Visual Analysis of the Air Pollution Visual Analysis of the Air Pollution Problem in Hong Kong Problem in Hong Kong CHAN Wing Yi, Winnie [Represented by MAK Wai Ho, Wallace] Department of Computer S cience and Engineering The Hong Kong University of S cience and Technology (HKUS T) Hong Kong ICT Awards 2007: Best Innovation and Research Award ICT07/ IR/ CU-18
Preface • This is the work of a final year thesis (research option of final year proj ect) • The research paper will appear in IEEE Transactions on Visualization and Computer Graphics (TVCG). Visual Analysis of the Air Pollution Problem in Hong Kong Huamin Qu, Wing-Yi Chan , Anbang Xu, Kai-Lun Chung, Kai-Hon Lau, Ping Guo IEEE Transactions on Visualization and Computer Graphics (TVCG), vol.13, no. 6, Nov.-Dec. 2007 (Proceedings of IEEE Visualization/ Information Visualization 2007) 2
Outline • Introduction ▫ Background ▫ Uniqueness of Air Quality Data • Visualization Techniques • Experimental Results • Conclusions and Future Work 3
Introduction • Visualization ▫ Presents data in pictorial form ▫ Visualizes the underlying data effectively • Visual analysis ▫ Is a visual way for data mining and decision making ▫ Performs analysis on the visualization result 4
Hong Kong Air Pollution Problem • Hong Kong air quality is decreasing tremendously • Air pollution problem becomes one of the biggest social issues • Causes are still unknown ▫ Many hypotheses are The spectacular harbor view has been proposed without any increasingly crippled by massive haze. formal proof yet 5
Institute for the Environment of HKUST • Maintain a comprehensive database on Hong Kong air quality data • Cannot obtain convincing results for high-level correlations with mathematical techniques • Demand visualization techniques for analysis 6
Uniqueness of Air Quality Data 1. Precipitation • Time-series (hourly-based) 2. Wind Direction • Inherited geographic 3. Air Temperature 4. Wind S peed information 5. Dew Point 6. Relative Humidity • Multi-dimensional 7. S ea Level Pressure (typically >10 attributes) 8. Respirable S uspended Particulates (RS P) 9. Nitrogen dioxide (NO 2 ) • Important vector field – 10. S ulphur dioxide (S O 2 ) wind speed and direction 11. Ozone (O 3 ) 12. Carbon monoxide (CO) 13. S olar Radiation 14. Air Pollution Index (API) 15. Contributed Pollutant to API (S pans more than 10 years) 7
Outline • Introduction • Visualization Techniques ▫ Polar S ystem ▫ Parallel Coordinates ▫ Weighted Complete Graph • Experimental Results • Conclusions and Future Work 8
Outline • Introduction • Visualization Techniques ▫ Polar S ystem ▫ Parallel Coordinates ▫ Weighted Complete Graph • Experimental Results • Conclusions and Future Work 9
Polar System • Is a common vector representation • Is heavily applied by domain scientists in environmental field weak southwest wind very strong south wind low attribute value high attribute value Distance from center � Wind S peed Angle from the north � Wind Direction Color � S calar Attribute 10
Circular Pixel Bars • Users select a sector to plot the inside-sector data (i.e. of certain wind direction and speed) • The corresponding wind direction and wind speed is obvious for rapid comparisons between sectors 11
Outline • Introduction • Visualization Techniques ▫ Polar S ystem ▫ Parallel Coordinates ▫ Weighted Complete Graph • Experimental Results • Conclusions and Future Work 12
Parallel Coordinates • Parallel Coordinates are well-established visualization tool for multi-dimensional data • Each parallel vertical axis represents an attribute • A data item is plotted by a polygonal line intersecting each axis at the respective attribute data value 13
S-Shape Axis for Vector Traditional layout Circular layout S-style layout (not intuitive) (lots of overlapping) An example 14
Outline • Introduction • Visualization Techniques ▫ Polar S ystem ▫ Parallel Coordinates ▫ Weighted Complete Graph • Experimental Results • Conclusions and Future Work 15
Weighted Complete Graph • It is used for exploring overall relationship among all data dimensions • Each node represents one data dimension • Distance between nodes encodes their correlation C A B not really correlated correlated 16
Outline • Introduction • Visualization Techniques • Experimental Results ▫ Correlation Detection ▫ S imilarities and Differences ▫ Time-S eries Trend • Conclusions and Future Work 17
Correlation Detection Color = Air Pollution Index (API) [SO 2 ] [O 3 ] [solar radiation] • RS P is correlated with S O 2 and O 3 , but not solar radiation • High API value ( red pixels) are not found when S O 2 is high, inferring that S O 2 contributed little to API • API is strongly correlated with O 3 which is known to experts • S ome suspicious clusters are shown in [S O 2 ] and [O 3 ] - a blue cluster is seen behind a green one 18
Similarities and Differences (1) • The Hong Kong society mostly weighs external pollution factors more ▫ Pollutants blown in from factories on the Pearl River Delta at the northwest of Hong Kong • Local pollution is often ignored ▫ Power plants ▫ Vehicles and vessels 19
Similarities and Differences (2) • High S O 2 for most stations: ▫ S trong wind ▫ Northwest wind ▫ External S ources • High S O 2 for Kwai Chung: ▫ All wind speed ▫ S outhwest wind ▫ Internal sources likely due to cargo ships at Kwai Tsing Container Terminals 9 stat ions of 3 years data Color represents amount of S O 2 20
Time-Series Trend for Tung Chung • 2004 and 2005 plots are more similar • In 2006 plot, temperature varies dramatically 21
Positive Feedback from Users • Domain scientists found that the polar system with embedded pixel bar offers easy navigation to explore the data interactively • Parallel coordinates show the general relationship for them to compare different data-sets rapidly • Weighted complete graph provides correlation overview that is useful for initiating an analysis 22
Outline • Introduction • Visualization Techniques • Experimental Results • Conclusions and Future Work 23
Conclusions • Comprehensive System ▫ The first attempt designed for air quality analysis • Novel Techniques ▫ Polar system with circular pixel bars: scalar + vector ▫ Enhanced parallel coordinates: vector + time axes ▫ Weighted complete graph: correlation overview • Significant Application ▫ Analyzed Hong Kong air pollution problem ▫ Revealed known findings effectively ▫ Detected unknown patterns by domain scientists 24
Future Work • Continue as a long-term proj ect with ENVF • Make the system available to the public on Web • Incorporate new datasets for further exploration • Add animations and other visual aids 25
Thank You! The End The End
Q & A Polar system with embedded Weighted complete graph circular pixel bars Enhanced parallel coordinates with S -shape vector axis 27
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