Publication Type
Journal Article
Version
publishedVersion
Publication Date
11-2012
Abstract
The advances in location-based data collection technologies such as GPS, RFID etc. and the rapid reduction of their costs provide us with a huge and continuously increasing amount of data about movement of vehicles, people and goods in an urban area. This explosive growth of geospatially-referenced data has far outpaced the planner’s ability to utilize and transform the data into insightful information thus creating an adverse impact on the return on the investment made to collect and manage this data. Addressing this pressing need, we designed and developed DIVAD, a dynamic and interactive visual analytics dashboard to allow city planners to explore and analyze city’s transportation data to gain valuable insights about city’s traffic flow and transportation requirements. We demonstrate the potential of DIVAD through the use of interactive choropleth and hexagon binning maps to explore and analyze large taxi-transportation data of Singapore for different geographic and time zones.
Keywords
Geographic Information System (GIS), Movement Data, GeoVisual Analytics, Urban Planning, MITB student
Discipline
Databases and Information Systems | Data Science | Numerical Analysis and Scientific Computing
Research Areas
Data Science and Engineering
Publication
International Journal of Computer, Electrical, Automation, Control and Information Engineering
Volume
6
Issue
11
First Page
834
Last Page
1353
Identifier
10.5281/zenodo.1081041
Edition
1358
Publisher
World Academy of Science, Engineering and Technology
Citation
KAM, Tin Seong; BARSHIKAR, Ketan; and TAN, Shaun Jun Hua.
DIVAD: A Dynamic and Interactive Visual Analytical Dashboard for Exploring and Analyzing Transport Data. (2012). International Journal of Computer, Electrical, Automation, Control and Information Engineering. 6, (11), 834-1353.
Available at: https://ink.library.smu.edu.sg/sis_research/1761
Copyright Owner and License
Authors
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Additional URL
https://doi.org/10.5281/zenodo.1081041
Included in
Databases and Information Systems Commons, Data Science Commons, Numerical Analysis and Scientific Computing Commons