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A Visual Analytics Approach for Station-Based Air Quality Data


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1

Department of Big Data Technology and Application Development, Computer Network Information Center, Chinese Academy of Sciences, Beijing 100190, China

2

Intelligence Engineering Laboratory, Institute of Software, Chinese Academy of Sciences, Beijing 100190, China

3

Department of Computing, Imperial College London, London SW7 2AZ, UK





*

Author to whom correspondence should be addressed.



Abstract With the deployment of multi-modality and large-scale sensor networks for monitoring air quality, we are now able to collect large and multi-dimensional spatio-temporal datasets. For these sensed data, we present a comprehensive visual analysis approach for air quality analysis. This approach integrates several visual methods, such as map-based views, calendar views, and trends views, to assist the analysis. Among those visual methods, map-based visual methods are used to display the locations of interest, and the calendar and the trends views are used to discover the linear and periodical patterns. The system also provides various interaction tools to combine the map-based visualization, trends view, calendar view and multi-dimensional view. In addition, we propose a self-adaptive calendar-based controller that can flexibly adapt the changes of data size and granularity in trends view. Such a visual analytics system would facilitate big-data analysis in real applications, especially for decision making support. View Full-Text

Keywords: visual analytics; spatio-temporal visualization; time series visualization; multi-dimensional visualization; air pollution visual analytics; spatio-temporal visualization; time series visualization; multi-dimensional visualization; air pollution





Autor: Yi Du 1, Cuixia Ma 2, Chao Wu 3, Xiaowei Xu 1, Yike Guo 3, Yuanchun Zhou 1 and Jianhui Li 1,*

Fuente: http://mdpi.com/



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