Yeah. Okay. Mhm. Peter Sforza
, I'm the director here at Virginia Tech at the
Center for Geospatial Information Technology. I'll be talking to
you today about some of our practical experiences with data
were deep in the digital world uh in the physical
world as well. We take measurements there, but
then we turned them into the digital representations. So
I'm gonna show you basically an overview of a few
of our projects as to provide some insight into how
we manage our data and some of the thinking that's
going in to make this very robust um uh applications
suite from both the science side, as well as
doing the best we can with the data and the
best management, best practices for data, for privacy
, for security, uh for building our user communities
and trust within those communities. And that's really the
reason we do those things. So I just I
had thrown this slide in here as it's a little
bit old. This was if it's going to play
from 2000 I'm sorry it's not going to play.
Maybe I'm not pushing thoughts a quick time out available
. Okay. All right so new quick time.
Well that was a great shot of uh collecting data
in the field. This was when google glass came
out. So I'm wearing google glass and I'm launching
a drone collecting data and sort of documenting the whole
process at the same time. Um And that was
going to be my opening example of one of the
ways that we're dealing with cutting edge technologies. Um
Let's start here. This is the at the center
for Geospatial Information technology required right across the bridge.
And Torgersen 2060. Uh We're currently uh as of
last year we are situated in the College of Natural
Resources and Environment uh for the previous five years prior
to that we were actually located in Central I.
T. Under the central administration. And we were
an experiment we were a hybrid research and administrative group
paired up in one office under converged technologies for safety
security resilience. And so we were sort of the
skunk works for the university side. They would throw
hard challenges at us whether it was caldwell field shooting
or campus three D. Models or network security issues
. We got involved with a lot of things.
And so the challenge for me as a research center
in that in that I. T. And administrative
role was how do we now? We're kind of
focused on operations. So we built a strategy of
operations to research and back to operations that allows us
to sort of help the operational community evolve and have
a more future proof kind of view of what they're
doing. And it allowed us to insert inject our
research um and and have a really grounded practice.
You know it's funny because as we're doing some top
researchers would criticizes that's way to operational. Where's the
science? And then they turn around and see the
NSF data called R. F. P. S
. That say you know this really needs to be
out of the Ivory towers and engaged with the communities
of practice. Um And how do we do that
? And so all of a sudden we're in a
really good position because we've been doing this. Um
So I'll tell you more about that some of the
other guiding principles here behind what we're doing this this
levels of interoperability chart that's here in the middle.
I want to speak that for a minute after hearing
the last speaker kind of and my experience there in
central I. T. And as a research group
, understanding how we do interoperable inter operation. what
that means. This is from Tokyo it was a
professor Jody. You are modeling and simulation groups and
so it's one of the grand challenges in the modeling
and simulation community would be how how do we kind
of build data and data models and analysis and then
to be able to kind of take those independently and
recompose them and reconfigure them in different ways and still
have the discrete models or data units work as attended
without breaking overall science. Usually we wire up the
workflow that's kind of statistically significant but maybe it's just
barely held together by um you know duct tape and
whatever else you have. So uh we deal with
this a lot and and so it's important because I
would go to our I. T. Folks sandwich
. We need help. This is a big data
problem. We need help with interoperability. And I'm
thinking about science interoperability right. I'm thinking like how
do we get our emergency management community to think,
you know centrally about the geospatial data workflows and how
all the information from satellites and sensors and ground crews
and risk models and all of this stuff can come
together into an analysis and at the same time you
know that engages so many different disciplines And so the
interoperability I was thinking of is a higher level of
interoperability amongst different disciplines or domains of expertise. But
we started out with sort of you know interoperability there's
you know, we don't have a network connected because
once we get up to sort of technical interoperability okay
. We can exchange files, maybe we know what
kind of file format they are and we get into
syntactic upper interoperability and we've got a little bit more
syntax built into our understanding and we're going from integration
to interoperability and then to compose ability through this maturing
capability. We get to semantic interoperability, we're throwing
data over to the next discipline. Do they understand
that we have some kind of meaningful understanding of what
we're exchanging in our data? Um and then,
you know, we can keep going higher, pragmatic
and we actually use that inter operation understanding could be
dynamic and then we reach some level of nirvana where
you can just look at the other discipline and we
know exactly what the other um, domain is thinking
. So that that's one kind of central thing that
brings our work together at geospatial because in geospatial we're
dealing lots of spatial data. And one of the
we collect data, aerial photography or elevation data we
want to use and reuse that for many purposes.
Usually it's paid for by a couple of purposes emergency
management E 911 on your cell phones in Virginia.
There is a uh tax that has gone to pay
for all of our statewide imagery for many years.
And so that's the, you know, officially from
on the legislation. That's that's what pays for our
statewide information um, now, but the reality is
we use that data in hundreds of thousands of other
ways. Uh, and so we think a lot
about kind of how we build spatial data infrastructures that
kind of support that. A few examples, three
D. Blacksburg is a project that I founded in
2000 and nine with a number of colleagues, Nicholas
Palace of Visual Computing, Kathryn Smith from the town
of Blacksburg. And we would bring in every week
different researchers. The problem we saw in 2000 and
8, 2000 and nine was All the people building
three D. City models. But they were the
architects would build a really wonderful architectural model with all
the detailed windowpanes and this enormous file. Um And
the civil engineers would serve a very accurate building scan
and we know precisely where it's d located. It
was, you know, but there was a plane
the digital artists would take some data that they could
find and make up a really beautifully rendered building with
shadows baked into its lighting and it just looked really
good. It was in the wrong place at the
wrong height but it looked good. Um And so
we started looking around all these community and the geospatial
. We're doing that too. So can we can
we help these people um by kind of building a
community around that poor in common framework layers and data
that could be used many many times over. So
if if a civil engineer wants to do a sound
pollution or noise pollution study, they don't have to
start from scratch and say, well how do I
get three D. City model to even start to
test these theories and take measurements or the broadband mapping
community? Maybe it's doing our propagation. They need
a city model um Urban uh sustainability or forestry might
have other purposes. All these communities have different reasons
that they want us a rich collection of players.
So we've we've built that for three D. Blacksburg
. Um And We're currently building three d. Virginia
. Have scaling this up statewide. We've got some
other research infrastructure, this is tentacle. So basically
a web based system that's harvesting lots of data sources
on a regular basis and putting them into our data
stores. Um And we do a little pre processing
so it's sort of analysis ready, ready for other
models. Were doing a lot of these things in
high performance computing. So we've we leverage Blue Ridge
uh and our advanced research computing capabilities here on campus
to to analyze big data collections. I'll show some
of these other slides here momentarily. Some of our
research examples I mentioned three D. Blackbirds, a
very rich collection of interior exterior campus above ground below
ground. We have nighttime lights, we have daytime
, we have every door handle and curb cut.
We collected and built into a three D. Model
can route you from one room to the next on
campus. We did laying stadium and some other really
detailed architectural models that are being used for evacuation,
simulation models and other purposes. So it was just
on campuses are very rich set of layers and then
we expanded to Blacksburg, Downtown Blacksburg. Now we're
working with Montgomery County, we've just launched their launching
right now, the new regional 911 system uh Montgomery
County public safety answering points. So prior to this
or is still currently currently we have Blacksburg Christiansburg Virginia
tech Montgomery County all taking 911 call separately in their
own public safety answering points. So this regional nine
months that emerges those four groups into one and so
were behind the scenes looking at all of the data
from these four jurisdictions And they needed all four of
them have to support lots of legacy applications. So
we we can't just go in there and change your
data model. It should be like this for the
future of 911 in 3D cities we have to support
all these legacy, so we have an extract transform
load and interchange format and put these things into a
common data model and now our regional nine on one
. So it's a great example of operational community and
why did they come to a research group because we
promised them we will future proof this And we will
bring in our best technology is thinking and prepare them
for the future. And so we we think 10
years out, well, first net is coming.
It hasn't been funded, fully funded and built out
. This is a new nationwide public safety broadband network
. They're not thinking about, they're they're still thinking
about next gen 911 which is the current paradigm.
How do we get, how do we do text
to 911 and and get our phone rather than hardwired
phone systems? We have cell phones kind of dealing
with the new reality of the communications infrastructure. But
there's something else down the road that has even more
difficult requirements. Um and so we're helping them prepare
. I've got a few of the other examples in
here were emerging or emerging, uh webcams and twitter
. So we built an application called, I can
see you tweet, you can hashtag VT burrows hall
webcam, It would take a picture of you and
tweeted back to uh those kind of a fun experiment
. We've got two master's thesis of that, looking
at uncertainty in the spatial information and positional accuracy and
trying to kind of do this video analytics and confusion
of information. Lots of interesting implications. Now we're
thinking about monitoring forests and have the forest or the
tree send out a social media message when it leaves
come out in the spring or when they start changing
colors, things like that. So there's lots of
engagement aspects, nighttime lighting, I'll show you a
few examples of that. We've got plenty of daytime
based maps, but what about the night the other
half? So there's lots of interesting things were doing
there. We have we've worked on a 19 state
and this is a five year. And overall about
$7 million across these 19 states. Not all came
to us but argue my group C. G.
I. T. Ran all of the geospatial data
for this 19 state vineyard site assessment and sustainability as
we grow vineyards and wine industry in the eastern U
. S. And that's maturing uh International Wine Review
just finally acknowledged Virginia as a legitimate and high quality
place to purchase it in sample wines. And and
that's after you know, decades of work people like
tuning wolf. So we're coming in now saying,
okay well now people are gonna want to buy up
lots of land and start vineyards. So we build
a site assessment tool that allows you to outline their
field and drill down and understand how climate, soil
topography and growing season conditions might help you um,
understand what varieties to plant at that site. Different
varieties have different requirements. We've got some global research
, we've just had teams go to Burkina Faso and
we're doing some global kind of aggro climate mapping and
modeling. Another really interesting one is our statewide transportation
efforts with the department of motor vehicles and every night
we receive from them all of the crash records that
occur in Virginia, all the crashes. Every time
there's a vehicle related accident, the police take a
report, it goes into a system locally and then
it gets boiled up to the state level database and
then we get a copy of that and we improve
it. We had attributed we we had a we
software engineering, we built pipelines that automate has the
improvement of this data, the positional location of where
the crash happened. Sometimes you have to resolve that
. The lat long that's reported by GPS is different
from the mile marker is different from the street intersection
that is written down in the record. So where
did the crash actually happened? We have to locate
these things the way that 50 ft, uh,
so we can do that semi automated and sometimes,
and then we still have to have a human in
the loop to make some judgment calls. That's been
uh, a very successful project. We have over
half half million records now public web accessible tool.
Um, so we were very cautious. I'll give
some messages here about data management like this, this
one we basically, the first cut we back off
, we said, we're just gonna repackage public data
and put that into a report. We're not gonna
we're not gonna give it a score or make any
too many uh judgments about the land because there might
be a liability there for one. We want to
be right when we when we do make those predictions
. So we're going to improve the science. Um
And so that part made it easy um that we
can just I hope all these movies. Okay.
No quick time. Okay. All right. Well
this was a walk through through the site assessment tool
. Um I suppose I could go to the live
one but it takes a few minutes to go through
. Um And and basically you get you get to
put your information in here and receive a um oh
, a pdf report that comes back to you basically
put in your email address, you zoom to anywhere
in this bounding box in the 19 states, down
to a farm level and outline that farm and say
get report. And then in your email box a
few minutes later shows up about a 20 page pdf
report. It says okay for that site. Here
is the soil and the climate topography and here's how
we evaluate that against the potential great varieties that you
might grow there. And so we have biological models
. We have all sorts of models going in the
background but we boil it down to something that's actionable
information. Uh And so that I apologize about the
movies. I suppose I should have encoded them in
a better interoperable format. Mhm. We create data
. So this is a new data layer that many
people are asking for. These Viticultural areas are basically
legal definitions that helped define the Terror Watch and you
know, the special, the unique kind of regions
. Um There's many of these around the U.
S. In the in the global system. There's
other ways to talk about these kind of viticultural areas
. But basically we went to um the Code for
federal regulations where they've encoded this down. I mean
it's basically like from the from the sign post um
at the Route 52 intersection to the ridgetop is the
first part of boundary and you follow this thing and
it's excruciatingly painful. Nobody's ever done it because of
that. The way these things were written, the
law says you're supposed to use these paper usgs,
topo maps and kind of kick out points. Okay
, well that you know, they came with that
law, you know, 50 years ago. Now
we have digital data and people want these the cultural
areas available shapes. We can do a different analysis
and marketing. Um So the story I wanted to
say here is now that we've produced this dataset we've
documented, we created good metadata. We created provenance
information. How did we do this? Not just
here's the shape file, here's the G. I
. S information. Good luck understanding why why we
made it or how it was made or what's the
appropriate use for it. But the provinces, you
know? Yeah, we went to the CFR so
we wrote all this up and yeah, this is
a straightforward one where the provenance is pretty straightforward but
sometimes, you know, we're transforming of satellite data
, creating output models and derived information. You really
have to keep track of that providence. Where did
I initially did it from? And how did I
treat the data? How do I change and alter
it? And now I can publish it. The
other part we're gonna do here is use the library's
services. We've been talking with them quite a bit
and we're going to give it A. D.
L. I. And put it out there as
you know, something that's suitable for us. We
may not put this into a full publication. I
don't I don't think although our methodology was good.
Um and others would benefit. We just don't have
time to publish that. So A. D.
O. I. Is a great compromise. So
some of the palace six examples we're bringing together three
derived in disease helio thermic index trying this index and
a cool night index to and then sort of principles
component analysis to see where common uh pixels are across
Virginia in this case. So anything that's the same
color has a similar characteristics of cool night. Helio
thermic kind of the sun warmness characteristics and the dryness
and the rain rainfall basically. And those are good
because they relate to grapes. Cool night is great
because when you have cool nights, the way of
photosynthesis works, you want to have cool nights to
slow down respiration, otherwise you're losing a bunch of
biomass and all your secondary metabolites that are the tasty
parts of the wine and the grapes. So having
cool nights is good, preserves those and builds the
secondary metabolites and so on. Each of these has
a reason that we implement another movie here. This
is our global, So I could look on it
, but it's probably the same story. I'll try
it just because I might have some MP four MP
3 formats, No quick bans on bills. So
All right, What this tool do? This one
I can go to this one is a little bit
safer. Doesn't take as long to drill through as
the site report. This one is a dynamic mapping
and modelling tool, basically a landscape analyst if you
wanted to do, if you wanted to give any
one of these factors, uh soil thermal regimes,
moisture regimes, that transpiration a score. Like for
my system, I'm worried about grapes. You what
we're building is a platform. It could apply to
corn or peanuts or other agricultural crowds. Mostly we're
focused on plants, but we have worked with some
with wildlife site assessment and species ranges. But basically
you turn on and off layers here. Mhm.
And I'll just since soil is highlighted, all try
to open that and just show you an example.
So we have global kind of data layers that we've
curated, put in a common projection and published on
our web mapping servers here at Virginia Tech. And
so you can you can, this is the explorer
part. You can map it. You can zoom
in and out. You can you can pan and
zoom around the map. Um So there's a lot
of layers in there. We can add more.
The modeling part is more exciting for me. We
can start selecting parameters and this basically walks you through
that process or supposed to there there. All right
. I'm interested in terrain, slope and terrain aspect
. There's a lot more here but I'll just for
the purposes of demonstration of quick, quickly move through
that Now on the slope, I can give that
await you know what if I were to if you
think about the world is a bunch of layers stacked
up in each layer representing different variables or parameters.
Um And then we had pixels representing discrete areas on
the ground. Um and we could give each of
those layers and each of their pixels scores based on
their values. So terrain slope is that 0-1 slope
? 1-2 slope to four, although to these really
steep slopes. So how do you want to score
those here? One is a low scoring nine is
a high score. So this default scale says a
really steep slope. Is it is a highly valuable
thing. You don't get a high score. So
I would probably, you know, for grapes,
I would move that way down. That's, you
know, dangerous to even plant on. Yeah,
people do grow grapes on these really steep slopes with
some caution. And then as we get into these
, like uh, these middle range is a steep
slope is actually a fairly, a moderately steep slope
is very good invented. You want cold air to
drain off, not to kind of pool on the
site. Actually do want some movement. Um So
we would actually wait um These these slopes a little
higher than we would have flat area and I can
say okay that's um 40. We've done some statistical
analysis, we know that based on when I include
these two variables, That one has a weight of
40 and terrain will have a weight of 60 and
then I can wait those two. Um The terrain
aspect is that north facing, South facing and so
on. Um And then I can just run a
model and get a result display dynamically. So now
it's going to the web server and telling you all
these parameters. And on the fly we're taking that
raw data and recalculating that into a weighted overlay.
What's the smallest pixel size on the second feature.
Well in this one, in this global model we
kind of went with a one km in Virginia were
doing much higher spatial resolution. So we've kind of
got different scales when we did. It's a global
Sort of one km big pixels. So that's not
that useful for a very specific site assessment. The
reason we built this was more about kind of exploring
, you know, could I possibly plant this new
variety of corn or soybean or exotic new vegetable crop
? Where else we've tested at this field site?
Where else might it grow in the world? I
want to find similar sites and then the way I
used it for vineyards, the weather marketing vineyards these
days is in Virginia is reputation borrowing. So we
want to say in Virginia that were like bordeaux France
that would be a great reputation to borrow from.
And so I can prove that with these kinds of
analyses that were actually similar in climate, soils and
topography in the continental alito and a couple other factors
that might be important to have evidence to say,
yeah, we can borrow that reputation because the antibiotic
and biotic factors are similar and the varieties we grow
might be similar. It's basically that's that's one way
we use it. And so in the Eastern Us
, the Eastern U. S. Project, the
scale of analysis area, get back to my slides
that one. The scale for the eastern us is
about a 10 m pixel. And then in Virginia
we have a task right now to get that down
to about a four ft pixel. So we're getting
better and better and because we have richer large data
sets available in Virginia, we don't have those for
the entire Eastern US for the entire globe. It
would take a lot of effort to kind of put
that together. We can try and we've got a
couple in sf big data grants out right now.
They're actually in development. This is kind of science
, a little simplistic version of the model that's behind
the scenes. The site score is the sum of
all the criteria, soils, aspect, climate and
the weight of that criteria times the product of all
the restrictive layers, which means if you multiply by
zero, you basically get zero. Right? So
if you have something like you just can't grow on
, that's clay. Okay. That's a restricted player
. So we're gonna, those are the red areas
. We give that a zero. So that's a
very simple way to show a little bit of the
what's under the scene in the mathematics. Here's an
example of how we're using the high performance computing.
So we take our weather station data and we take
um long term climate data, which usually is a
spatial climate products. So you'll understand if the north
slope in the south slope over a long term have
different uh temperature precipitation regimes. Um And the but
the instantaneous data that we get from a weather station
right now, how do we how do we take
those points and make a broad scale wall the wall
map coverage. Uh So that's what we're doing here
is basically fusing long term climate and instantaneous weather into
a climate aided interpolation. We take those points and
now we can interpret it based on the long term
plan. Um We have lots of other data,
soils, terrain, climate of the main categories.
The other thing we do now this is a modeling
and simulation and grow 10,000 years of grapes across the
eastern us all varieties about in about an hour on
our high performance computing system. So I'll show you
that in a second. But the simulation models,
this is important. These are kind of static layers
, the soil climate departure. But what about the
which varieties we're gonna take And how do we select
? So we run simulation models, then we bring
all that together into a multi criteria decision analysis.
We can do a more robust statistical assessment of that
and then basically present the results uh and so processing
the data is happening in high performance computing. These
are some of the example outputs we get from the
simulation model. Now this is just intended temperature interpretations
. We create daily weather services in this case and
use those daily team in and T max surfaces to
drive biological models. So we know we have we
have biological models that would describe how these different varieties
grow over time, their physiological um at stage of
maturity, from bud break to maturity and all the
stuff between when the the grapes turn purple Eurasian.
And so we have kind of we we have models
that predict all that pretty well. So we're simulating
that. We're simulating that in this big HPC system
here is reasoning the day of but break day of
year is we can predict that. And then we
have ground truth where we validate echo dormancy. When
does the great plant the vineyard go from becoming being
cold hardy in the fall to the early winter and
then it accumulates enough heat cold units that it switches
over into an active physiological growth. So we can
predict the day that that happens. And then we
can predict other things like the number of years for
chardonnay that, but Break actually could occur based on
the growing season requirements. And you know, so
short is gonna be great in these northern latitudes.
But then we're kind of on the fringe here if
we get too far in the south north Carolina and
south Carolina. Here's another example of the data,
the weather data. And for one particular site,
one pixel we're looking at the probability of different temperatures
occurring on a particular day of year. So this
is only about 12 years. So it's kind of
, we can smooth this little better. But this
is the raw data and probability of certain temperature thresholds
that gets fed into this is what we're building right
now the next couple of months um to be able
to bring the technological information, the growth stage information
these plants, you see different race so we don't
block freezing or below. And when what day of
the year? On the X axis? What day
of the year today reached? But break you see
, we've done a model here, we've got sort
of the mean and the median and some of the
um extremes for all these things. So what we're
looking at here is on that day of year,
what's the probability of a certain freezing temperature going to
make sure that we're going to plant varieties at this
particular location and they're not gonna but brake too early
when you have frost risk or in the summer is
going to be too hot in the summer. That
can damage the great so the probability of reaching a
certain temperature in the summer. And then did we
make it to maturity in the fall? This is
the big one that we've been using to make some
recommendations. Sauvignon blanc at this site most years,
you don't reach maturity. These other varieties did reach
maturity earlier, their short season varieties and others take
longer so we can we can start that's you know
, the end of season happens here in this Frost
kicks in so we can use that kind of information
to help inform our decision makers. I'm gonna skip
a couple of things there and this is also a
movie about our vineyard supporter. This was going to
be a key part. I'm gonna try. It
was actually, I think it is encoded in a
different, basically a message and the message on the
same but we just got what's going on the message
that okay, this is our vineyard portal. So
now we're asking growers to go in now we're getting
personal right now. This is where we get into
the personal and privacy data issues. We're asking growers
to go to our vineyards portal. It's in beta
right now and tell us the outline of your farm
registered first. You got registered in our system,
you create a user account that's unique to you.
And then it's like a my vineyards, like here's
my vineyard number one and you go on a map
and you outlined the site and that site information is
stored with that user information. We get even further
. We do blocks and rose and plants. You
can get all the way down to very specific levels
of exact plant. I'd plant which plant is planted
where um, that's very useful for analysis down the
chain down the road. Um, what this also
sets us up to do is push information back out
to the users now that I know where their vineyards
are and they've agreed to our terms, uh,
can push information to them. I know where their
field is. I can drill down through our daily
models and currently I don't think I showed it yet
, but we're running hourly disease forecast models, five
diseases that occur commonly on grapes farmers want to wake
up and have this in their email inbox this information
that there might be a risk and here's what you
might do. Um and so this is a great
example of why now we're asking to collect their private
information, store what they might consider proprietary information.
So so basically in order to launch this, we
did a whole bunch of users interviews and studies.
So we got an I. R. B.
Approval and had a process where we talked to different
producers regulatory people, um consultants and um vineyard owners
, vineyard managers, winemakers and ask them, you
know, what are your concerns about your data and
your privacy and because we want to use it for
science if they would just give us a little bit
more about their yield information or their quality information not
aggregated, but really down to a specific block of
grapes and or even a row or plant. That's
what's going to really drive our science to the next
level, but there's some concerns about that. So
were carefully navigating this and building the trust, making
sure look, we're asking our community first, you
know, what are your concerns and then how do
we address them? Um, and so I've had
uh fortunate to have Peter radical PhD student in computer
science who's doing his dissertation on data privacy. So
he's helped quite a bit. It was their questions
. Do you consider the 6th and final this part
. There is a citizen science component. This not
this part right here, but we do have some
citizen science project, one that I've been working on
with john Mansell, we got a extension innovation grant
to look at forest farming in Appalachia ginseng cohosh blood
root. How do we turn that into a new
industry for these folks that have forest plots and maybe
don't want to chop down, do a clear cut
or selective logging. There's alternatives. They can they
can grow some of these medicinal herbs and as the
regulatory side of all those medicinal herbs comes into place
, there'll be a legitimate market there. So john's
building a really nice um kind of capability. We
just like all of our extension stuff. We've got
to show the farmers how to do this and give
them best practices. That one. We're we've got
a citizen science grant where we're asking people to go
out and collect very detailed information about their ginseng plots
. Just highly prized, valuable uh information. There's
lots of people that coach ginseng. So now all
of a sudden we have a latitude longitude And then
basically our procedures interesting. We've built smartphone based protocols
that go out in the in the patch and we
set up a couple of markers and then we have
them take about 50 photos walking around it, walking
around the patch We can do structure from motion and
turn that into a 3D. model and later count
the plants or measure them or estimate biomass. And
we also have them take a soil sample. They
also do a photo sphere from inside out. So
we get an entire photo sphere. We can derive
canopy closure metrics and some other information. They do
some soil evaluations. So they're filling out these citizen
science forms. It's a competition called the plant shoot
competition. And uh but yeah there's there's some really
sensitive information there so we're going above and beyond to
make sure that we have all the right data policies
and security policies in place. Uh, so yeah
, it does relate to citizen science and with our
vineyards we are going to take it there. I'm
not even gonna try these movies. This is our
great disease modeling. So we pull in a ton
of weather data every hour, package it up in
the net CDF data cubes and and then kind of
store that we do a lot of pre processing and
get it ready for our analysis and then we put
on the server, we run these models and then
we have the outputs available on the web. Um
There's some fancy things going on behind the scene that
I won't talk about too much. Um, but
again, basically now if I had that users um
farm location and I had these disease models, I
can push the information out that's what people want,
they want to push to them. Okay so we're
going to do that. I'll switch gears a little
bit into some of the mechanics of how we do
it. Um And then maybe this is just an
example. We published a paper on this. Our
problem was we had lots of geography majors great at
thinking about geography and maybe they're okay G. I
. S. And some of the technical aspects.
But when it came to high performance computing and command
line big data processing it wasn't happening. It's not
the discipline doesn't focus on it. We needed computer
scientists but peter scientists didn't know enough about the geography
and the content to really be effective. So what
do we do? So what we built an interface
who are really easy or it's not really easy but
our G. I. S. S. Every
kind of um standard desktop mapping software that's used extensively
and we built an added so from your from your
comfortable, comfortable place of your desktop G. I
. S. You can load up your raster dataset
and hit start execution and the analyst has no idea
that just disappears for a minute, goes into the
big job queue, we do a multi you know
big parallel compute on the analysis and send it back
to their desktop and it loads right back in the
dark map interface. They you know, they just
did a supercomputer job and just by pushing a button
so that's you know that's how we want to enable
are some of our nontechnical users that might be experts
in other parts and that's you know that's that's kind
of a big focus of C. G. I
. T. We're bringing together computer scientists, geographers
policy and planning, electrical engineering. We've done you
know, other we've got probably 10 other disciplines in
there right now. And so it's really fascinating.
Here's an example of for the ipad cube, you
guys have probably seen the tornado maybe the tornado simulation
in the iCAC cube. So as part of the
team and this is the pipeline I put together that
we would be able to take of that cast screen
the way we compute on high performance computing, we
can put other libraries there. It doesn't just have
to be for elevation processing or whatever. It could
be. Lots of things if we have the data
. Right? And the models, right algorithms.
We're basically using this pipeline this platform To produce 3D
scenes from your raw data and your user specified parameters
, push of a button, big compute output um
and is ready to go ready to visualize. Yeah
. Uh this is another interesting example. We we
ran this for a few years. It's no longer
active because we're focusing a little bit more than three
D. Virginia. But this was crowdsourcing. I
described the problem of three Blacksburg earlier and so the
way we solve that was to say, okay,
here's a web interface. If you want to do
a three D model of a building, come here
first. We'll give you the best available terrain,
the authoritative terrain and imagery and building footprints and other
contours information. So you basically click on that building
, you get a zip file with aerial photos,
contour light are you can bring that into sketch up
or Unity or whatever your 3D modeling environment is that
you're comfortable with or even. And build your light
are points that show you how high the roof should
be, building footprints that show you where the building
should be based on our best authoritative data. Once
they finish that we put a creative commons license on
it and load it into our common um model library
and then other people can use it as a uh
creative commons with attribution licensing. So if you're a
model or you do something for us, that's awesome
. Thank you. And we're gonna give you credit
your names in the metadata for that building file.
Um And so we've we've kind of put three D
. Black burger in different um environments, multi user
environments, immersive environments there in the cube. Um
That's up in Ferguson and we're using that for several
studies. I'll show you one other example of how
we're visualizing things. This is gonna take some of
our three D. Gps data And downloading 26 some
plus megabytes and it's gonna be a little bit more
64. And what we're going to see is a
three D. Campus model with our tree later in
there in a moment. And so this is uh
this is relatively new technology because it's hard, this
is just a web browser, I just clicked on
this thing. How am I gonna get a three
D. File to load up so that there's lots
of technical things going on behind the hood under the
hood that are enabling this. So there's our campus
model and you can fly around it, it's gonna
load in the textures in here in a minute slowly
. Um And we'll just choose some mail. Hit
play I think should be safe that will start flying
around so I can stop at any moment here and
turn on and off layers. Say okay I forget
the building. So I just wanna I just wanna
visualize the terrain or just the trees you can interact
with in different ways. Like in another for kenton
farm, I have one with a swiper and I
can swipe between different views of the tree models that
we have there from the Silver pastor experiments. Um
So that's, that's our three D. One of
our three D pipelines. Um Here's another one.
I'll show this one because it relates to the library
and special collections. Um the original initial page,
I made sure this is gone, but this is
a 3D view. We took the historical photo collection
of the library and I built a multimedia database that
said, hey, what if we added lat long
to these photos and a timestamp. Time begins,
time ends where it is a time span and a
few other metadata fields. And we can drive a
geo located multimedia library that then takes those photos and
in our three D worlds it loads them right into
place and you can have different queries here. We
have a way to do annotations and so is our
prototype for if we're flying around a three D city
model trying to decide what to do next, how
do people comment on that, contribute or discuss it
Or annotation tool allows somebody to collect on something in
three D and make a note. And then anyway
, this is really slow. But when we load
in here anyway, that's behind this. I don't
think they're actually no word. You know, it's
still loading parts of it. You should be able
to click and get a photo but I'm gonna run
out of time. So I show a couple more
things about data. This is probably just another example
maintain humanized landscape architecture professor. We started the night
scape research group. I'm about to do my third
flight over Blacksburg at night, third year in a
row. So the first year there was also the
collagen lights you can see here in orange. The
second year they started replacing them with led uh bolts
on campus. And I think this this spring when
I fly it's gonna be all LED. So this
is the problem. Like we have plenty of daytime
base maps. But what about the night time?
And so we're kind of taking geo referenced nighttime imagery
, putting it in the same coordinate system as the
daytime base man. Now we can use this for
different things. We're using it for, you know
, we can count the lights, I can see
the mercury vapour lamps and sodium halogen lamps and LED
. Or the Cfl's and Spectra. We we can
start to distinguish those based on the spectral signatures and
classify them. We can overlay crime maps or we
can do energy assessments. Uh, there's many other
, this is kind of areas of research, building
up use cases and the value of nighttime research.
And Nasa was here this week next door and there
, you know, I talked to them about this
when we started five years ago and they came back
to me and said, hey, you're still doing
that night scape. What's the value of it?
Because we're ready to know. So we can tell
Congress whether we should put this nighttime mode in and
turn it all the way up. Um, and
is it useful at this moderate resolution scandal or do
we need high resolution? So there's this is research
that's um going to possibly lead to a new type
of data that I approach the commercial companies to G
. O. I. And they basically said engineering
constraints, we can't image daytime and nighttime. The
sensor gets too hot. We don't have enough energy
. We didn't design it to do that. So
maybe maybe next next satellite three d. Virginia.
This is we've taken all of the lighter data,
laser scanning data. It's about it's about 30 terabytes
of data right now. And we're putting this together
into three D. Virginia. So what we did
for Blacksburg, and we've we've automated some processes now
that extract buildings and trees were going to do that
for the entire state. Um It's gonna take a
number of years, but uh we have funding to
do it for our broadband modeling. Uh So we
can do our f propagation models. We have a
tool box that says, hey, I wanna drop
a tower, what if I dropped an antenna and
a tower here? Uh What kind of coverage would
I get do box will based or a signal strength
That was after three or four years of working with
our electrical engineering, wireless Bt folks. And we
kind of figured out enough to be dangerous and we
have better spatial data than they do. So we
can in the end it's weird, but we can
get slightly better our model than some of our electrical
engineers and in a very pragmatic sense and I'll always
put a for planning purposes only please. Um,
their liability. Uh, and you know, it's
Fox will base. So now we're talking about this
with the drone people, the U. A.
V. Folks. Well, when it is your
drone out of range of the base tower, um
, Here's our crash analysis. 566,000 crash records were
using three D. Virginia to analyze that these hotspot
intersections where there's lots of crashes, maybe there's a
visibility issue. And so this is the 4 60
North main area. As you're turning the one on
the left bottom left, your there's a vehicle turning
out onto 4 60. And the problem is if
there's a big truck barely down the mountain, you
have about seven seconds to react to that and you're
gonna make a left turn across traffic. Okay?
So last year somebody it killed and the delegate um
for the of joost, um, put in some
legislation now also we're going to fix that intersection.
We have scientists. But our question from a big
data perspective was how many other intersections are killing people
because of these kind of situations? And why did
you know this one got attention because somebody you know
picked up the phone and started pushing the case.
But how do we, how do we wait all
of these crash. This is a map entirely made
up of crash points for the state of Virginia.
Can see all the roads and back roads there.
Uh We work with Department of Emergency Management, pulling
in lots of real time data now that this was
others Through the date, October five they called me
, it was friday night at 10 o'clock and it
was like, hey we have, we think there's
gonna be a landslide and Floyd or Patrick county,
can you help? We just ordered a bunch of
satellite imagery. Okay, okay of course I'll help
for that. Uh And so high Tao Wang were
my faculty and I've worked through the weekend, we
produced a 40 page report in three days and analyzed
the landslide risk uh for these areas. Now we
did that as sort of, you know, uh
let's let's get a good example of big data and
and how we might help Virginia, Department, Emergency
Management and now we're in a good position to get
research funding to do that because we work directly with
that operational community that has a very real need And
we have the data and tools to do it.
Basically what we did was took the rainfall analysis the
soil that I already showed you the aspect, there's
models that bring in probably 20 factors. We only
had about five or six of them but um the
other one we hit was that was interesting was uh
this again this is probably a movie that won't play
. I apologize for that. Most of my presentation
were good movies. This is supposed to be a
vegetation index over time. And basically It was,
this starts in 1984 and goes all the way to
current from land sat from satellite imagery. We get
how green is stuff on the ground and then how
does it change over time? And if we see
a sudden change of a forest stand disappearing basically.
Now we got exposed soil that's very high risk for
landslide. If if these other factors lineup, there's
been there's soil conditions and slope and things that might
line up. Yeah. So that's how we're applying
the big data there. Um, I think I'm
gonna remember that. This is another way we're using
data. This is the Hokies Thank the world event
after the Shootings on campus in 2007, we organized
This is me on the 16 blocks cover. We
had a satellite plane, helicopter, bucket trucks.
We got 5000 people are out there. Sorry about
that was that was Eddie McClain easy and he wrote
a song about this whole tragedy when tragedy strikes.
And it's really uplifting. Well, let's go now
. But this is a picture from space made up
of about 5000 people on the drill field. There
was a time lapse from birds hall. So lots
of creative ways to engage people. And this was
sort of also for for many reasons, we did
this to send a message of thanks to the world
of support. Um But you know, part of
it was also things were working on before the tragedy
happens. How do we, how do we get
satellite industry out there? How do we get people
in the loop and engage them? This is another
one that we did. We took all these,
this is the flight, best 10 design. We
took all these sheets that were donated, died in
different colours. Um and mapped it out. And
then took a picture from space of Floyd Fast 10
. You can see the design of the phoenix there
and this was a poster that together that's the sun
stereo satellite which takes pictures of the sun constantly from
two angles. So the whole story, it was
, you know, pretty out there, but you
know this is the sun at the exact moment of
the picture and you know, we told the story
of all the energy and the wavelengths kind of moving
from the sun to the earth back to the satellite
and back down. And so kind of cool story
to think about how the data works like that.
I probably don't have any more time. Um So
I'll probably show you the thing that zuko probably wanted
me to show in the end, uh which is
we've been working on our privacy um and security practices
. So the way I did it, I said
, well we have three major projects with privacy and
security concerns. Someone of the citizen science ones are
vineyards and our broadband work. We're in our broadband
work. We do some expert sourcing and crowd sourcing
. We asked localities to give us to our data
will manage the data, but it's really they own
it. And so we have these kind of cases
and so we basically went through this thing and okay
, you know, as a common set of best
practices for the consistency of collection exchange of data across
our applications and stakeholders. Um and so we wrote
first before we got into specific policies for specific applications
, we first wrote a best practices doctor and I'm
willing to share that. This is the draft version
of it and it's it's just about out of draft
, we keep tweaking it, we're looking at the
White House initiatives on citizen science and we want to
use some of our work as a case study for
some of the work that's happening there with EPA in
the White House. Um and anyway it's a massive
kind of collection of things and motivations first, but
then we get into all these portability in terms and
disclosures and who really owns the data when it's citizen
science who owns the data, we asked for data
so you have to be very explicit about these things
and tell people what you intend to do with your
data and how you're going to store it, how
you gonna use it and um do your due diligence
and that helps basically build trust. It also covers
your ass because if something goes wrong you've got a
policy but it you know, I think the most
important part uh for our end users who might say
yeah any any server could be hacked um And you
lose your data. We can't prevent all this but
we have to raise the bar we have to do
the things we can. Um And then um But
but really the important thing is it builds that trust
that they trust us with their data because we've gone
through the hard steps of thinking about the privacy and
security and locking down the servers and doing the I
. T. Security audit. We just went through
an internal audit and past that. And and so
the security side is okay it's enough. Um And
now it's really the privacy side which is really hard
to navigate. You really have to understand the application
and the user community and intended purpose. I've just
got a couple of minutes so I do want to
leave room for questions. But yeah this is a
large document I'm willing to share this, a lot
of good work that went into this from my team
and especially peter addicts, who is the PhD student
working on data privacy issues, he's building a tool
. So we've we've kind of got this as a
template and then we turn that into the specific policies
for our services. So for our broadband mapping and
modelling, which is a huge suite of tools and
data sets, um we came up with very specific
terms uh that work for that suite of applications.
And so this is, this is uh that's kind
of what we end up with and we put this
on the website and it's one of those long things
you scroll through and degree hopefully, but if you
read it, it's, you know, it's all
there and we're doing the best we can to follow
the best practices. These are emerging practices. You
know, we sort of wait until something goes wrong
and then we make a new policies federal or local
. Um and sometimes we can anticipate things that might
go wrong and head those off before they occurred as
well. Uh So yeah, a couple of minutes
for questions maybe. Okay. Mhm. Yes.
So it's clear that you deal large large man.
Yeah. Uh Do you would you recommend any resources
good resources for effectively linking databases with geospatial uh frameworks
that's not something that classically done but seeing colleagues do
it it seems to be really helped. Uh So
you have a specific example because yeah the answer is
yeah there's lots of ways. Yeah. It sounds
like it would be more helpful for you to.
Okay. Well possibly. Yeah. Can say information
on street networks and and it's the aquatic yeah,
Within those streams. Yeah. Yeah. So you
got researchers collecting all this wonderful data and then it
goes and lives in their own hard drives. Maybe
their students use it if we're lucky these days they
published their thesis or dissertation and include the data and
the models. But we're still getting there. Um
But yeah I think with the library we have a
meeting next week. Um Geo Black Light I thought
yeah there you are. Yeah. And so that
you want to answer that one. There's the library
has a solution that we're putting in place that will
help create the metadata, the search and discovery and
the sharing with other universes. Other library institutions looking
up the game from using individuals. Oh yeah.
Okay. Okay. So on the levels of conceptual
interoperability level two and 3 is what your work.
Okay. Um yeah there there are some good standards
. If you have good data manipulation techniques, you
basically have to know kind of the fields that you
would encode and there's many choices. There's no one
standard for all of these things. Uh teach a
data discovery class, having a little bit more in
depth about raster and vector data and vector data points
, lines, polygons. And how do you properly
? There's kind of some international standards. The open
geospatial consortium has standards about how you might encode those
and represent those. But yeah, I mean a
CSB as long as you got lat long and the
other attributes. That's a very simplistic way. It's
more than that long that leads you down a bad
path sometimes lat long. Oh, in what projection
? What coordinate system? What's your representation of the
earth? Is it a sphere of flat earth or
that you use A G. OID or is this
a three D. You know the X, Y
and Z. So when you get to the mapping
experts there will be some problems with just lat long
unless you are explicit. Yeah. This isn't a
world W G S 84 projection using the 1983 data
and stuff like that then we know. Okay I
trust your lap long decimal degrees now a little better
. Uh Yeah there's rosters and vector files and there's
many different ways to encode that. It's a constant
source of discussion and debate as to what's the best
. Um So we're it just depends. I can't
really answer it without having the very specific example preserve
. Yeah. Yeah. Okay. But we help
people with those kinds of problems to the last night
, like people collecting road condition assessments, it was
like the protocol had seven different ways for them to
collect the location information. So that was poor design
at the end of the day. What we had
was a big spreadsheet with seven different ways the location
was recorded and we couldn't really conflate it with our
road networks very easily. It took us a lot
of work to say. Okay here you described address
range and here you had a lap long and here
you had a of a more narrative description of the
place. How do you do you bring all this
together? So at design time think about those things
before you go out and collect data. Can help
a lot. Yeah lunchtime you talked a little bit
about the intention of creating an actual original data published
. You talk a little bit more about the intersection
. You retain attribution of that material. Yeah it's
been it's been a problem and I think the library's
hoping to solve it now with yeah if we can
have a D. O. I. On that
dataset and a stable place for that to live for
some time. Uh that can help quite a bit
and now yeah, when I send that to my
colleagues I can say and you cite it this way
please. And then we still get some credits.
Not maybe a peer reviewed pub and maybe we are
grant funding or are where we put our time is
not to that like yeah I could write a million
papers right now and not innovate on anything for the
next five years. I'd rather innovate and write fewer
papers because I'm not about I'm not tenure track so
I don't have to do it. First of all
we want to do it because it helps our credibility
and get it out there. So we published strategically
, we don't publish everything that we do. So
these are nights for our data sets. We put
a lot of effort into it. We're working on
a project with Phil Radke in forestry, he had
a grant, he collected legacy biomass data from all
these first literature sources. People have done research for
the last 100 years. He collected all this stuff
and we're helping him now published that. We're going
for a you know there'll be a peer reviewed publication
and then the data will go in the library with
the D. O. I. And a versioning
kind of system because we might change the data later
so we wanna version one version to keep track of
providence there. So yeah there's different strategies, depends
on how important the collection is. Yeah in this
case it's like okay yeah that was it was about
30 hours or 40 hours of work to do that
, and maybe a little bit more. By the
time we got a really robust method and had collected
all layers and built a couple of convenient tools to
help us do the thing. Um yeah, we
could get a paper on that, but um we're
sharing the data first and moving on to the hard
questions. Thank you, enjoy your day, mm
. Okay. Mhm.