex discriminant analysis outputs (e.g. classifications and posterior probabilities) also spatially varying outputs (e.g. classification function parameters). In this article, we suggest using geovisual analytics to visualise results from LDA and GWDA to facilitate comparison between the global and local method results. For this, we develop a bespoke visual methodology that allows us to examine the performance of global and local classification method in terms of quality of classification. Furthermore, we are also interested in identifying the presence (or absence) of non-stationarity through comparison of the outputs of both methods. We do this in two ways. First, we visually explore spatial autocorrelation in both LDA and GWDA misclassifications. Second, we focus on relationships between the classification result and the independent variables and how they vary over space. We describe our visual analytic system for exploration of LDA and GWDA outputs and demonstrate our approach on a case study using a data set linking election results with a selection of socio-economic variables.

Smalltalk on a RISC (SOAR) is a simple, Von Neumann computer that is designed to execute the Smalltalk-80 system much faster than existing VLSI microcomputers. The Smalltalk-80 system is a highly productive programming environment but poses tough challenges for implementors: dynamic data typing, a high level instruction set, frequent and expensive procedure calls, and object-oriented storage management. SOAR compiles programs to a low level, efficient instruction set. Parallel tag checks permit high performance for the simple common cases and cause traps to software routines for the complex cases. Parallel register initialization and multiple on-chip register windows speed procedure calls. Sophisticated software techniques relieve the hardware of the burden of managing objects. We have initial evaluations of the effectiveness of the SOAR architecture by compiling and simulating benchmarks, and will prove SOAR's feasibility by fabricating a 35,000-transistor SOAR chip. These early results suggest that a Reduced Instruction Set Computer can provide high performance in an exploratory programming environment.