BIG DATA ANALYTICS IN THE PUBLIC SECTOR: A CASE STUDY OF NEET ANALYSIS FOR THE LONDON BOROUGHS
Chen, D, Asaolu, B and Qin, C (2016). BIG DATA ANALYTICS IN THE PUBLIC SECTOR: A CASE STUDY OF NEET ANALYSIS FOR THE LONDON BOROUGHS. International Conference on Big Data Analytics, Data Mining and Computational Intelligence. Funchal, Madeira, Portugal 02 - 04 Jul 2016 London South Bank University.
|Authors||Chen, D, Asaolu, B and Qin, C|
For decades, the issue of young people who are aged 16 -18 and not in employment, education, or training (NEET) has been a major concern for governments and local authorities. In this paper, a big data perspective is taken to examine the NEET issue in order to highlight factors that are correlated with NEET, the negative consequences and causes of NEET, and potential solutions to NEET. The NEET dataset about the 33 London Boroughs has been considered along with other seven datasets relating to population, crime offences, benefits claimants, median property price, active businesses, immigrants, and conception under 18. All the datasets are public-accessible and comprise a data collection of the same period of time from 2009 to 2013. Each of them represents a particular measure. Hierarchical variable clustering, k-means clustering, and correlation analysis have been conducted using SAS Enterprise Miner and Tableau in this work. These tools enable us to analyse the problem in a multi-dimensional, hierarchical, integrated, and longitudinal way. The research has demonstrated that a) The NEET issue is much more severe in outer London than in inner London; b) The main factors correlated with NEET vary from inner London to outer London; c) Each of the measures considered has a certain correlation strength with the NEET rate, and amongst them, median property price is a simple and seemingly accurate indicator of areas likely to suffer from NEET and thus to take appropriate precautions in order to reduce the likelihood of further increases in NEET; and d) The London Boroughs can be grouped based on similarities in terms of a set of given measures, and the memberships of the groups remain stable.
|Keywords||Big data analytics, NEET analysis, k-means clustering, Hierarchical clustering, SAS Enterprise Miner, Tableau.|
|Publisher||London South Bank University|
|Accepted author manuscript|
|04 Jul 2016|
|Publication process dates|
|Deposited||26 Sep 2016|
|Accepted||31 Mar 2016|
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