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all right hello it's so exciting to be
here at the grand opening of the earth

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at night book which our team I'm the
science PI of the black marble project

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which is the data set that you see
behind me and it's sort of that the

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theme of the book this year which has
really has been exciting for our team to

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work on obviously this imagery is so
sexy, um, it's beautiful to look at but it's

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also I'm hoping that this talk gives you
some idea of the the depth of the

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scientific insights that it can also
give us besides just being a bunch of

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pretty pictures but actually I'm going
to start by looking at the Blue Marble

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which is the first slide that's going to
come up here this is the next-generation

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Blue Marble it came out like less than a
decade ago and this is a composite of

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the earth during the daytime and cloud
free so we can see really interesting

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signals with this blue marble things
like changes in natural land systems

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like the change in forest green up we
can see changes in the ice caps etc but

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we what we can't really see with the
Blue Marble or what you can barely

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perceive on this map is urban areas it's
like a tiny little blip on the map 3% of

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the global land area and yet it has huge
implications on planetary health so if

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you can flip the slide, urban areas even
though they're only a tiny land area

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they produce 80 percent of the global
GDP they house more than half of

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humanity and they also produce 76
percent of our carbon emissions so when

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you're thinking about what happens in
this tiny little and land area it's

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actually going to be the site where the
interventions of bringing about the UN

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sustainable development goals have to
happen it's places like this that house

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millions of people who live in slums
it's places like urban areas that are

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the drivers of planetary changes that
we're seeing

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in the blue marble and so in some ways
the black marble is like the yang to the

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blue marbles Yin it is it is the human
stat to our natural land system stat of

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them of the MODIS so this is an imagery
of black marble before the clouds are

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are taken away black marble is derived
from a sensor aboard Suomi NPP called

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VIIRS, it's the VIIRS day/night band is
what you're looking at and the that's

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been out since 2012 and since then our
team has been working really hard to put

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out two products one is a top of
atmosphere reflectance which looks like

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this it has all the clouds and
everything intact and then the second

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which is coming out in the beginning of
this year in just a couple of weeks is

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going to be that black marble composite
that you see it actually it'll be daily

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but with the clouds removed with the
moonlight removed with all of these

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aberrations that Mar your understanding
of urban this is Cairo removed so that's

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a really exciting upcoming event for us,
next slide, so this is not the first

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night lights data set that's been out
there it's the first scientific night

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lights data set so before of years there
was DMSPLOS which is a meteorological

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satellite that NOAA used to try to
understand clouds actually, so that's

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been out since the 70s it's been digital
since the 1990s this is the picture of

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Cairo from DMSP and you can see that it
has a pretty coarse spatial resolution

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2.7 kilometers also with in Cairo you
can't really see any differences between

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the center of the city and the suburbs
but it has been out for a long time so

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it's good for that long-term record if
you look now this is VIIRS it's almost

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like putting on glasses and compared to
DMSP you can really make out the

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different heterogeneities within the
urban area you also have a higher

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spatial resolution at 750 meters and
maybe what's most important is there's

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calibration across time which
means that from day to day we're getting

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a signal that's stable and that's in
part due to a lot of the algorithmic

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algorithmic developments that we've put
into place in the last year but this is

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going to be useful for a whole new set
of research questions that DMSP

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couldn't even touch next

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so but of
course it doesn't look like that pretty

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picture when it first comes out of the
sensor it looks more like this here what

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you're seeing is different swaths the
moonlight reflecting off of the desert

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isn't taken away and so the cities don't
even pop out when moonlight is there so

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a big physics challenge for us was to
try to even understand how to understand

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the physics of nighttime radiance
reflectance etc if you go to the next

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slide I don't want to get into the weeds
too much but I do think it's important

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to understand kind of what the algorithm
is doing there are several sources of

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light in addition to urban light which
the urban light is shown in red here you

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got the moon contribution you also have
the reflectance off of the atmosphere

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and off of the ground
for example from day to day I was

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looking at Texas once and it was in
December and I didn't even think but in

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one day the radiant shot up really high
and I was like what could this be it was

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because there is a freak snowstorm in
Texas so of course the reflectance of

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the ground really matters for how
streetlights reflect off of off of the

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ground in addition the vegetation
occlusion of streetlights can really

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dampen the signal so we have to remove
all of these artifacts in order to get a

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stable understanding of what what night
lights are otherwise you think

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something's happening when something
really isn't happening next

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right so

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this is the this is the global composite
from 2012 it's gonna scroll over and

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show you 2016 we now have all the way to
2019 and we have it daily you can't

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really see big changes here when it's
like in this format so the next slide is

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going to show you a colored version
where the areas of the

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world where there are new lights are
shown in purple and where lights have

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declined are shown in orange and I'm so
clear regional patterns start to stick

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out if you want to turn to the next
slide you can see India Lower South East

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Asia having lots more additional lights
this is largely because of

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electrification that has been happening
there you can also see like Puerto Rico

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a huge loss of lights from a lot of the
storms that had happened there so some

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of these regional patterns start to come
out we we actually can't explain all of

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them we don't we don't know because
we're not experts in each area but

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that's kind of what's exciting about
this is it's like objective data that we

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can talk to regional experts and really
start to understand what could these

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these different signals mean I mean ice
and actually I think you can skip over

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this one this one's just a slow version
right so I'm gonna just sort of talk

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about two different kind of areas of how
we've used this data to look at new

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science questions the first is to
understand how planetary changes are

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impacting urban areas and this has
mostly been in the area of disasters

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this is an image of cyclone Edye which
hit Mozambique this past year actually

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that area of the world was hit seven
consecutive times this hurricane season

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and not unlike many other places in the
world there's been escalating incidences

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of these storms and combined with the
poor infrastructure and the low quality

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housing in Mozambique even though this
was only a category two storm when it

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hit land hit landfall it killed over a
thousand people so it had a huge impact

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next slide so while this storm was
happening black marble was one of the

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only datasets that was available to
really show the impact so we worked with

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the World Bank and other recovery teams
on the ground to try to help them map

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and understand where the biggest outages
had occurred and also where recovery was

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happening so this picture on the right
is Mozambique before cyclone Edye on

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the left is 10 days after the the storm
hit and you can see that the Manga

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region the Micucci region they were all
very, very hard hit by this storm and so

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not just the before after but we can
also progressively track how recovery

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efforts are doing which is a good way to
keep and understand whether the the

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investments that are put into recovery
are having an effect next slide it's not

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just disasters like hurricanes and
cyclones that we can monitor also

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geopolitical conflict and disasters are
another application where black marble

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has proven useful these areas of the
world and you can keep going are really

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hard to get on the ground data because
because they're involved in conflict

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this this first picture is from 2012 and
then 2016 and then changed on the far

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left and this is the Syria and you can
see areas that were totally demolished

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by the conflict in Syria so this is like
a spatial distribution of where battles

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took out the electric infrastructure in
the country during the the war next and

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it's not just the spatial distribution
that we get we also get the timing of

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these events so this is um Aleppo Syria
and you can see that drop that just

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happened right there in 2012 that was
the Battle of Aleppo which was a major

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battle in Syria and in Aleppo that
really it forced hundreds of thousands

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of people to leave the city and so we
can also see whether there whether

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there's a recert whether there's people
coming back whether the infrastructure

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is um is repaired and as you can see in
Aleppo there really hasn't been much

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recovery
unfortunately next at the same time we

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can also see signals where many of these
migrants are going so this is Zaatari

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refugee camp in Jordan houses 30% of the
Syrian refugees in

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Gordon and you can see the increase in
lighting over Zaatari as that camp grows

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and this is not just a people signal
it's mostly actually that the increase

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in population in Zaatari was happening
mostly in 2012-2013 this is the delayed

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signal for when infrastructure was
provided for those people that are still

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living in the Zaatari refugee camp also
an area where on the ground data has

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been hard to get so next so the next
area that I want to talk about is how we

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use black marble to understand how urban
areas are changing the planet not how

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the impacts within urban areas and so
this is you can you can turn the slide

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so before we scrolled in and we saw that
electrification signal in India you can

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see it again here in close contact so
this is 2012 2016 and the change here so

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almost all of India is purple and that's
because in 2012 over I think it was over

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a million I don't quite remember the
number but several towns still didn't

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have electricity infrastructure in 2012
that was down to less than 20,000 towns

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that didn't have electrification so
there was huge progress made between

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2012 and 2016 and that is reflected in
the purple next a similar case is in

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Ivory Coast there was a lot of
infrastructure investment there in both

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solar and hydro providing new
electricity for that region these are a

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bunch of different news articles that
came out during that time period

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tracking the power generation
development next slide and then this is

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a black marble understanding of how
electrification has happened in three

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cities in the Ivory Coast and where
which which parts of the cities have

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benefited from that investment so what's
really nice about this data is you know

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you know that the investment is going
there but it's

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it's a way for people to objectively
track how successful that has been at

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the end user level so one of the key
sustainable development goals I think

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it's SDG 7 is to ensure modern energy
services for all and so this is the key

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way that we're gonna have to be able to
track that across places that have very

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different levels of statistical data
collection systems

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next

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ok so I wanted

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to end by talking about a couple of
future directions for our team a lot of

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times in the remote sensing literature
nightlights has been used as a proxy for

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everything it's a proxy for GDP, it's a
proxy for where people are, people use it

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to map where urban areas are but we want
to move a step beyond that instead of

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thinking of it as a proxy we want to
look at places where infrastructure

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where lights might diverge from those
other things for example where does

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population grow but lights doesn't
where does lights grow but population

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doesn't where has land change happened
without infrastructure development and

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what are these different signals where
those signals diverge what are those

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mean
so this ternary diagram is something

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we've developed where we're looking at a
global population data set, a global land

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data set and a global infrastructure
data set which is nightlights at the

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same time and seeing what kind of
classes are pulled out and what kind of

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new information about urbanization is
pulled out by using these three data

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sets in tandem, orthogonally, as opposed
to as a proxy of one another, ok next so

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this is the ternary diagram that we've
developed in this corner this is a when

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population increases but the other two
don't we call that a population dominant

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class a land dominant class and an
infrastructure dominant class we have in

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the center we have our most common
conceptualization of urbanization that's

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where they all happen at once and then
these other ones are what we're

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referring to as skewed urbanization
where two of the three growth signals

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happen next

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and so you can pull out
different kinds of

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development from that from taking these
three together and these different types

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of development have really different
impacts on natural land systems in the

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environment so for example vertical
growth in cities is a really different

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type of urbanization than growth in
informal settlements so though these

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have very different social implications
and also very different like energy and

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resource implications
okay now so I'm just going to show you

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some examples of what this
classification pulls out so this first

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one is this is the center okay then this
first one this Center triangle right

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here that's concurrent change so this is
what we think of this is Noida India

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before in 2012 and 2017 basically a
whole city grew up out of nowhere this

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is a very common signal to see in India
where population growth happens

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infrastructure growth happens and land
change happens all at once and people

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who have visited this area of the world
understand this is what people usually

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mean when they say urbanization next

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these are three of the independent types

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so this is actually where I live in
Washington DC and NOMA this is the

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previously developed area that started
to grow vertically and so this is we saw

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an increase in population here but no
change in land and no change in

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infrastructure because already there
were streetlights already those places

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were paved so they didn't get pulled out
in an impervious signal but what was

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really changing was a vertical change
and so that was pulled out in the

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population signal this is in Uttar
Pradesh this is a electrification of a

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road these red you can barely see it but
these red circles are showing the shadow

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of the street lamps that were picked up
by the Google images so this is just an

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electrification change no population
change and no land change associated and

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then this is just a land change where
one undeveloped land was changed into

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like a garden farming so these are three
different signals that are pulled out as

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urbanization depending on what what data
set you're using but they're really

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different processes and they have really
different implications on sustainability

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and next
and then finally oh sorry and this is

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also from the electrification this is
the electrification one where I was

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showing you the roads so we looked at
this class across all of India and we

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compare that to the statistical data
from surveys and we found that actually

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it mapped pretty well the
electrification signals so if you pull

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out just that that class it's a it's a
pretty good indication of

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electrification in terms of estimating
the population in those pixels okay next

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and so finally the three classes we
haven't talked about next are these

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three so this class is where population
and infrastructure are changing but no

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land change this is Broadmoor
neighborhood in Detroit so here we

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actually saw a decrease in population a
decrease in lights but no change in land

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and that's because when Detroit went
into the Great Recession lots of people

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moved out of this area the streetlights
weren't even kept on because they lost

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so much population but there's very
little change in land you can see a few

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houses here and there that have changed
but it's not really picked up in a lot

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of our land data so it was picked up in
the other two this is a really important

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land class that's pulled out by this
this is the growth of informal

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settlements in in Mumbai so here you
could see that there was a change in

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population and a change an
infrastructure but the land data set

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really didn't capture because the pixels
were were large and there's not a ton of

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I mean it's it's an infill land change
and then here this is the final one this

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is like a sports facility in California
there was a huge change in lights

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because they put a sports field there
was a big change in land because they

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also created this parking lot and this
new development but no change in

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population no one's living there so that
kind of commercial development is also

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pulled out by this urbanization signal
which is really really interesting

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because then we're getting it to land
use not just lot land cover right okay

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so next
and so finally one other next step is

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the fusion of our black marble data with
different spatial resolution data so

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this is black marble in its native form
it's 500 meters this is overlaid on an

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Open Street Map of University of Houston
okay next one so when we downscale that

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based on Landsat we get kind of a 30
meter which we've been calling the black

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marble HD product and this is where we
start to guess for each pixel what are

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the uses within each pixel that we're
using the light how do we distribute the

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light within each pixel based on our
Landsat there's a paper out about how we

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do this but it starts to get us to more
exact targets especially for those cases

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where we're looking at disaster impacts
of like where the outages might be

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occurring and what are the uses that
might be affected and then this last

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image is something nobody's seen before
and this is the 2 meter version with

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digital Globes data so you can see how
you can start to get really specific

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about how lights are potentially
distributed within each pixel and you

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can have a very high-resolution
understanding of the energy

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infrastructure in a city I think over
here it's gonna be a full-scale version

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of Houston based on that data so a
little bit of eye candy to leave you

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with next so yeah I just wanted to wrap
up by saying I think that black marble

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is really exciting because it's making
the case for how we start to bring

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together this human system side with the
natural system side that NASA has been

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so focused on and really we can't
understand one without the other because

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now that we're in the Anthropocene it's
the human systems that are driving a lot

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of the natural system changes so we
really have to understand the processes

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within urban areas to understand why
their impacts are the way they are so

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this is a one step towards completing
that picture thanks so much for

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listening