Know where - the earthquakes are (in 3D)
by Miles Denton (Critchlow)
https://docs.google.com/viewer?a=v&pid=explorer&chrome=true&srcid=0B1hHTea8xIPjYmYxMDNmZTUtMDg0OC00YTU0LTlkYWItYWFmZTU3Y2M4NTNm&hl=en_US
This study shows earthquake density, population density and dwelling insurance cost per meshblock for New Zealand in 3D. Earthquakes are a hot topic currently around NZ, especially due to the Christchurch Earthquakes which devastated the Canterbury region and the people of NZ. However did you know that in 2009 an earthquake of 7.8 hit Fiordland at a depth 30km. The February Christchurch earthquake was a magnitude 6.3, 10 km south-east of Christchurch at a depth of 5 km. Imagine if there was a large population density or city in Fiordland during the 2009 quake, the consequences could have been far worse than Christchurch. This is why the location of populations/ cities are important to avoid events like Christchurch. Obviously proximity to fault lines (especially unknown ones) are also important, along with soil type. What I hope this 3D visualisation does, is to get people to think about Location Intelligence and how this industry can help shape our future and that of New Zealand cities. I think the 3D element provides the audience with new insights on eathquakes and location, this is why it's original. This is why I use Critchlow's catch phrase of - know where. Location is very important and it can not only save lives but also your pocket. This is why the cost of dwelling insurance is "mashed up"/ included, which adds value and something different. The most useful part of the study I discovered was that Wellington pays a high premium on dwelling insurance even though the frequency of earthquakes are low. The most appealing aspect would be the seven "mash up" comparisons between the three data sets in 3D. The most fascinating and gosh darn awesome part would definitely be Christchurch due to its high levels across all visualisation models and Fiordlands frequency of earthquakes! Please refer to the following links to access and view all 7 pdfs (Google couldn't share them all on one page/link, as they are individual pdfs, the offical link pasted further into the entry form is all three datasets mixed and mashed together): EQ=Earthquake, IN=Insurance & POP=Population EQ EQ_IN EQ_POP EQ_POP_IN IN POP POP_IN
