So this module, we're gonna focus on time and temporal data.
This is a relatively new thing in GIS,
being able to handle temporal data in an effective way.
And essentially what we're doing is adding an additional dimension to the map.
So makes even harder to represent, particularly in sort of flat, static map.
There lots of different types of data that we deal with
when we're dealing with temporal data,
because there's lots of different things that can change over time.
Now, most of what we're gonna work with is gonna be vector data,
changes in vector data over time, then we're gonna focus a little bit
at the end here about how to represent changes in roster data over time.
So the first thing we need to think about is what is changing and
how is it changing, in some sort of spacial temporal context.
Now this could be points, this can be lines, it can be polygons,
it can even be volumes.
So for example a point is probably going to change in continuously along a line.
Whether this is something like a track log or whether this is simply the location of
say a radio collar on an animal changing periodically throughout the day.
Now we can also if we're looking at areas we could be thinking about evolution
of an area in either size or shape.
So a particular area whether it be say urban sprawl or decreasing forest cover.
It could be getting bigger relative to other things.
It could be changing shape entirely.
We could even be getting things where we're seeing changes in the number of
occurrences.
Where we might have what was previously one
forest that gets fragmented when someone puts a road through it.
And then over time, this could even one of the two sections could move around
a little bit, and they could even merge back together.
So as you can imagine,
when you're building something like this in a GIS context, having the ways that
those vector data interact with each other when they're becoming different features,
and then merging back together can be pretty complicated.
Now, there are a lot of different ways we can illustrate time on a map as well.
These can be as simple as a single, kind a static map that shows something temporal,
whether be change from two different time periods.
So this could be,
here's a very famous one showing ancient courses of the Mississippi river.
This is by the US Army Corp of Engineers from the 1940's and
you can see what it's showing here is it's showing something temporal.
They're showing the different pass of the meanders along the Mississippi river.
That taken over sort of accorded history,
but it's not necessarily showing them as a slice of time.
It's showing them over time, colored differently.
So we see the current one in 1944 path of the Mississippi in sort of white.
And then other time periods showing different colors.
We can also show something statically
with time in there by showing differences between two time periods.
So in this case we're seeing population change between two different time periods,
so an increase or decrease in population.
Now a lot of these spatial and temporal visualizations are based on a very, early
map that I just wanna present because it's a really kind of cool one here.
This is from 1861, this is the Charles Minard map of
showing sort of Napoleon's path towards Moscow and then back.
So it's showing both time, it's showing weather along the bottom.
And it's also scaled to be showing both geography of where the troops went but
the line itself is scaled to how many or
how large the army was at any given moment.
So you can see it's very big at the beginning as they're going
out towards Moscow it's getting smaller, slightly smaller as battles are fought and
people are lost to the winter weather.
After the battle, it's extremely small and it just gets even narrower and
narrower and narrower as we're moving backwards.
So these are interesting ways of showing time on a static map.
Now, we can also show time by looking at a series of static maps,
so a time series if you will.
So this could show something like how a country or
states boarders have altered over time, or
how neat national features have changed by showing individual slices of time.
[SOUND] So essentially we're looking at a different slice of time across each one,
allowing ourselves to be able to compare between the different times as we're
looking at them.
So here, we see an example of this showing election results.
You can see that there are different slices of time
showing things that our eyes can follow across the maps.
We could also do this for something like raster data where we see vegetation
indices compared over multiple different times here.
Now the last way that we can visualize data, temporal data in a GIS context, or
when we're creating maps, is through animation.
Now obviously when you're creating an animated time series map,
you're going to be seeing, essentially, a video.
So, this allows us to show everything at once.
To show all the different time slices that we might be interested in,
and then animated them together into some sort of video.
So, obviously, it's only gonna work in a digital context.
You can't print this out and have it mean anything at all.
And it can be, depending on how you're viewing the video,
difficult to focus on an individual slice of time.
So these are not always the most applicable way to visualize temporal data.
So here we see a time series video.
An animation showing the different states and
territories of the US from 1783 up until the modern time.
So you can see that there are changes.
We have them symbolized in different ways and
we're still able to show everything kind of as a video here.
So when we're dealing with time data in ArcGIS, for
vector data we have integrated tools that we can use.
We need to have them formatted in a particular way, but
there are different methods that we can use
to visualize temporal data that are sort of integrated in ArcGIS.
Now in particular what we need to have is we need to have repeated features
instead of repeated fields.
Now what do I mean by this?
Well essentially each feature has to be repeated for
each time series that there is.
We can't just have a column that's 1980 a column that's 1981, a column that's 1982.
We need to have a feature for 1980, a feature for 1981, and a feature for 1982.
And this is so that we can show things like different changes in the actual
geometry of those features or the locations of them.
So if we see here, this is an example of showing different time periods
as different columns, as different fields.
And what we need to have is we need to have it like this,
where we have different.
Features, individual features for each time slice that have either
a start date or a start and end date for the each one of the features.
Now, there are also ways that we can render and
deal with raster data in our GIS.
They're a little bit more complicated, and they use a type of file called a Net CDF.
And this is not something that there's native support for in our GIS.
We have to manually create a layer using the Arc Toolbox for this.
And we'll go over this in some detail in the demonstration, but essentially what
a Net CDF is is it's a raster image where there's one single layer of data
might be precipitation, might be temperature, might be something like that
there usually environmental data and then there's a whole lot of different slices so
essentially it's like a thousand band image where each band is a time slice.
So we have to trick the program to be able to display that in one band and
then correlate the all of the different ones with the time.
So this is what one of these might look like rendered as a video where we can see
the changes in each month in precipitation shown as the different colors
here where red is a higher precipitation and blue is a lower precipitation.
So you can see the seasonal changes.
You can see everything else put together into a nice little animation here.
[SOUND] So there are a lot of different places where you can find already
pre-rendered temporal data to use in ArcGIS.
Now, a lot of these tend to be vector data.
The two that are listed up here are the Atlas of Historic County Boundaries
through the Newberry Library.
And the National Historic Geographic Information System.
Both of these are pre-built layers showing county and state boundaries
with some census data associated with them throughout the time in the U.S.
there's similar institutions for other countries if you're interested in those.
And then there are a lot of places to find climatic data, particularly for
the last 20 years or so.
But there are models that go back even further.
And the best one of these is the National Oceanic and Atmospheric Administration,
NOAA's, website who created gridded climatic datasets, the link for
which is seen up here.
So that's a little bit of an overview of time and temporal data.
I want you to take away from this that it's possible to use these things to
create animations, to create static maps, to really have your GIS layers time aware.
But you do have to make some pretty significant choices in how you organize
the data and how you create the vectors you're working with.