1. On trend analysis in relation to my project of data markup and visualization, I think it is very relevant. For, with data markup there needs to be observed certain trends in most cases to effectively markup and later process the data. Also, in data visualization, the goal would be to properly display data so that it can be interpreted well and how it's supposed to be interpreted for the audience. In data visualization, one would want the person who is observing a visualized data set to not only comprehend the data well but also to comprehend it in a manner so that he is not be misguided or deceived by the figures. I.e. the two graphs from the class session on Trend Analysis.
2. On weather prediction, data markup and visualization are very applicable. Already with data visualization organizations and governments enable people to see maps with future, past, or present air pressure, temperature, air current, cloud formations, etc. Visualization will only continue help with reading weather predictions to the public. Also, in the weather agenda data markup is already prevalent. From http://xml.coverpages.org/weatherML.html: ""WeatherML (Weather Mark-up Language) will be the global standard protocol for weather derivatives deal description data. It will enable efficient electronic processing of weather trades, allowing compatibility between systems, reducing trading costs and operational risks. WeatherML will provide increased credibility for the weather derivatives market by signaling to observers that the market is mature enough for standards to emerge." Too, for the public, people could grab specified future weather headlines in an automated fasion, so that one could find the weather and not bother to look specifically for the weather.
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