Mapping worlds? Excavating cartographic encounters in Plantation Ireland through GIS

This paper uses the analytical potential of Geographical Information Systems (GIS) to explore processes of map production and circulation in early-seventeenth century Ireland. The paper focuses on a group of historic maps, attributed to Josias Bodley, which were commissioned in 1609 by the English Crown to assist in the Plantation of Ulster. Through GIS and digitizing map-features, and in particular by quantifying map-distortion, it is possible to examine how these maps were made, and by whom. Statistical analyses of spatial data derived from the GIS are shown to provide a methodological basis for ‘excavating’ historical geographies of Plantation map-making. These techniques, when combined with contemporary written sources, reveal further insight on the ‘cartographic encounters’ taking place between surveyors and map-makers working in Ireland in the early 1600s, opening up the ‘mapping worlds’ which linked Ireland and Britain through the networks and embodied practices of Bodley and his map-makers.

A computational intelligent approach to multi-factor analysis of violent crime information systems

Various scientific studies have explored the causes of violent behaviour from different perspectives, with psychological tests, in particular, applied to the analysis of crime factors. The relationship between bi-factors has also been extensively studied including the link between age and crime. In reality, many factors interact to contribute to criminal behaviour and as such there is a need to have a greater level of insight into its complex nature. In this article we analyse violent crime information systems containing data on psychological, environmental and genetic factors. Our approach combines elements of rough set theory with fuzzy logic and particle swarm optimisation to yield an algorithm and methodology that can effectively extract multi-knowledge from information systems. The experimental results show that our approach outperforms alternative genetic algorithm and dynamic reduct-based techniques for reduct identification and has the added advantage of identifying multiple reducts and hence multi-knowledge (rules). Identified rules are consistent with classical statistical analysis of violent crime data and also reveal new insights into the interaction between several factors. As such, the results are helpful in improving our understanding of the factors contributing to violent crime and in highlighting the existence of hidden and intangible relationships between crime factors.