Visualizing Earthquakes
These data consist of the dates, times, locations, depths, and
magnitudes of all 3+ magnitude earthquakes in California, Nevada, and
Oregon for approximately the last 30 years. (I'm going to have to dig
up my online source again - it's definitely possible to get
up-to-the-minute data. It's also possible to set a lower threshold
for the magnitudes to include.
(DTL - Possibly
http://www.ncedc.org/ncedc/catalog-search.html)
) I used this example in Stat 133 to
demonstrate how we can create KML animations in Google Earth; see the
attached assignment and screenshot. For this
assignment there was no specific scientific question, just to describe
the characteristics of the process that were apparent visually. (For
example, you can clearly see fault lines in the animation, and it's
also interesting to see how incredibly common the low-magnitude
earthquakes are.) There are a number of ways to spin off from this,
for example to take the qualitative observations from the animation
and think about how they might inform modelling choices for fitting
something like a self-exciting point process model. I think there are
also many simpler explorations/discussions that could be pursued, such
as
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how to scale the size of the points in KML to represent the
magnitude of the earthquakes - this raises some questions about human
perception and how to honestly represent the data
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discussing extreme value distributions and estimating parameters
related to the occurrence of various extreme events
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discussing censored data
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ignoring the magnitudes of the events and fitting a surface
representing the conditional intensity -- where are the most
earthquake-prone locations?
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looking for evidence of triggering of aftershocks
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- looking at the distribution of waiting times between earthquakes
within a certain radius of a particular location. I used this as part
of an example discussing sensitivity analysis when I was teaching
Bayesian statistics. We went through the process of eliciting a
"class prior" for the mean waiting time between events near Berkeley
and compared the results to using a noninformative prior -- see
SensitivityAnalysis.R.