Analysing spatio-temporal change by use of national historical Geographical Information Systems: Population change during and after the Great Irish Famine

Several countries have made large investments in building historical Geographical Information Systems (GIS) databases containing census and other quantitative statistics over long periods of time. Making good use of these databases requires approaches that explore spatial and temporal change.

Risks to patient confidentiality when communicating health information to blind and partially sighted patients.

The research reports on a survey of 228 blind and partially sighted persons in 15 health authorities across Scotland. The survey reports data on patient experience of receiving health information in accessible reading formats. Data indicated that about 90% of blind and partially sighted persons did not receive communications from various NHS health departments in a format that they could read by themselves. The implications for patient privacy, confidentiality and wider impact on life and health care are highlighted. The implications for professional ethical medical practice and for public policy are also discussed. Recommendations for improved practice are made.

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.