Good
afternoon.
Today
we're
going
to
be
talking
about
the
understanding
of
crime
rate
and
crime
prediction.
Before
we
go
into
the
data,
let
me
introduce
a
team.
We
have
Karanveer, our data
scientist
and
data
modeling
expert,
and
myself Grant Lackey
as
a
data
researcher
and
data
visualization
specialist.
So
before
we
get
into
the
data,
let's
do
an
overview
of
the
entire
presentation.
We're
going
to
begin
with
the
background
which
will
be
the
initial
data
sets
and
why
we
chose
our
data.
The
data
overview,
which
again
is
going
into
the
reason
why
we
chose
our
data
and
what
we're
going
to
be
trying
to
answer.
The
business
problems,
which
is
the
problems
that
we
had
with
our
data.
Why
we're
trying
to
answer
certain
questions
and
the
overall
idea
of
the
entire
project.
Next
is
our
methods
and
plans,
which
is
our
procedure
of
answering
our
business
problem
and
then
our
results,
which
are
the
results
of
our
methods'
plans.
Our
applications,
which
are
real-
life
implications
from
our
results,
and
post-analysis,
which
is
what
we
could
include
or
add
on
to
our
results.
What
we
could
add
on
to
improve
upon
this
and
years
to
come.
Beginning
with
background,
why
should
we
care
about
crime
rate?
Well
crime
is
just
important
to
everyone,
and it's
everywhere
in
the
United
States,
and
so
what
is
crime
rate
and
how
can
we
define
it?
How
we
define
crime
rate
is
our
initial
criminal
activity,
divided
by
the
population
density
per
county
or
per
state.
We're
mainly
going
to
be
looking
at
per
state.
So
how
are
we
going
to
identify
factors
which
can
reduce
crime
rates
throughout
our
entire
project.
Here
we're
going
to
be
speaking
about
how
certain
crime
is
going
to
be
more
influential
in
certain
states
than
others,
and
do
certain
crimes
influence
other
crimes?
So
for
example,
if
there
was
a
murder
crime,
would
guns
or
theft
be
more
influential
in
that
murder
crime
or
would
other
crimes
be
influential
in
that?
So
looking
at
our
data
overview.
We
started
off
with
our
initial
data
set
which
is
our
crime
statistics
data,
and
we
added
other
data
variables
later
on
throughout
this
initial
data
set.
Beginning
with
our
initial
data
set,
we
started
with
2014
data,
and this
initial
data
set
was
given
to
us
from
Federal
Bureau
of investigation:
the
FBI.
We
looked
at
42
criminal
activities
which
are
wide
range
from
murder,
theft
to
drug
possession,
drug
activities.
We
looked
at
about
3,200
counties,
and
within
all
these
counties
or
within
these
states
would
be
all
those
counties.
We
had
to
look
at
48
states.
We
had
to
exclude
Florida
and
Illinois,
because
Florida
and
Illinois
did
not
provide
data
to
the
FBI
for
the
criminal
activities.
If
you
look
at
future
2018
data
or
past
2012
data,
it's
the
same
issue.
They
just
don't
provide
data
to
the
FBI
it
seems
.
With
all
of
this
for
2014
data,
there's
180,000
data
points
talking
about
the
FIPS
codes.
This
is
how
we
identified
certain
criminal
activity
in
certain
counties.
For
example,
we
have
our
state
codes,
which
would
be 01
for
Alabama.
These
states
are
represented
alphabetically.
Alabama
would
be
the
first
one,
and
then
each
county
within
that
state
would
have
numbers
to
them.
For
example,
Baldwin
would
be
003.
If
you
looked
at
Baldwin,
Alabama,
it'd
be
01003,
so
on
and
so
forth
for
every
county
detailed
in
the
state.
Looking
at
our
extra
variables,
we
looked
at
the census
data.
Census
data
is
always
great
for
checking
out
the
age,
population,
income
per
county
or
per
state,
and
we
had
to
look
at
other
data
sets
like
gender,
immigration,
religion,
marriage,
unemployment
and
literacy
rates.
These
other
data
sets
looked
more
or
so
at
the
statewide
rates,
and
this
isn't
really
related
to
criminal
activity,
but
we
wanted
to
involve
it
within
our
initial
data
set
to
see
if
there's
any
correlation
with
them.
Going
into
our
business
problem,
we
want
to
answer
what
states
in
the
United
States
specifically
have
the
highest
and
lowest
crime
rates
and
why
is
that
so?
To
answer
our
business
problem,
we
have
to
answer
these
business
questions
going
into
that.
How
can
we
identify
variables
that
influence
crime?
Which
are
the
most
important
factors?
Are
there
crimes
that
influence
other
crimes?
I'm
going
to
hand
it
off
to
Karanveer
to
talk
about
plans and
methods.
Thank
you,
Grant.
Our
approach
to
solve
this
business
problem,
was
to
come
up
with
a
regression
model.
We
have
used
JMP
to
make
it.
First,
as
Grant
mentioned,
we
have
connected
the
various
databases,
that
is
the
crime
data
set,
along
with
those
extra
variables
such
as
religion,
income,
etc.
We have
made
sure
whether
the
data
looks
clean.
A
fter
that,
we
have
run
our
regression
model,
which
is
able
to
predict
the
crime
rate
for
us.
With
this,
we
are
able
to
know
the
various
variables
and
their
importance
in
determining
this
crime
rate,
and
we
are
able
to
list
them
by
their
importance.
A
t
the
end
we'll
also
be
showing
you
visualizations
based
on
it.
As
Grant
mentioned,
we
had
42
criminal
activity
variables.
Some
of
these
variables
were
very
small,
such
as
drug
possession,
drug
consumption,
drug
sales.
In
that
case,
we
have
simply
grouped
them
to
make
sure
that
we
can
come
on
a
conclusion
on
that
since
the
data
was
otherwise
too
small
for
the
subgroups.
We'll
be
looking them
state
wise
as
we
didn't
have
the
extra
variables
on
a
county
basis.
But
I
feel
that
this
is
great
for
starting
this
project.
Our
target
variable
would
be
the
crime
rate.
We
have
defined
the
crime
rate
as
the
number
of
arrest
in
that
certain
population.
Now,
coming
down
to
the
variables
that
we
are
using.
Most
of
these
variables
have
been
normalized
and
we
have
used
a
percentage
for
them,
such
as
immigration
for
gender.
We
will
be
using
two
types
that
will
be
a
male
and
a
female,
and
then
religion,
unemployment,
marriage,
literacy.
Most
of
these
are
normalized
so
that
we
don't
have
an
analysis
which
could
be
misleading.
Coming
down
to
the
final
equation
of
our
regression
model.
This
is
the
equation
of
a
model.
We
have
rounded
off
the
samples,
and
as
we
can
see
there
are
a
lot
of
variables
that
have
a
positive
influence,
as
in,
that
they
increase
the
crime
rate,
and
there
are
certain
variables
which
have
a
negative
sign
with
them.
They
basically
decrease
the
crime
rate.
Using
this
we
can
see
how
we
can
define
a
crime
rate
in
any
state
or
county.
Coming
down
to
the
results.
The
finding
number
one.
We
really
wanted
to
see
which
states
have
the
highest
crime
rate.
These
are
the
following
five
states.
Tennessee,
Wyoming,
Mississippi,
Wisconsin,
New
Mexico.
Then
we
have
the
following
states
with
the
lowest
crime
rate,
that
are
New
York,
Alabama,
Vermont,
Massachusetts
and
Michigan.
Here is
a
following
visualization
explaining
how
the
crime
rate
varies
across
United
States.
As
we
see,
there
is
no
certain
pattern
and
it's
all
over
the
place.
Finding
number
two.
Using
JMP
and
doing
a
log
[inaudible 00:07:57]
on
the
variables,
we
could
basically
see
which
variables
have
more
importance.
The
number
one
was
weapon
owned,
followed
by
literacy
rate,
then
religion
percentage,
immigration,
population
density,
and
the
unemployment
rate.
I
think
this
is
a
great
finding,
while
any
government
body
or
any
organization
wants
to
allocate
resources
whenever
they
are
trying
to
reduce
the
crime
rate
or
trying
to
analyze
it.
The
finding
number
three
is
something
really
interesting.
Our
goal
was
to
see
whether
there
are
certain
crimes
which
could
help
us
solve
not
just
that
crime,
but
maybe
other
crimes
as
well.
Which
these
crimes
are
trying
to
influence.
Drug
and
weapon
was
one
of
them.
We
could
see
drug
and
weapons
have
a
very
high
correlation
with
say,
theft,
robbery,
murder.
Using
a
chi-
square
test,
we
saw
that
the
correlation
is
very
high.
So
in
case
any
organization
would
want
to
focus
on
and
start
with,
I
think
drug
and
weapon
is
a
great
category
where
they
can
focus
at
for
reducing
crime
rate
in
any
state
or
county.
This
is
the
following
map
showing
the
religion
rate,
weapons owned,
and
literacy
rate,
and
the
variation
across
United
States.
If
we
put
it
with
the
crime
rate,
we
can
see
a
certain
pattern
which
is
actually
explained
by
our
regression
model.
Now
coming
down
to
the
implications,
how
we
can
use
our
analysis
to
a
real-
world
solution.
Like
the
data
set
we
have
used,
and
we
have
connected
to
variables,
we
would
definitely
want
to
work
with
governments,
towns
and
communities
because
crime
is
a
universal
problem
and
this
is
something
everybody
wants
to
reduce.
The
restore
allocation
can
be
done
according
to
this,
and
further,
this
would
result
in
a
decrease
in
crime
rate
and
increase in
happiness
in
the
community.
Post-
analysis.
There
are
a
lot
of
things
that
we
would
want
to
include
in
our
project,
and
this
is
a
great
future
scope
as
well.
First
thing,
we
would
want
to
include
more
variables
such
as
weather,
ethnicity
and
the
list
goes
on.
We
could
definitely
even
listen
to
the
government
bodies
and
take
inputs
for
these
variables
from
them.
County
detailed
or
at
least
city
detail.
I
feel
it's
great
to
start
with
state-
wise
data,
but
we
would
definitely
want
to
focus
on
a
more
detailed
level
of
analysis,
so
that
we
can
use
these
conclusions
to
the
real
world
more
clearly,
more
precisely
and
we
would
have
a
better
impact
as
well.
The
data
time
frame.
Right
now
we
have
used
the
data
from
the
year
2014.
I
feel
this
is
an
eight
year
old
data
set.
We
would
definitely
want
to
use
a
more
latest
data
set,
and
something
that
is
spanning
over
a
couple
of
years,
so
that
it
gives
us
clarity.
Since
COVID
has
impacted
us
in
a
lot
of
ways,
and
it
has
changed
how
basically
lives
are
working
around
us,
and
so
has
crime
rate
and
the
way
crime
happened
has
been
changed.
We
would
definitely
want
to
focus
post
COVID
,
and
over
the
last
two-
three
years,
for
a
post-
analysis.
That's
all
and
thank
you.
