BEMM458 Programming for Business Analytics

Air quality visualization Summary For multivariate data visualisation, the dataset to be visually analysed is of high dimensionality. Being a specific type of information visualisation, multivariate data visualisation is an active research field with numerous applications in diverse areas ranging from science communities and engineering design to industry and financial markets, in which the correlations between many attributes are of vital interest. The visualisation tasks for analysing weather data can be classified into three categories:

 

1. Finding correlations between different attributes. For example, any correlations between air pollution index and temperature should be examined for pinpointing air pollution sources.

 

2. Comparing data from different stations. It is always remarkable to examine the similarity or difference at different locations, as the geographic information can greatly affect the weather behaviour and thus lead to more accurate analysis and forecast.

 

3. Detecting the trend for UK weather and air quality. For time-series data, one of the most important issues is how to predict the future tendency based on the pattern we observe today. Based on the data we have and the visualisation task to be accomplished, we develop a comprehensive visualisation system. Our system consists of three major visualisation modules: polar system, parallel coordinates, and weighted complete graph.