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.

Example General Feedback on Routes to Success Portfolios

This document provides example feedback which has been generated following the marking of a class set of portfolios. It is used as a part of the Routes to Success Module, specifically on the section titled Sustaining Success. Students can read the feedback prior to completing the portfolio to alert them to the possible shortfalls which may occur when they undertake this type of task. The feedback is introduced in the context that the task of completing the portfolio is a developmental one, and that students can expect to learn and improve their performance for this type of task as they develop and refine their skills.