E-government / E-democracy

In what ways does the web change the ways we interact with government and change the ways we engage in politics?

What is Cybercrime and what do we do about?

A guest lecture by Professor David S.Wall from the University of Durham. This talk will explore the way that networked technology has transformed criminal behaviour. The first part will map out cybercrimes and identify the challenges they pose for both criminologists and also regulators. The second part will show that cybercrimes are informational, networked and global. In this section it will also be shown that cybercrimes are highly disorganised forms of offending when compared to the organisation of more 'traditional' crimes, but display some new organisational logics of their own. The third part of the talk will illustrate how the 'culture of fear' that has arisen around cybercrime has placed demands upon government and police - demands that, for reasons related to the distinct nature of cybercrimes, are hard to resolve. The fourth and final part will look at the new policing arrangements that are designed, it is argued here, to close the reassurance gap.

Open Government Data

Linked Open data – a platform for modern science, engineering, education and business. In the more recent talk, Sir Nigel Shadbolt speaks about "The Value of Openess - The Open Data Institute and Publically Funded Open Data" during the Natural History Museum of London Informatics Horizons event.

Predicting sense of community and participation by applying machine learning to open government data

Community capacity is used to monitor socio-economic development. It is composed of a number of dimensions, which can be measured to understand the possible issues in the implementation of a policy or the outcome of a project targeting a community. Measuring community capacity dimensions is usually expensive and time consuming, requiring locally organised surveys. Therefore, we investigate a technique to estimate them by applying the Random Forests algorithm on secondary open government data. This research focuses on the prediction of measures for two dimensions: sense of community and participation. The most important variables for this prediction were determined. The variables included in the datasets used to train the predictive models complied with two criteria: nationwide availability; sufficiently fine-grained geographic breakdown, i.e. neighbourhood level. The models explained 77% of the sense of community measures and 63% of participation. Due to the low geographic detail of the outcome measures available, further research is required to apply the predictive models to a neighbourhood level. The variables that were found to be more determinant for prediction were only partially in agreement with the factors that, according to the social science literature consulted, are the most influential for sense of community and participation. This finding should be further investigated from a social science perspective, in order to be understood in depth.