|
Title:
|
VISUAL ANALYTICS IN THE PUBLIC SECTOR: AN ANALYSIS ON DIVERSITIES AND SIMILARITIES OF LONDONS WARDS |
|
Author(s):
|
Daqing Chen, Beatriz Martin Sanz and Erlong Zhao |
|
ISBN:
|
978-989-8533-80-7 |
|
Editors:
|
Ajith P. Abraham, Jörg Roth and Guo Chao Peng |
|
Year:
|
2018 |
|
Edition:
|
Single |
|
Keywords:
|
Visual Analytics, Data Mining, Principal Component Analysis, K-Means Clustering Analysis, Inequality |
|
Type:
|
Short Paper |
|
First Page:
|
214 |
|
Last Page:
|
219 |
|
Language:
|
English |
|
Cover:
|
|
|
Full Contents:
|
click to dowload 
|
|
Paper Abstract:
|
In this paper, an analysis is presented on the diverse and common characteristics in different geographical areas across Londons wards with respect to certain social, economic, and welfare measures. 18 data sets from different sources are used in the study. The principal component analysis and the k-means cluster analysis have been applied by using SAS Enterprise Guide and Miner. Visual analytics has been implemented with Tableau to identify patterns and correlations 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 suggests that collaborative management across all the Londons council boroughs is meaningful. |
|
|
|
|
|
|
