MALE SPEAKER: Be even better if it moved.
MALE SPEAKER: It would be.
MALE SPEAKER: You ready?
THIJS DAMSMA: OK, I'm ready, I guess.
MALE SPEAKER: OK.
Take it away.
THIJS DAMSMA: OK.
Hi, everyone.
Thanks for tuning in.
This is my presentation about visualization of
coastal data with KML.
My name is Thijs Damsa, and I've done this work for
EcoShape last summer.
And all the work I've done is available on openearth.eu.
First little bit, this is going to be about data from
the Netherlands.
That's where I'm from.
Netherlands is a low-lying country.
And we basically owe our existence to coastal zone
management.
If we wouldn't manage our coastal zone, the sea would
have taken out at least half of the country.
So that's why in the Netherlands coastal protection
is very important.
Now, within the Netherlands we have this new thing, EcoShape,
Building with Nature, that's a collaborative research program
for integrated coastal zone management.
So just a bunch of companies and institutes who work
together, put up some money, and basically do more research
about the coast.
And of course, Building with Nature indicates that it's all
sustainable nowadays, just like everything has to be.
Now, within EcoShape we have a thing called OpenEarth Tools.
And that's the geeky part of it, the technicalities to
facilitate the research.
When many people do a lot of research, you need some
technical infrastructure to help you out.
So OpenEarth Tools is all about managing data.
Because basically research, or the research we do, is mostly
about managing data.
So it's collecting data, getting it from whatever
institute owns it.
Then we collect it.
We store the data.
We put it in manageable format.
We put everything in that CDF.
Process the data, so do whatever you want with it, and
then present the data to whoever's your audience.
So that's the research cycle.
And one of the basic things we want to do, we want to collect
all the data available.
Because nowadays in research it's usually you spend a lot
of time collecting your data, finding out who owns it, where
is it, how is it stored, which format is it in, and how do I
get that into whatever computer program you use to
process it.
Also another thing we do next to collecting and storing data
is sharing tools.
So we don't want to reinvent the wheel every time, so
everybody is encouraged to share as much of their
programming with other people as possible, so everybody can
use and work on each other's tools.
We have a subversion repository with scripts.
A lot of it is in Matlab, because that's where a lot of
coastal researchers are familiar with, but also
Python, Fortran, whatever you can put in there.
And the participation is, of course, important.
More is better.
So you have to have an attractive thing that
everybody feels encouraged to sign up on and to work with.
Now that's where it gets a bit complicated.
Because not everyone is a programmer.
And if you explain these kind of sheets with how we manage
data, how we process it, how we have different servers to
do everything, for a lot of people, especially the
non-technical, the non-programmers, it's just
basically a bunch of [INAUDIBLE] mumbo jumbo.
And they don't really know what you're talking about or
what the advantages are of joining your program.
So the important thing is a visualization.
And this is a scan of a book which is--
every year they bring out the new book which shows some
coastal data from the Netherlands.
They have a map.
You have some green bars, which are good, and you have
red bars, which are bad.
But it's a book, so you have to print a new one every year.
And it's not interactive.
So it's a really kind of static thing.
And this is how it's done nowadays.
And of course we want more things.
So another way to present your data is, of course, in
computer programs.
But you usually get plots like this or like
this, or like this.
And usually you kind of miss the context of
what the data means.
And it's not always that appealing, so I'd say it's
kind of ugly.
And it's good for some purposes, but as an attractive
way of presenting your results, it's
maybe not that good.
Because it's all very static.
And still you don't get user interaction.
Or if you do want it, you have to develop a user interface.
And those are expensive to develop.
And it's difficult to do that in a proper and nice way.
So we have another thing, and that's called Google Earth.
We use Google Earth to present our data and as sort of a
visual front-end for the databases we've created.
We're experiencing now how we can use Google Earth to its
maximum to interact with the data and to present it.
So this is--
I want to give you a live demonstration of the Google
Earth stuff I've done.
So switch to Google Earth.
And I'll just pull up a chair.
OK, so first let's go to the Netherlands.
So fly here.
Netherlands, it's a country right down here.
Amsterdam, the capital.
First I'll show you a data set which we only
recently got hold of.
It's a Dutch heights, so it's like a datum of the country.
And this is a tiled image I've generated using the tools we
have, which show the height of the country.
Now here's the coloring, which more or less indicates the
natural beach color.
So blue is underwater, submerged, and green is above.
And sandy is sort of beach level.
So if I just switch it off, you'll see that a lot of the
country is actually colored blue, which indicates that
it's pretty much below sea level.
And you'd reduce the size of the country by about 50% if
there were no sea protections.
So just to give you a bit of an idea why we think coastal
protection so important in the Netherlands.
The good thing is you can use Google Earth to present data
which you would otherwise also present.
But if you look at the resolution of this map, it's
actually pretty high.
And if you print this out, you'll have a huge map.
And if you're only interested in a little bit, well, then
you need a lot of small maps.
But that's really inconvenient.
So this is a big advantage of Google Earth, just being able
to zoom interactively.
It dynamically loads data when it needs to.
So quickly you can get a good overview of your data.
There's much more possibilities.
And I'll show you a little thing we've made of Katrina
the hurricane.
This is a little animation of the hurricane.
The water levels are indicated by the coloring.
So the Gulf of Mexico and the arrows indicate the wind.
So I'll just play this.
MALE SPEAKER: [INAUDIBLE]
THIJS DAMSMA: Needs to load a little bit more.
So it's needs a bit time to load the background images.
But as I scroll through, get a bit smoother.
And you see here all the arrows indicating how the
hurricane has developed and how the wind pushed the water.
And here you can really see clearly how the wind pushed
the water all through New Orleans.
So you can understand why New Orleans was so badly hit.
Because just before the hurricane reached the shore,
the water levels said it wasn't that bad.
And really, as it progressed closely to the shore--
here you see it--
it really pushed the water all the way up to
the back of the city.
And that's why we got so much damage there.
So you can use Google Earth to make simple animations and
just look through them as you go.
And if you have a map any other way, of course it's
really hard to get this level of user interaction where you
can just scroll through your data just like that.
So this is one of the things I'd like to show you.
In the Netherlands we have an estuary.
It's called the Westerschelde.
It's down here in the bottom.
And this is a harbor of Antwerp.
So there's a lot of shipping from down this corner all up
to the sea.
But as it's an estuary you need to do a lot of
maintenance to make sure it's deep enough.
So there's a lot of maps of it.
So first I'll show a
bathymetric map of this estuary.
I'll just change the angle.
And a good thing of Google Earth is that you can easily
compare different data sets if you at least treat
them in the same way.
So the one I'll first show you, the height data set of
the Netherlands, this is a different source.
And you see immediately if I just pop it on and off that in
the estuary you get basically the same data, but it's
slightly different.
So if you're really interested in it, you could check it out,
why is the data different.
But at least visually you can get a quick impression that
there's different data, different sources, that
they're not always identical, of course.
Now, you can use traditional quiver plots to plot the flow.
Because a simulation was done here, so using advanced
modeling, a simulation was done of the tidal flows and
the speeds of the water which you get.
So at a certain time, these arrows represent the flow you
have in your estuary.
And of course, static arrows, that's something
maybe of the past.
So you want to have the flow, and have the flow dynamically.
So let me just load that.
It takes a sec.
And now if I look through it, you get a much better
indication of where you have the flow.
And you see at the deep parts you have redder, bigger arrows
moving faster, so that indicates a strong flow.
And with a downgoing tide, all the water flows back.
And you can see it really follows the gullies and gives
you a good indication of where the flow is and what the
important areas are, and gives you a nice way to disseminate
the results of a computational model within the public in a
way that they can actually play with it.
Because I just press the play button here, but of course you
can also drag different time steps or maybe if you enhance
the slide, you can get more of a history--
let's say a particle tracking way of how
the flow has developed.
Now, all these computations were done on a certain
computational grid.
It's also a good thing to note that in Google Earth, you see
here all the black indicates the grid, and all the--
these are all grid cells.
So it also gives you a good indication of which resolution
you can achieve and how this lines up with, for instance, a
satellite image.
So lines up pretty good, but not perfectly.
So this is the computational grid which we have used.
It gives you an idea of the scale and the size of what you
could achieve.
Now, another way of presenting the depth would be using a
contour plot.
So I'll also use contours here.
But as Google Earth 3D, why not make the
contour plot in 3D?
So this is what I've done.
Gives you nice 3D contour plot.
But unfortunately, the digital terrain model's not user
adjustable, which means that I have to plot something which
is underwater.
I have to lift it up above the ground and then exaggerate the
height of it to get an indication of how deep the
gullies are.
So you can really see the really steep edges here.
Let's go down here.
Now I could add some labels here.
A good thing about Google Earth is I don't have to worry
about all my labels clogging up.
Because as I zoom out, it will draw less labels.
Always make each individual label visual.
Now also, I could combine this with the grid
I just showed you.
And then you really get a nice indication of the 3D grid,
which is created to do these model runs.
So this is basically a powerful visualization which
you could never achieve in any other way.
The other data set which I've put in here is called the
Dutch Beach Lines.
Gives an indication of dune foot and shoreline positions
throughout the years.
And I'll just zoom in to one of these islands here.
And the data goes back-- this is 1842.
And as soon as these lines pop up, that's where I have
measurement data available.
So the blue line indicates the shoreline,
the edge of the beach.
And the green line, the dune foots are where the beach goes
over in the dunes.
As I scroll through that, you really get an indication of
how this island has changed throughout the years, where it
grows, where it erodes.
And this can help the general public understand what's
natural variations or what are artificial ones, and where you
should be worried about or what not.
It just really gives a quick view of what you can expect.
And also, if you make it broader this side where you
get an idea of the variation.
So it's normal that you have a few meters of
variations at points.
And if you want to measure, you can just use a ruler tool
here and see how many meters or feet of variations you have
throughout a certain time span.
So this is really something which can help out a lot.
And I've also plotted out the grid over which these lines
are measured, just to give an indication.
Now, other data sets have also been created.
This is called a [INAUDIBLE] data set.
It's only available from 1960 something.
Here it is.
And these are coastal profile measurements of the dunes.
Now, normally these are profiles-- you would
draw them in a plot.
But here I thought, why not plot them in 3D just over the
map, which, over the satellite picture, gives you a good idea
of these different data sets, what context they are made in.
Now, a powerful interpretation of this is all these lines
which you see here, they all seem to
represent dune profile.
You can't see by a line that actually this line is nonsense
and that line is nonsense and that one is good.
But you can see from the context that you see on this
satellite picture.
Let me just illustrate this to you.
So here you see dune profiles from year to year.
See also how the profiles influence where the beaches
and where not.
I see the blue and green lines moving back and forth.
But also you see the lines here on the right side jagging
very much from year to year.
And I'll just expand the time span which I'm viewing.
And you see here that there are really big variations of
the dune profile in here also from year to year.
And if you just look at the satellite picture, you
immediately understand why-- because this
is not a dune profile.
This is actually a coal stack of some kind of big
factory over here.
But they are included in the data.
And if you don't have the context, there's no way to
know, except if you very closely study the data, you
ask some questions.
But here you can just easily have a glance at your data,
just fly over it.
And those things, they become clear pretty quickly
what's going on.
So the next data set, the last one I want to show you, is
called [INAUDIBLE], a Dutch word.
But it means basically height maps which are measured from
year to year at different locations.
They're all stored in different charts.
And if I go to this location, I've taken some metadata from
the database, which is behind this.
And it just gives a really--
it's a quick way to look at metadata, even though I didn't
put very much in here.
But you can see the coordinates of the
measurements and whatever information you
want to show here.
You can see the years on which it's measured.
And on a few years I've pre-rendered some charts.
And I'd like to show you one.
You could click on the links, but I've already
loaded them in here.
So this is a 3D map of, actually, what's the data.
This is from 2005.
So I also used KMLs.
I directly plot a three-dimensional image of the
measurement.
I colored it and triangulated it.
So this gives an idea of the area which is measured.
And of course, you could load data from
several different points.
Just click on it, and then you can really make your
own map as you go.
Another one which is important, I've created this
time animation.
So all the different data which is
stored at this point--
let me just load everything.
Might take a sec.
OK, there, it's coming.
And now you see how the profiles have changed in time.
They seem getting nothing much changed, because it hasn't
been measured that frequently.
In the later years you can see how the coast has changed.
So I'll just do it myself.
You get a good idea of how the coastal
evolution is of this area.
Also, you notice in the back in, say, 1980,
there's hardly any bumps.
It's a very smooth bathymetric map.
But in later years there's a lot more detail.
Of course, the topography probably hasn't changed.
But it's more that the measurements have changed.
So it's probably multi-beam EcoSound instead of
single-beam, which gives you more detail.
But you can quickly compare all these maps, and really
easily you get an idea of the changes which
have occurred in time.
So small wrap-up--
if you use Google Earth as a visual
front-end for your database--
oh, I see I'm out of the screen.
MALE SPEAKER: Just stay seated.
THIJS DAMSMA: So if you use Google Earth as a visual
front-end of a database, first thing is it looks good and
everyone can use it.
I just showed you in a live demonstration, which went
almost flawless-- no but--
everyone can use it.
Google Earth is a user-friendly program.
I think Google put a lot of effort in making it easy for
everyone to use, no complicated menus and no
complicated installation procedures, and-- good thing--
no license fees.
It works on a lot of operating systems.
You have lots of possibilities.
You can make it interactive.
You can use four dimensions, three dimensional plus time.
This is a huge advantage over most other data viewers.
So to wrap it up, Google Earth can be used as a visual
front-end for the data in OpenEarth and, I think, for
many other data sets.
One big wish I have for KML is that I could use an adjustable
DTM so that I could adjust the terrain model without having
to make complicated 3D overlays and be able to dig a
hole in the earth to show my own data instead of have the
data as it is now, that I have to raise
everything up from the earth.
Now, OpenEarth tools, everything I've shown here is
created with Matlab.
Its source code, the documentation, and tutorials
to use this is freely available.
So you just go to openearth.eu and you'll
find everything there.
So thank you for your attention.
[APPLAUSE]