Visualization of real-time monitoring datagraphic of urban environmental quality

Quality of urban environment directly affects people health, and it is important to understand the real-time status of urban air quality. Air quality monitoring, data analysis, and visualization can grasp the concentration data of air pollutants in cities. In view of the current air quality monitoring using digital displays, it is difficult for users to intuitively determine the air pollution level with unsatisfied interaction mode of the data query. Using the real-time monitoring data of 23 observation points in Beijing, the work based on Google Earth applied Keyhole Markup Language (KML) for the visualization of air monitoring data. The interactive query makes it easier for users to query air quality, and gradually varied color can visually highlight the air quality level. Visualization of data has stronger expression (more images and more intuitive) than the original data table, which is beneficial for further analysis of data.

[1]  Eiman Kanjo,et al.  NoiseSPY: A Real-Time Mobile Phone Platform for Urban Noise Monitoring and Mapping , 2010, Mob. Networks Appl..

[2]  Moustafa Ghanem,et al.  Grid-based analysis of air pollution data , 2006 .

[3]  Helmut Mayer,et al.  Evolution of the air pollution in SW Germany evaluated by the long-term air quality index LAQx , 2008 .

[4]  Antoine Triantafyllou,et al.  Geolokit: An interactive tool for visualising and exploring geoscientific data in Google Earth , 2017, Int. J. Appl. Earth Obs. Geoinformation.

[5]  Jie Li,et al.  Visual analytics of smogs in China , 2016, J. Vis..

[6]  Yong Li,et al.  A web-based visual analytics system for air quality monitoring data , 2014, 2014 22nd International Conference on Geoinformatics.

[7]  Michael Silberbauer,et al.  Google Earth: A spatial interface for SA water resource data , 2009 .

[8]  Chu Yanya Analysis of Urban Air Pollution Diffusion Simulation Based on Google Earth , 2014 .

[9]  Nataliia Kussul,et al.  Grid and sensor web technologies for environmental monitoring , 2009, Earth Sci. Informatics.

[10]  Aijun Chen,et al.  Visualization of A-Train vertical profiles using Google Earth , 2009, Comput. Geosci..

[11]  Qingyuan Zhou,et al.  Multi-layer affective computing model based on emotional psychology , 2018, Electron. Commer. Res..

[12]  Lu Tao Application of AIRNow Ambient Air Quality Notification System of USA in Shanghai , 2011 .

[13]  Deepayan Sarkar,et al.  Lattice: Multivariate Data Visualization with R , 2008 .

[14]  Gunasekaran Manogaran,et al.  Visual analysis of geospatial habitat suitability model based on inverse distance weighting with paired comparison analysis , 2017, Multimedia Tools and Applications.

[15]  Qingyuan Zhou,et al.  The Study on Evaluation Method of Urban Network Security in the Big Data Era , 2018 .

[16]  Ping Guo,et al.  Visual Analysis of the Air Pollution Problem in Hong Kong , 2007, IEEE Transactions on Visualization and Computer Graphics.

[17]  Hong Fan,et al.  A Visualization Approach to Air Pollution Data Exploration—A Case Study of Air Quality Index (PM2.5) in Beijing, China , 2016 .

[18]  Salil S. Kanhere,et al.  A survey on privacy in mobile participatory sensing applications , 2011, J. Syst. Softw..

[19]  Stefano Tavani,et al.  Building a virtual outcrop, extracting geological information from it, and sharing the results in Google Earth via OpenPlot and Photoscan: An example from the Khaviz Anticline (Iran) , 2014, Comput. Geosci..

[20]  Gabor Grothendieck,et al.  Lattice: Multivariate Data Visualization with R , 2008 .