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.

Introduction to Matlab - a self-study tutorial

Matlab is a high level language that is very easy to use and very powerful. It comes with a wealth of libraries and toolboxes, that you can use directly, so that you don't need to program low level functions. It enables you to display results very easily on graphs and images. To get started with it, you need to understand how to manipulate and represent data, and how to find information about the available functions. During this self-study tutorial, you will learn: 1- How to start Matlab. 2- How you can find out all the information you need. 3- How to create simple vectors and matrices. 4- What functions are available and how to find them. 5- How to plot graphs of functions. 6- How to write a script. After this (should take about an hour), you will know most of what you need to know about Matlab and should definitely know how to go on learning about it on your own…

Conways Game of Life JavaScript Exercise

For students learning JavaScript programming, this exercise sets out a fairly complete template for a DHTML implementation of Life. Students have to program the missing sections of code and attempt the extra features described. Only I have the password to unlock the solution!

WAIS seminar: Computer Science, the Revolution in Schools

Tuesday 22nd April 2014 Speaker(s): Sue Sentance Organiser: Leslie Carr Time: 22/04/2014 15:00-16:00 Location: B32/3077 File size: 698 Mb Abstract Until recently, "computing" education in English schools mainly focused on developing general Digital Literacy and Microsoft Office skills. As of this September, a new curriculum comes into effect that provides a strong emphasis on computation and programming. This change has generated some controversy in the news media (4-year-olds being forced to learn coding! boss of the government’s coding education initiative cannot code shock horror!!!!) and also some concern in the teaching profession (how can we possibly teach programming when none of the teachers know how to program)? Dr Sue Sentance will explain the work of Computing At School, a part of the BCS Academy, in galvanising universities to help teachers learn programming and other computing skills. Come along and find out about the new English Computing Revolution - How will your children and your schools be affected? - How will our University intake change? How will our degrees have to change? - What is happening to the national perception of Computer Science?

Justified assessments of service provider reputation

Abstract Reputation, influenced by ratings from past clients, is crucial for providers competing for custom. For new providers with less track record, a few negative ratings can harm their chances of growing. In the JASPR project, we aim to look at how to ensure automated reputation assessments are justified and informative. Even an honest balanced review of a service provision may still be an unreliable predictor of future performance if the circumstances differ. For example, a service may have previously relied on different sub-providers to now, or been affected by season-specific weather events. A common way to ameliorate the ratings that may not reflect future performance is by weighting by recency. We argue that better results are obtained by querying provenance records on how services are provided for the circumstances of provision, to determine the significance of past interactions. Informed by case studies in global logistics, taxi hire, and courtesy car leasing, we are going on to explore the generation of explanations for reputation assessments, which can be valuable both for clients and for providers wishing to improve their match to the market, and applying machine learning to predict aspects of service provision which may influence decisions on the appropriateness of a provider. In this talk, I will give an overview of the research conducted and planned on JASPR. Speaker Biography Dr Simon Miles Simon Miles is a Reader in Computer Science at King's College London, UK, and head of the Agents and Intelligent Systems group. He conducts research in the areas of normative systems, data provenance, and medical informatics at King's, and has published widely and manages a number of research projects in these areas. He was previously a researcher at the University of Southampton after graduating from his PhD at Warwick. He has twice been an organising committee member for the Autonomous Agents and Multi-Agent Systems conference series, and was a member of the W3C working group which published standards on interoperable provenance data in 2013.

ImageLearn - Decoding Britain's Landscape

Abstract Ordnance Survey, our national mapping organisation, collects vast amounts of high-resolution aerial imagery covering the entirety of the country. Currently, photogrammetrists and surveyors use this to manually capture real-world objects and characteristics for a relatively small number of features. Arguably, the vast archive of imagery that we have obtained portraying the whole of Great Britain is highly underutilised and could be ‘mined’ for much more information. Over the last year the ImageLearn project has investigated the potential of "representation learning" to automatically extract relevant features from aerial imagery. Representation learning is a form of data-mining in which the feature-extractors are learned using machine-learning techniques, rather than being manually defined. At the beginning of the project we conjectured that representations learned could help with processes such as object detection and identification, change detection and social landscape regionalisation of Britain. This seminar will give an overview of the project and highlight some of our research results.