CHAN Wing Yi, Winnie[Represented by MAK Wai Ho, Wallace]

Visual Analysis of the Air Pollution Visual Analysis of the Air Pollution Problem in Hong KongProblem in Hong Kong

Hong Kong ICT Awards 2007:Best Innovation and Research Award

ICT07/IR/ CU-18

Department of Computer Science and EngineeringThe Hong Kong University of Science and Technology (HKUST)

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Preface

• This is the work of a final year thesis(research option of final year project)

• The research paper will appear inIEEE Transactions on Visualization and Computer Graphics (TVCG).

Visual Analysis of the Air Pollution Problem in Hong KongHuamin 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)

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Outline

• Introduction

▫ Background

▫ Uniqueness of Air Quality Data

• Visualization Techniques

• Experimental Results

• Conclusions and Future Work

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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

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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 proposed without any formal proof yet

The spectacular harbor view has been increasingly crippled by massive haze.

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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

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Uniqueness of Air Quality Data

• Time-series (hourly-based)

• Inherited geographic information

• Multi-dimensional (typically >10 attributes)

• Important vector field –wind speed and direction

1. Precipitation

2. Wind Direction

3. Air Temperature

4. Wind Speed

5. Dew Point

6. Relative Humidity

7. Sea Level Pressure

8. Respirable Suspended Particulates (RSP)

9. Nitrogen dioxide (NO2)

10. Sulphur dioxide (SO2)

11. Ozone (O3)

12. Carbon monoxide (CO)

13. Solar Radiation

14. Air Pollution Index (API)

15. Contributed Pollutant to API

(Spans more than 10 years)

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Outline

• Introduction

• Visualization Techniques

▫ Polar System

▫ Parallel Coordinates

▫ Weighted Complete Graph

• Experimental Results

• Conclusions and Future Work

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Outline

• Introduction

• Visualization Techniques

▫ Polar System

▫ Parallel Coordinates

▫ Weighted Complete Graph

• Experimental Results

• Conclusions and Future Work

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Polar System

• Is a common vector representation

• Is heavily applied by domain scientists in environmental field

Distance from center Wind SpeedAngle from the north Wind DirectionColor Scalar Attribute

very strong south windhigh attribute value

weak southwest windlow attribute value

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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

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Outline

• Introduction

• Visualization Techniques

▫ Polar System

▫ Parallel Coordinates

▫ Weighted Complete Graph

• Experimental Results

• Conclusions and Future Work

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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

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S-Shape Axis for Vector

Traditional layout(not intuitive)

Circular layout(lots of overlapping)

S-style layout

An example

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Outline

• Introduction

• Visualization Techniques

▫ Polar System

▫ Parallel Coordinates

▫ Weighted Complete Graph

• Experimental Results

• Conclusions and Future Work

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Weighted Complete Graph

• It is used for exploring overall relationship among all data dimensions

• Each node represents one data dimension

• Distance between nodes encodestheir correlation

AB

C

correlated

not reallycorrelated

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Outline

• Introduction

• Visualization Techniques

• Experimental Results

▫ Correlation Detection

▫ Similarities and Differences

▫ Time-Series Trend

• Conclusions and Future Work

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[solar radiation]

Correlation Detection

• RSP is correlated with SO2 and O3, but not solar radiation

• High API value (red pixels) are not found when SO2 is high, inferring that SO2 contributed little to API

• API is strongly correlated with O3 which is known to experts

• Some suspicious clusters are shown in [SO2] and [O3] - a blue cluster is seen behind a green one

[SO2] [O3]

Color = Air Pollution Index (API)

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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

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• High SO2 for most stations:

▫ Strong wind

▫ Northwest wind

▫ External Sources

• High SO2 for Kwai Chung:

▫ All wind speed

▫ Southwest wind

▫ Internal sources likely due to cargo ships at KwaiTsing Container Terminals

Similarities and Differences (2)

9 stations of 3 years dataColor represents amount of SO2

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Time-Series Trend for Tung Chung

• 2004 and 2005 plots are more similar

• In 2006 plot, temperature varies dramatically

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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 graphprovides correlation overview that is useful for initiating an analysis

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Outline

• Introduction

• Visualization Techniques

• Experimental Results

• Conclusions and Future Work

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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

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Future Work

• Continue as a long-term project with ENVF

• Make the system available to the public on Web

• Incorporate new datasets for further exploration

• Add animations and other visual aids

Thank You!

The EndThe End

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Q & A

Polar system with embedded circular pixel bars

Weighted complete graph

Enhanced parallel coordinates with S-shape vector axis