Visual Analysis of the Air Pollution Problem in Hong Kong
Huamin Qu1, Wing-Yi Chan1, Anbang Xu3, Kai-Lun Chung1, Kai-Hon Lau2, Ping Guo3
IEEE Transactions on Visualization and Computer Graphics (TVCG), vol. 13, no. 6, pp. 1408-1415, Nov.-Dec. 2007
(Proceedings of IEEE Visualization and Information Visualization 2007)
HKICT Awards 2007:
Best Innovation and Research (College & Undergraduates) Silver Award
Certificate of Merit - Best Innovation Award
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Abstract
| We present a comprehensive system for weather data visualization. Weather data are multivariate and contain vector fields formed by wind speed and direction. Several well-established visualization techniques such as parallel coordinates and polar systems are integrated into our system. We also develop various novel methods, including circular pixel bar charts embedded into polar systems, enhanced parallel coordinates with S-shape axis, and weighted complete graphs. Our system was used to analyze the air pollution problem in Hong Kong and some interesting patterns have been found. |
Figures
| Figure 1
Hong Kong's air pollution problem. The spectacular harbor view has been increasingly crippled by a massive haze. Locations of different air quality monitoring stations shown as dots in 18 districts of Hong Kong. Figure 3Traditional polar system: (a) Encoding scheme; (b) Mapping radius without preserving the area; (c) Area-preserving polar system. Blue denotes low values and red denotes high values. The color bar is also used for other figures. Figure 4Polar system with circular pixel bar: (a) A sector selected by a user and a circular bar chart embedded into the sector; (b) Blending of circular pixel bar for data falling in the sector against one for its complement. Figure 5Comparing circular pixel bars with rectangular ones: (a) A polar system with multiple circular pixel bars; (b) Conventional pixel bars for the sectors. The overall patterns are preserved in the sector for comparison, and in-depth numerical analysis may be performed on the supplement rectangular pixel bars. Figure 6Polar system with time information: (a) x-position, y-position, and color of the sector indicate the month of observation, amount of SO2, and temperature, respectively; (b) the x-position now represents the day in which the entry was recorded; (c) the y-position now encodes the day and the x-position encodes the month. Figure 7Different layouts of parallel coordinates: (a) Traditional layout; (b) Circular layout; (c) S-style layout. Figure 8Enhanced parallel coordinates with S-shape axis to encode wind direction and scatterplot to reveal bi-variate relationship between neighboring axes. Figure 9Weighted complete graph: (a) Layout of weighted complete graph with node size encoding the accumulated correlation coefficients and edge encoding the correlation between two nodes. Edges with small weights are removed for clarity. (b) Parallel coordinates with a user-chosen axis order based on the weighted complete graph; (c) Parallel coordinates with a random axis order. Figure 10Detecting the correlation between the Air Pollution Index (API) and other attributes when API is high: (a) Initial polar system with color denoting API value. The northwest sector is chosen, plotting RSP against solar radiation. (b) Plotting RSP against SO2 instead, high API value (red pixels) are not found when SO2 is high, revealing SO2 contributed little to API. (c) Y-position now becomes O3 clearly correlated with API. For (b) and (c), suspicious clusters (blue clusters behind green ones) are shown. Figure 11Detecting correlations of the same set of data by Parallel Coordinates, with color denoting API value. Figure 12(a) Tracking the possible internal and external pollution sources through nine stations in the past three years. Pixel color represents the amount of SO2 recorded in each individual station. (b)-(c) The detailed plots for station Tung Chung and Kwai Chung respectively. Figure 13Comparing two stations, Kwai Chung and Tung Chung with parallel coordinates using color to represent wind direction. Clusters of wind direction records are found in Tung Chung station but not in Kwai Chung. Figure 14Visualizing time-series data. Each row is comprised of polar plots for different stations, namely Tung Chung, Yuen Long and Mong Kok, in different periods of time from March 2004 to March 2007 at intervals of six months. The pixel color denotes the Air Pollution Index (API). Figure 15Time-series polar plots for Kwai Chung station focusing on the impact of local pollution from the southwest direction. X-position, y-position, and color of the sector encode day, SO2, and API accordingly. Prominent red pixels are mainly seen in year 2004. Figure 163-year time-series data of Yuen Long district that are constrained to a range of wind speed and direction by sector selection: (a) Weighted complete graph for each year with edge width encoding the correlation strength; (b) Parallel Coordinates with a time axis. Color also denotes time value for clarity. Figure 17Using a weighted complete graph as a visual aid in exploring dimension correlations for year 2006 data of the Yuen Long station: (a) By arranging more correlated attributes together, positive and negative correlations are clearly shown in the parallel coordinates; (b) Users can also plot the attributes demonstrating interesting relationships in the weighted complete graph as the embedded pixel bar in the polar system. |
Links
- IEEE Visualization 2007
- IEEE Digital Library Page
- IEEE Xplore Page
- The Hong Kong University of Science and Technology (HKUST)
- VisGraph, Department of Computer Science and Engineering, HKUST
- Institute for the Environment (IENV), Environmental Central Facility (ENVF), HKUST
- Real-Time Data Display from IENV Atmospheric & Environmental Database
- Beijing Normal University (BNU)

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