A Visual Analytics Approach for Station-Based Air Quality Data
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 integr...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2016-12-01
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| Series: | Sensors |
| Subjects: | |
| Online Access: | http://www.mdpi.com/1424-8220/17/1/30 |
| _version_ | 1798039785403580416 |
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| author | Yi Du Cuixia Ma Chao Wu Xiaowei Xu Yike Guo Yuanchun Zhou Jianhui Li |
| author_facet | Yi Du Cuixia Ma Chao Wu Xiaowei Xu Yike Guo Yuanchun Zhou Jianhui Li |
| author_sort | Yi Du |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-11T21:58:29Z |
| format | Article |
| id | doaj.art-4449f3863e6549b9a8113a5d09ad11e0 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-04-11T21:58:29Z |
| publishDate | 2016-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-4449f3863e6549b9a8113a5d09ad11e02022-12-22T04:01:02ZengMDPI AGSensors1424-82202016-12-011713010.3390/s17010030s17010030A Visual Analytics Approach for Station-Based Air Quality DataYi Du0Cuixia Ma1Chao Wu2Xiaowei Xu3Yike Guo4Yuanchun Zhou5Jianhui Li6Department of Big Data Technology and Application Development, Computer Network Information Center, Chinese Academy of Sciences, Beijing 100190, ChinaIntelligence Engineering Laboratory, Institute of Software, Chinese Academy of Sciences, Beijing 100190, ChinaDepartment of Computing, Imperial College London, London SW7 2AZ, UKDepartment of Big Data Technology and Application Development, Computer Network Information Center, Chinese Academy of Sciences, Beijing 100190, ChinaDepartment of Computing, Imperial College London, London SW7 2AZ, UKDepartment of Big Data Technology and Application Development, Computer Network Information Center, Chinese Academy of Sciences, Beijing 100190, ChinaDepartment of Big Data Technology and Application Development, Computer Network Information Center, Chinese Academy of Sciences, Beijing 100190, ChinaWith 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.http://www.mdpi.com/1424-8220/17/1/30visual analyticsspatio-temporal visualizationtime series visualizationmulti-dimensional visualizationair pollution |
| spellingShingle | Yi Du Cuixia Ma Chao Wu Xiaowei Xu Yike Guo Yuanchun Zhou Jianhui Li A Visual Analytics Approach for Station-Based Air Quality Data Sensors visual analytics spatio-temporal visualization time series visualization multi-dimensional visualization air pollution |
| title | A Visual Analytics Approach for Station-Based Air Quality Data |
| title_full | A Visual Analytics Approach for Station-Based Air Quality Data |
| title_fullStr | A Visual Analytics Approach for Station-Based Air Quality Data |
| title_full_unstemmed | A Visual Analytics Approach for Station-Based Air Quality Data |
| title_short | A Visual Analytics Approach for Station-Based Air Quality Data |
| title_sort | visual analytics approach for station based air quality data |
| topic | visual analytics spatio-temporal visualization time series visualization multi-dimensional visualization air pollution |
| url | http://www.mdpi.com/1424-8220/17/1/30 |
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