Visual analytics in the public sector: An analysis on diversities and similarities of London’s wards
Chen, D, Sanz, BM and Zhao, E (2018). Visual analytics in the public sector: An analysis on diversities and similarities of London’s wards. International Conference on Big Data Analytics, Data Mining and Computational Intelligence 2018 (BigDaCI 2018). Madrid, Spain 18 - 20 Jul 2018 Bigdaci.
|Authors||Chen, D, Sanz, BM and Zhao, E|
In this paper, an analysis is presented on the diverse and common characteristics in different geographic areas across London’s wards with respect to certain social, economic, and welfare measures. 18 data sets have been collected from different sources and used in the study. The principal component analysis and the k-means cluster analysis have been applied by using SAS Enterprise Guide and SAS Enterprise Miner. Visual analytics has been implemented with Tableau to identify patterns and correlation among various measures. It has been found that a geographical distance or proximity does not necessarily indicate a significant difference or similarity between different areas on a given social and economic measure. The work is of practical importance in that it suggests that collaborative management across all the London’s council boroughs is sensible and meaningful. The traditional approach to manage a borough needs to be reconsidered.
|Accepted author manuscript|
CC BY 4.0
|18 Jul 2018|
|Publication process dates|
|Deposited||10 Jul 2018|
|Accepted||14 Jun 2018|
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