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.

Enabling Provenance on the Web: Standardization and Research Questions

Provenance is a record that describes the people, institutions, entities, and activities, involved in producing, influencing, or delivering a piece of data or a thing in the world. Some 10 years after beginning research on the topic of provenance, I co-chaired the provenance working group at the World Wide Web Consortium. The working group published the PROV standard for provenance in 2013. In this talk, I will present some use cases for provenance, the PROV standard and some flagship examples of adoption. I will then move on to our current research area aiming to exploit provenance, in the context of the Sociam, SmartSociety, ORCHID projects. Doing so, I will present techniques to deal with large scale provenance, to build predictive models based on provenance, and to analyse provenance.