Welcome,
everybody,
to
Pictures
from
the
Gallery 7.
My
name
is
Scott
Wise,
I'm
a
Senior
Systems
Engineer
in
the
US W
est
Coast
and
I'm
joined
today
by
my
daughter
Samantha.
-Hey.
-Hey.
I
wanted
to
ask
you,
you're
a
brand-
new
incoming
college
student.
What
are
you
most
concerned
about
for
the
future?
Well,
to
start,
I'm
pretty
worried
about
negative
effects
on
the
environment
like
deforestation
and
soil
depletion
and
climate
change.
Additionally,
I'm
worried
about
things
like
sexism
in
the
workplace
and
gender
gap
wages.
Wow,
that's
a
lot
to
think
about.
It
got
me
thinking
as
well
what
we
can
do
to
make
this
world
a
better
place.
To
start
off
our
presentation,
I
got
three
suggestions
here.
If
we
stay
curious,
part
of
what
we
can
do
with
that
curiosity
is
actually
share
with
each
other
good
data.
You
all
like
to
analyze
data,
but
sharing
some
good
data
would
be
a
great
idea.
and JMP
has
a
data
for
green
initiative
where
over
the
JMP
community,
you
can
actually
share
what
you
think
are
meaningful
views,
meaningful
data
collections,
and
we
can
analyze
these
things
together.
The
second
thing
you
can
do
is
to
use
your
time.
So
JMP
and
SAS
have
both
partnered
with
the
IIASA,
which
is
trying
to
actively
measure
the
amount
of
deforestation
in
the
rainforest
to
help
guide
better
policy.
So
it's
kind
of
a
cool
application
that
lets
you
look
at
some
of
their
satellite
images
and
actually
help
identify
where
you
see
development
in human
growth
in
the
rainforest
and
enable
them
to
do
a
better
measurements.
Lastly,
user
skills.
We
all
have
great
JMP
skills
in
practice,
looking
at
analytics
and
building
visualizations.
Our
friends
at
WildTrack
I
think
are
some
of
the
best
examples
where
they're
using
the
footprints
of
many
different
species
of
endangered
animals,
and
by
doing
a
little
bit
of
JMP
and
a
little
bit
of
visualization,
they're
able
to
help
track
in
a
non-
invasive
way
these
endangered
animals
to
help
us
again
create
better
policy.
That's
Sky
and
Zoe,
and
definitely
I'll
put
a
link
in
here
where
you
can
check
out
their
work
and
get
inspired.
All
right,
so
without
further
ado,
here
is
the
pictures
for
the
gallery
for
this
version.
And
in
our
version,
I
am
going
to
dedicate
every
view
to
something
around
environmental
green
data.
We
can
start
that
conversation.
But
as
usual,
I
am
showing
you
some
things
that
are
new
into
Graph Builder,
such
as
the
first
five
years
I
show
you
have
been
brand
new
things
in
JMP
16,
as
well
as
I'm
going
to
show you
just
a
couple
of
tips
and
tricks
you
probably
have
never
seen
before.
All
right,
I've
got
the
first
chart here,
and
the
first
chart
is
going
to
address
equality
and
it's
on
the
gender
wage
gap.
It's
going
to
show
you
a
new
interval
type
chart
that's
available
in
JMP 16
that
I
call
the
Dumbbell
Chart.
Now
I'm
going
to
give
you
this
journal,
and
why
I'm
pointing
this
out
is
if
you
want
to
recreate
this
view,
you
not
only
have
a
picture
of
what
it
looks
like
and
tips
on
how
to
set
your data up,
to
make
this
chart,
but
I
give
you
the
steps.
In
order,
not
only
that,
I
give
you
the
data,
and
within
the
data,
you
can
just
click
on
the
script
to
regenerate
the
view.
It's
all
there
for
you.
I'm
going
to
build
this
one
from
scratch.
What
is
this
data?
This
comes
from
the
International
Labor
Organization
and
it
is
looking
at
the
nominal
mean
earnings
of
males
and
females.
But
it
has
it
normalized
by
US
dollars.
It
would
be
nice
to
see
if
that
gap'
s
getting
smaller,
like with
Sammy's
concern
about
the
gender
wage
gap.
So
I
used
to
think
you'd
have
to
create
a
formula
which
actually
took
the
delta,
but
to
graph
it,
you
do
not.
You
just
need
to
have
both
columns
you're
wanting
to
compare.
I'm
going
to
go
to
Graph Builder.
Everything
today
is
going
to
be
in
Graph Builder.
And
I'm
going
to
take
the
female
monthly
and
the
male
monthly.
I'm
going
to
put
them
both
on
the
x-
axis,
but
I'm
also
going
to
put
them
on
the
interval
landing
spots.
And
I'm
going
to
take
year
and
put
year
on
the
Y.
Now
it
looks
really
busy
and
it's
because
there's
many
countries
represented
here
over
this
span.
If
I
go
under
the
red
triangle,
I
like
to
call
this
a
hot
spot,
and
add
a
local
data
filter.
We'll
just
look
at
it
by
one
country.
Let's
pick
out
France.
Now
I
get
a
pretty
good
view.
It
might
be
better
to
clean
up
the
view
a
little
bit.
I
can
right-
click
right
on
the
female
monthly
marker,
and
I
can
take
this
marker
size
up
a
little
bit.
I'm
going
to
make
it
a
10.
And
now
I
can
do
the
same
thing
with
the
male
monthly.
I'll
make
that
10 as
well.
I
can
right-
click
right
here
into
the
graph
and
go
to
Customize.
And
I
want
to
make
that
intersection
bar.
I
want
to
make
it
a
different
color.
It's
the
second
air
bar
in
that
list,
and
I'm
going
to
make
it
gray
and
maybe
make
it
a
bigger
width
of
three.
Now
I
got
the
view
I
like.
Now
you
can
kind
of
tell
why I
call
it
a
Dumbbell
Chart,
because
if
anybody
likes
to
work
out,
you
know,
at
the
gym,
you
have
weights
and
the
ends
of
the
weights
is
where
the
heaviness
of
it
is
on
the
ends.
In
the
middle,
you
have
a
bar
to
lift.
That's
why
a
lot
of
people
called
this
a
Dumbbell
Charts.
Now,
a
couple
of
cool
things
I
can
show
you.
Number
one,
we
generally
don't
read
from
bottom
up,
we
read
from
top
down.
You
can
right-
click
here
in
your
Axis Settings,
and
I
can
reverse
this
order
just
by
clicking
on
this
little
box
right
here.
Now
I'm
going
2010
to
2019.
Also,
I
might
want
to
put
a
reference
line
on
the
X-axis
to
help
my
eyes.
So
I'm
going
to
right-
click
go
to
Axis Settings.
And
I
think
about
3,500
would
make
a
good
little
reference
line.
It's
going
to
put
one
on
the
X-
axis,
and
there
we
go.
Now
I
can
kind
of
gauge,
is
the
gap
closing,
is
the
wage
increasing
for
both
sexes,
that
type
of
thing.
Now
I'm
going
to
bring
a
lot
of
pictures
in
as
examples
for
our
data,
and
I'm
going
to
show
you
that
if
you
just
take
a
picture
and
you
just
put
it
into
your
graph,
it
will
put
the
picture
as
a
background.
Now,
it's
sized
horribly
here.
It's
easy,
you
just
right-
click
go
to
Image,
go
to
Size
and
Scale,
and
save
Fill Graph.
There
we
go.
And
this
is
pretty
cool.
Now,
I
know
the
female
symbol
here
in
the
background
map
is
red,
so
maybe
I'll
go
right
up
here
to
my
legend
and
I
will
change
the
colors
around
here.
Pretty
easy
to
do.
A
lso
I'm
going
to
right-
click
back
into
the
graph
and
in
my
image,
I'm
going
to
make
that
background
a
little
more
transparent,
maybe
like
a
0.3
here.
Now
I've
got
a
really
cool
view.
Now,
one
word
of
warning,
it's
locked
into
this
scale
,
and
you're
going
to
get
a
different
scale
for
each
country
because
some
countries
pay
more
than
others.
I
know
Germany
pays
very
well.
You
can
see
it
changed
my
picture,
so
you'd
have
to
right-
click
and
go
to
Image
again
and
Fill
the
Graph
to
pull
it
back
correctly.
You
might
want
to
move
your
reference
line.
The
background
maps
are
not
great
if
you're
going
to
change
your
scale
a
lot.
I
do
have
a
version
here
that's
a
multiple
view
version
without
a
picture
and
if
you
right-
click
on
this
one,
you
can
see
here
I
was
comparing
over
the
same
scale.
I
fixed
the
scales
but
made
them
a
little
bigger.
What
was
the
difference
in
France,
Germany
and
Sweden,
and
you
can
see
that
in
France,
the
wage
gap
doesn't
look
so
bad
on
this
scale,
but
Germany
has
a
bigger
wage
gap
but
pays
higher.
Maybe
you
want
to
be
in
Germany.
And
I
noticed
in
Sweden
that
the
females
make
more
than
the
males.
There
you
go.
There's
a
lot
of
differences
out
there.
This
is
some
fun
data
to
play
with,
so
definitely
see
what
views
you
like.
All
right,
second
picture
we're
going
to
look
at
is
a
Word
Cloud.
This
was
the
second
most
popular
thing
that
got
requested.
And
you
might
have
seen
in
JMP,
there
is
a
Text
Explorer
platform
that
allows
you
to
look
at
unstructured
text
data
that
you
might
have.
And
Word
Cloud
was
one
of
the
views
it gave
you
just
with
a
click
of
a
button.
But
how
do
you
do
that
in
Graph Builder?
Well,
let's
take
a
look.
In
Graph Builder,
all
you
need
is
the
unstructured
text.
In
this
case,
I
have
a
column
of
words
and
you
need
some
sort
of
counterweighting.
Here
I
have
the
weight
and
where
this
data
came
from.
This
was
a
study
run
during
COVID
of
what
are
the
top
five
things
teachers were
worried
about.
Of
course
they
were
dealing
with
a
lo
t.
Remote
teaching,
sick
students,
sick teachers,
a
big
change
to
curriculums
just
to
get
through
the
year.
So
here,
the
highest
weight
was
anxious.
Twenty
respondents
all
mentioned
being
anxious.
So what
I've
done
is
I
have
sorted
the
words
by
weights
and
then
I
just
put
a
order
here.
So
the
highest
weight
got
an
order
of
one
and
the
next
highest
got
an
order
of
two
and
so
forth.
That's
how
I
got
the
weight
column
and
that's
how
I
got
the
order
column.
And
I
also
created
some
random
data
because
you
can
have
a
sorted,
ordered
word
cloud,
but
you
can
also
have
one
that
just
looks
like
a
cloud.
To
generate
that
one...
You
might
not
have
known
this,
but
if
you
go
and
open
a
new
column
in
JMP
with
this
initialized
data,
you
can
put
in
random
data
and
you
can
put
in
things
like
random
normal
data.
Okay,
I
have
already
done
that.
Let's
just
go
to
Graph Builder
and
see
how
this
works.
Well,
the
first
thing
I'm
going
to
do,
I'm
going
to
put
weights.
There
we
go
on
the
Y-
axis,
and
I
am
going
to
size
by
weight,
but
I
don't
want
points.
And
here's
a
little
trick
in
JMP
16,
under
the
red
triangle,
under
the
points
elements
panel
here
that's on
your
bottom
left
hand
side
of
the
Graph
Builder,
I
could
set
a
shape
column.
When
I
do
that,
I
can
substitute
for
points
the
actual
word.
And
now
you're
starting
to
see
the
words
and
as
you
start
to
move
around
your
graph,
you
can
see
what's
going
on
with
those
words
and
that
is
very
cool.
This
right
here
is
your
first
check
of
doing
a
word
cloud.
Now
I
can
color,
by
the
way,
to
give
this
thing
some
color.
Now,
I'll
want
to
make
it
as
cloud-like
as
possible.
I
can
move
this
random
over
here.
A
gain,
playing
around
with
the
data
set,
I
can
get
the
view
that
I
like.
Maybe
I'll
say
done
here,
maybe
I'll
go
to
the
legend
position,
maybe
I'll
put
it
on
the
inside
left.
Maybe
I'll
go
under
the
legend
settings.
Maybe
I'll
turn
off
all
but
just
the
color
code
.
And
now
I
got
a
pretty
nice-
looking
word
cloud.
Now
as
well,
if
I
wanted
it
to
be
in
sorted
order,
because
I
know
anxious
is
the
most
important.
That's
the
biggest-
sized
word,
I
love
that
to
kind
of
be
on
top
and
then
the
next,
and
then
the
next.
So
to
do
that
one,
I'll
open
my
control
chart
panel
back
up.
I
will
swap
out
the
order
for
the
random.
Now
you
can
see
all
the
big
words
are
on
the
bottom,
so
I'm
going
to
right-
click
here
under
Axis
Settings,
and
I'm
going
to
do
that
reverse
order
again.
N
ow
all
the
big
stuff
is
up
at
the
very
top
that
I
have.
And
Jittering,
what
you
didn't
see
before
was
you
were
getting
a
centered
grid
jitter.
And
that's
actually
what's
automatically
in
your
points
jittering.
If
I
do
a
positive
grid
now,
I
get
things
in
order.
Anxious,
constant
stress
and
tired,
whatever
it
has
room
for
on
the
line.
But
it
is
in
that
row
order,
which
is
pretty
cool.
But
it's
so
much
on
the
left-
hand
axis
that's
a
little
weird.
So
I
can
right-
click
here
on
the
X-axis.
Even
though
there's
nothing
down
here,
you
can
still
play
with
the
settings.
And
I
can
go
and
maybe
make
this
a
negative
0.5
for
the
minimum.
It's
going
to
add
a
little
bit
of
space
over
here.
And
if
it
did
a
nice
job,
did
a
really
nice
job.
Now
you
can
get
an
ordered
word
cloud.
All
right.
And
then,
of
course,
the
ones
I
have
in
my
data
that
you
can
play
with,
you
can
see
I
put
in
a
nice
transparent
apple
background
just
by
bringing
in
that
picture,
which
is
really
nice
to
play
with
the
colors
and
all
those
type
of
things.
All
right,
that
was
a
nice
popular
view
everybody
asked
for.
The
third
most
popular
view
was
Line
Charts.
Everybody
likes
to
do
line
charts.
In
JMP
16,
there's
many
new
features
that
actually
fit
lines
through
points
in
a
lot
of
different
formats,
as
well
as
label
your
line
interactively.
And
we're
going
to
look
at
tree
cover
loss.
Remember
I
showed
you
that
link
that
would
help
you
with
folks
that
were
trying
to
save
the
rainforest.
Well,
it's
important
to
know
how
much
we're
losing
around
the
planet.
We
have
some
of
that
data,
the
under
three
here,
moving
average
smoother
line
chart.
I
bring
up
my
data
here.
I've
got
tree
cover
loss
in
hectares.
By
year,
this
should
be
pretty
straightforward,
so
I'll
just
go
to
my
Graph Builder.
I
will
put
my
tree
cover
loss
in
hectares.
I
will
put
my
year
down
here
on
the
X.
And
you
can
see
I've
got
points
and
smoother
lines.
Not
so
exciting,
maybe
take
drivers
and
overlay.
Getting
more
interesting,
but
I
don't
like
these
lines.
What
other
options
do
I
have
for
other
smoother
lines?
Well,
in
JMP
16,
they
put
things
like
moving
average.
Maybe
a
moving
average
would
be
cool.
You
can
control
the
spread
of
that
mover
average
with
this
local
width.
And
I'm
going
to
do
that
one.
I'm
going
to
say
done.
And
now
it's
looking
pretty
good.
I'm
actually
going
to
open
it
right
back
up,
there's
one
thing
I
forgot
to
do.
You
can
actually
put
a
confidence
in
around
them.
Now
I'll
say
done
just
fine,
but
I
clicked
this
little
button
right
there.
Very
cool.
But
I
just
want
to
look
at
kind
of
the
big
hitters.
So
this
might
be
a
good
place
to
go
under
the
red
triangle,
go
to
the
local
data
filter.
Go
ahead
under
drivers,
and
just
take
the
top
three
drivers.
There
we
go.
This
is
a
good
chart,
I'm
very
close.
But
one
thing
that
this
legend'
s
kind
of
hard
to
read.
Wouldn't
it
be
nice
to
put
the
name
next
to
the
line,
maybe
even
on
the
line?
Oh,
that
would
be
awesome.
Well,
you
can
do
it,
you
might
not
know
this
is
the
place
you
can
do
it,
but
if
you
just
right-
click
right
here
on
the
legend
where
it
says
Agricultural
Shift,
you
can
say
what
happens
to
the
label.
You
can
add
minimum
values
and
first
value,
last
value,
but
just
go
click
Name
and
you
can
see...
Oh,
look
at
that,
drew
it
right
in
there.
I'm
going
to
do
the
same
thing
with
Commodity,
and
I'm
going
to
do
the
same
thing
with
Forestry
Driven.
Now
I
don't
need
that
legend.
I
can
go
under
my
red
triangle
versus
Graph Builder.
I
can
turn
off
the
legend
because
it's
not
adding
any
value.
Now,
here's
what's
really
cool,
you
don't
have
to
leave
it
out
here.
You
can
move
it
anywhere
along
the
line
if
you
get
close
to
the
line,
it
will
try
to
take
the
slope
of
the
line
as
its
orientation.
I'll
do
this
for
Agricultural
Shift,
I'll
put
that
one
there.
Commodity, I'll put
one
there.
And
now
I
can
move
out
the
axis.
And
now
I've
got
a
really
cool
chart.
By
the
way,
on
this
chart,
Agricultural
Shift
was
the
big
haha.
It
was
something
that
we
were
definitely
having
a
huge
spike
in,
but
I
think
those
efforts,
of
our
friends
trying
to
save
the
rainforest
have
managed
to
pull
it
back
a
little
bit.
A
gain,
you
have
that
version
scripted
in
your
data
as
well
as
a
cool
little
background
picture
in
the
background.
What
else
do
we
have
here?
Let's
go
to
the
bottom
of
our
chart.
The
next
most
popular
chart
was
actually
a
Point
Cumulative
Summary
Chart.
Very
interesting,
it's
on
safety
data.
It's
not
using
points,
it's
using,
looks
like
a
value
of
years.
That's
pretty
cool.
This
data
came
from
the
Bureau of Transportation Statistics,
and
why
I
liked
this
data
was
it
not
only
gave
us
a
index
of
crash
rate
and
injury
rates,
and
this
is
all
based
off
millions
of
miles
driven,
but
for
each
year,
like
in
1998
year,
it
told
us
that
the
dual
front
airbags
was
the
safety
innovation
that
came
in
that
year.
T
his
would
be
cool
to
see
what's
going
on
with
my
line
chart.
I'm
going
to
go
graph,
Graph Builder.
We'll
go
ahead
and
take
both
crash
and
injury
rate,
put
it
on
the
Y,
put
year
on
the
X.
I'm
going
to
turn
off
the
smoother line
here
and
just
look
at
the
points.
And
can
I
tell
any
difference
between
crash
rate
and
injury
rate?
I
really
can't.
This
is
where
having
a
cumulative
summary
would be
really
cool
because
I
can
go
under
the
summary
statistic,
under
the
Points
element
and
just
change
this
out
from
none
to
cumulative
summary.
Now,
do
you
get
a
sense
of
the
differences
in
the
slope
of
the
line?
You
should,
because
the
summary
of
the
crash
rate
definitely
has
a
steep
line,
and
I
would
expect
this,
there's
more
people
on
the
Earth
now
driving
more
miles
.
But
you
can
see
that
it
looks
like
the
injury
rate
has
less
steep
slope
and
seems
to
be
flattening
out.
And
maybe
that's
because
of
these
innovations.
Here
under
my
Axis
Settings
for
the
X-
axis,
I
can
put
in
like
in
1998,
there
were
the
dual
airbags.
And
see
if
that
might
be
an
inflection
point, a
cause
of
cars
that
are
now
protecting
us
more
from
injury.
That's
pretty
cool.
The
other
cool
thing
to
do,
you
could
as
well
under
your
points
red
triangle,
set
the
shape
column
by
year.
And
even
though
it's
continuous,
it's
just
going
to
give
you
the
value
in
this
case,
that
is
really
cool.
So
now
I'm
seeing
it
by
year.
Very
nice.
Then
I
have
a
view
in
here
where
I
have
gone
through
and
added
a
whole
bunch
of
the
safety
innovations
over
time
and
put
a
nice
more
transparent
airbag
background
because
airbags
was
a
big
deal.
But
you
can
see
when
things
like
blind
spot
warnings
came
in,
anti-
lock
brake
[inaudible 00:22:34]
technology.
Really
cool
to
see
how
the
industry
is
helping
to
save
us
from
injuries.
All
right,
[inaudible 00:22:45]
right
along.
Our
next
to
last
chart,
but
still
very
popular
review,
is
Advanced
Box
Plots.
There's
a
lot
more
you
can
do
with
box plots
to
integrate
them
even
with
other
elements
like
points
and
labels.
And
we're
going
to
look
at
some
climate
city
risk.
And
this
is
some
really
fun
data
that
I
found
on
looking
out,
projecting
out
to
2050.
And
it
was
coming
up
with
this
total
climate
change
risk
index
on
a 1-
100
scale.
And
it
was
looking
at
things
like
potential
sea
rise,
shifts
in
temperature.
Shifts
in
climate
is
something
very
important
for
a
lot
of
us,
especially
us
out
in
the
West
Coast,
which
is
water
stress
or
water
scarcity.
And
that's
how
I
came
up
with
this
total
climate
change
risk
score.
If
I
want
to
see
what
that
one
is
looking
like
on
a
box
plot,
it
would
be
pretty
easy
to
just take
my
total
climate
change
on
the
Y.
Take
my...
Well,
actually,
I'm
going
to
put
it
on
the
X.
There
we
go.
And
I'm
going
to
do
it
by
region
on
the
Y.
Now
that's
going
to
allow
me
to
then
ask
for
some
box plots
and
here
we
go.
It's
not
so
interesting
to
me.
I'm
going
to
hold
my
Shift
key
down
and
add
back
in the
points.
Now,
boring
box
plots
don't
have
to
be
boring
anymore
because
now
I
have
different
types
of
box
plot
types
I
can
do
in
styles.
Under
style
I
got
this
Solid
Style.
Now
it
colors
it
in,
which
is
pretty
cool.
And
as
well,
you
can
go
and
notch
them,
and
you
can
go
and
add
fences
to
them. By
the
way,
it's
got
the
Outlier
selected
and
since
it
looks
like
on
this
data
set,
you
can
see
I've
turned
the
labels
on,
I've
already
sorted
this
by
total
climate
risk.
I
just
want
to
label
the
top
10 that's
already
on
there.
I
can
turn
off
this
outlier
and
that
takes
any
duplications
out
of
there
and
that's
what
we're
looking
at,
which
is
pretty
cool.
Maybe
change
this
color.
By
the
way,
if
you
cannot
see
your
points
in the boxplot,
sometimes
if
you
put
the
box
plot
in
last,
it
would
have
moved
it
forward
and
it's
over
top
of
your
points.
So
then
all
you
have
to
do
is
go
into
your
points
and
move
it
forward.
Just
right-
clicking
into
your
graph
and
just
go
into
the
right
element
and
bringing
it
forward.
That
is
pretty
cool.
And
you
can
see
what's
going
on
where
we
have
the
highest
risk
of
cities
running
in
the
climate
change
risk
in
2050
and
usually
things
coastal
are
at
extreme
risk
of
water
shortage.
Very
cool.
All
right.
And
as
well,
I
put
a
nice
little
background
picture
in
the
background
on
this
one.
It
moved
the
legend
in
a
little
bit,
so
that's
all
in
the
instructions
as
well.
All
right,
so
we
are
to
our
last
beautiful
pictures
from
the
gallery
review.
And
that's
a
Wind
Rose
Chart,
and
I
was
thinking
that
we're
getting
a
lot
of
adverse
weather
given
the
changes
in
our
climate,
and
so
we're
always
trying
to
get
better
at
predicting
which
way
are
the
winds
blowing,
how
strong,
where
are
hurricanes
going,
tornadoes,
typhoons,
all
these
types
of
things.
There
is
a
cool
view
and
this
is
not
limited
to
JMP
16.
But it is a
type
of...
The
pie
chart
is
actually
a
version
of
a
Coxcomb
chart
that
will
make
a
compass
rose.
If
I
can
get
it
labels
that
tell
it
like
in
a compass,
what's
north
east?
What's
north
west?
Those
type
of
compass
directions.
I
can
come
up
with
a
pretty
cool
pie
chart
that
lets
me
segment
that
chart
by,
in
this
case,
wind speed.
So
let's
take
a
look
at
this
data.
There
we
go.
This
is
a day's
worth
of
data
in
the
Great
Lakes
area.
If
you
take
a
look
here
at
the
6th
row,
you
can
see
that
I
not
only
have
latitude
and
longitude
and
the
speed,
where
it
starts,
how
strong
the
wind
was,
what
direction
it
was
going,
and
then
of
course,
I
can
get
that
into
a
compass
direction
like
west
southwest.
With
that
I
should
be
able
to
go
and
just
put
that
compass
direction
down
on
the
X,
ask
for
a pie
chart,
but not
only
any
kind
of
pie
chart,
I
am
going
to
ask
for
the
Coxcomb chart
and
I'm
going
to
take
the
wind
speed
and
I'm
going
to
overlay
it
by
the
wind
speed.
You
can
play
with
these
colors,
I
might
move
this
in
a
little
bit.
I
might
make
the
really
fast
winds
red.
And
now
I
can
see
that
they
were
mainly
in
this
direction
on
the
compass.
That
was
where
predominantly
most
of
the
wind
was
and
where
some
of
the
darker
red
was
as
well.
Very
cool.
I
have
a
couple
of
versions
with
this,
you
might
have
seen
that
I
brought
in
kind
of
an
old
type
of
wind
direction
map
where
they're
drawing
the
wind
vectors
onto
a
map,
which
is
pretty
cool,
and
if
you
want
to
see
how
to
do
that
one,
I
put
this
in
the
instructions
as
well.
And
if
I
open
the
control
panel
back
up,
and
I
go
under
the
spread
hot spot
where
points
are,
and
I
go
to
Set
Shape E
xpression.
You
can
see
that
there's
a
formula
behind
it.
And
what
this
formula
is
doing
is
it's
looking
at
each
point,
which
is
plotted
by
the
latitude
and
the
longitude
on
the
map.
Then
it
is
taking
the
wind
speed,
it
is
drawing
an
arrow,
and
it's
drawing
a
bigger
arrow,
of
course,
if
there
was
a
stronger
speed.
That's
kind
of
cool,
and
that's
what
draws
those
blue
lines
that
you're
seeing
involved
right
there.
All
right,
all
those
are
in
your
instructions.
I'm
just
about
out
of
time
here,
I
will
show
you
that
I
have
put
into
the
journal
that
I'm
giving
you
where
to
learn
more.
You
can
see
other
galleries,
you
can
see
blogs
in
journals,
other
presentations,
and
even
great
tutorials.
They're
all
from
the JMP C
ommunity,
community.jmp.com.
Those
are
there
for
you.
Go
have
fun
with
your
Pictures
from
the
Gallery
7.
Go
try
to
recreate
these
views
on
your
own
data
so
they
can
be
nice
and
compelling,
and
do
use your
curiosity,
time,
and
skills
to
help
save
the
planet.
