Hello.
My
name
is
Rob
Carver,
and
today
I
want
to
share
a
story
about
a
project
I've
been
working
on
in
my
small
town
in
Massachusetts.
At
the
outset,
I'll
point
out
that
the
slides
and
the
JMP
data
table
are
up
on
the
discovery
website
and
there's
a
new
academic
case
study
on
this
very
topic
that
will
be
posted.
It's
not
already
posted.
It
will
be
posted
very
soon.
What
I'm
hoping
to
do
in
30
minutes
is
spend
most
of
our
time
with
a
JMP
demo,
but
you're
going
to
need
some
context
and
background.
I
want
to
provide
a
little
bit
of
a
scenario,
give
you
a
sense
of
the
problem
that
I'm
trying
to
solve,
and
talk
about
the
research
strategy
and
then
get
into
the
demo
and
wrap
up
with
some
conclusions.
I
live
in
a
town
called
Sharon,
which
is
an
archetypal
New
England
town.
Here
you
see
a
picture
of
our
talent
center.
It
was
incorporated
in
1765,
so an
old
community.
Like
many
New
England
communities,
the
legislative
function
of
the
town,
is managed
by
the
annual
town
meeting
in
which
anyone
can
come
and
speak.
M
emorialized
in
the
Norman
Rockwell
images.
From
the
start,
we
have
used
an
open-town
meeting
and
the
executive
function
is
carried
out
by
a
three-member
board
known
as
The Select Board.
But
since
Norman
Rockwell's
day,
municipal
government
has
become
technologically,
financially,
legally
more
complex,
even
for
the
most
fundamental
services
that
a
town
provides.
Attendance
at
the
town
meeting
has
really
dwindled.
About
a
year
and
a
half
ago,
The Select Board
created
a
governance
study
committee,
of
which
I'm
a
member.
we
are
doctors
and
lawyers
and
accountants,
and
teachers,
and
marketing
people,
local
business
people.
I'm
the
resident
stats
guy.
Overtime,
the
population
of
the
town
has evolved, it's grown
.
It's
more
diverse
than
it
was
100
years
ago.
We've
gone
from
an
agrarian
manufacturing
community
to
a
bedroom
community
for
the
city
of
Boston.
Lots
of
professionals
working
in
hospitals
and
universities,
law
firms
in
the
city.
People
tend
not
to
live
and
work
in
the
town,
and
that
has
impactsq
on
participation
in
town
governance.
The
charge
to
the
governance
study
committee
is
find
ways
to
boost
citizen
engagement.
we've
been
doing
our
due
diligence.
We've
been
researching,
we've
surveyed
residents,
we've
read
the
literature,
we've
interviewed
town
officials.
One
part
of
our
research,
and
that's
what
this
talk
is
about,
is
we
wanted
to
reach
out
to
towns
like
Sharon
to
find
out
what
are
they
doing,
what's
their
experience.
There's
some
comparative
research.
There
are
350
towns
in
Massachusetts.
We
have
time
constraints,
and
so
we're
looking
for
a
way
to
identify
a
smallish
number
of
communities
that
are
similar
to
us.
We
didn't
want
to
reinvent
the
wheel,
but
we
thought
that
modernizing
it
some
would
be
a
good
idea.
The
driving
question
covered
in
this
research
is
which
towns
are
similar
to
this
town.
A
little
bit
about
Sharon.
We
sit
in
South-eastern
Massachusetts.
We
are
not
too
far
from
Plymouth,
which
is
where
the
1620
May
Flower
landing
happened.
This
community
was
originally
populated
by
Wampanoag
peoples.
Europeans
arrived
in
163
7.
We're
about
halfway
between
Boston
and
Providence.
For
the
sports
fans
out
there,
we
are
next
door
to
where
the
New
England
Patriots
play
football.
Population
about
18,500,
which
is
quite
average
in
Massachusetts.
We
have
great
percentage
of
the
voters
of
the
population
are
registered
voters.
Yet
out
of
all
those
people,
we
get
2%
for
a
town
meeting.
Most
recently
in
May
of
2022.
This
was
the
scene
and
a
lot
of
that
is
COVID
related.
There
was
social
distancing
rules
in
effect,
but
turnout
is
low,
partly
because
of
COVID,
partly
because
of
factors
that
we
don't
fully
understand.
One
task
for
the
governance
study
community
is
to
consider
other
alternatives
to
town
meeting,
or
tweaks
and
enhancements to town meeting.
Under
state
law
in
an
open-town
meeting
to
participate,
you
have
to
be
in
the
room.
It's
broadcast
on
local
television,
but
you
have
to
be
present
to
speak
or
to
vote.
State
law
also
says
there's
three ways
to
run
local
government.
74%
of
the
communities,
the
large
majority
do
what
Sharon
does.
Open-town
meeting
once
or
twice
a
year.
A
small
number
have
what's
called
representative
town
meeting
in
which
voters
elect
their
neighbors,
maybe
a
few
hundred
of
them,
to
participate
and
vote
in
town
meeting.
Traditionally
cities
have
had
small
councils
with
a
mayor
or
administrator
of
some
kind.
Increasingly
that's
being
adopted
by
towns,
and
so
we're
looking
into
that.
For
this
talk,
the
task
is
identify
peer
towns,
that
we
can
then
we
could
then
interview
and
consult
with
and
reach
out
to
them.
I
mentioned
some
of
the
state
legal
constraints.
One
other
constraint
is
the
town
boards,
like
a
government
study
committee,
have
to
have
open
meetings.
Anything
we
do
and
decide
and
deliberate
about
has
to
be
in
public,
which
is
a
good
thing.
We
have
no
budget.
We
have
some
wonderful
staff
in
the
town
hall,
but
they
are
busy
doing
other
things
as
well.
Data
availability
was
a
mixed
story.
Plenty
of
data
available
about
characteristics
of
communities.
We're
really
interested
in
how
many
people
participate
in
local
government
and
there's
no
centralized
data
about
that,
so we
needed
to
hunt
for
proxies.
We
also
had
no
ability
to
compel
folks
in
other
towns
to
meet
with
us,
advise
us,
or
share
data
with
us.
We're
operating
in
a
topic
area
that
is
heavily
governed
by
tradition.
People
really
cleave
to
that
Norman
Rockwell inch.
We
came
up
with
a
three-
stage
plan.
As
a
committee,
we
brainstorm
variables,
say
why
do
people
participate?
Why
don't
people
participate?
Why
are
different
towns
different?
I
then
grabbed
some
data
from
voter
turnout
in
a
recent
state-wide
election
to
use
as
a
proxy
for
citizen
engagement.
Ran
some
models
in
JMP
to
identify
those
variables
that
seem
to
have
predictive
value.
The
committee
then
discussed
and
added some more variables
that
they
thought
were
important
on
the
town
meeting
dimension.
That
generated
20
predictor
columns,
which
I
knew
was
far
more
than
I
wanted
to
deal
with.
I
consulted
my
brain
trust
some
academic
colleagues
special
thanks
go
to,
Mia
Stevens
and
Ruth
Humble
at
Chomp,
who
advised
me
on
principal
components
analysis,
which
I'll
note
that
the
outset
was
not
part
of
my
comfort
zone,
so I
told
her
a
little
bit
about
that.
Then
I
ran
cluster
analysis.
That's the
main
event
today.
People
on
the
committee
understood
that
we
probably
want
to
be
talking
to
towns
of
comparable
size,
but
there's
more
to
similarity
than
size.
There's
more
to
similarity
than
being
a
geographic
neighbor.
Part
of
the
work
involved
instructing
the
committee
a
little
bit
on
cluster
analysis.
Just
in
case
anybody
watching
doesn't
have
much
background
in
this,
here's
how
I
did
it.
I
said
well,
we
can
look
at
population
and
something
else
at
the
same
time.
and maybe
though
that
something
else
has
an
impact
on
participation.
In
this
case,
the
Y
axis
was
a
single
family
property
tax
bills.
You
can
see
that
there's
a
bunch
of
towns
similar
in
size
to
Sharon,
but
which
might
have
very
different
tax
impacts.
The
idea
and
cluster
analysis,
if
you
are
going
to
work
in
two
dimensions,
choose
two
attributes
that
you
think
are
relevant
to
your
query,
spread
the
towns
out
on
those
two
dimensions,
and
then
identifying
a reasonable
number
of
towns
that
are
reasonably
similar
to
Sharon.
That's
a
big
idea
in
cluster
analysis.
Fortunately
we're
not
limited
to
two
attributes
or
two
dimensions.
We
can
have
more
than
that.
with
that,
I
think
you
now
know
enough
to
follow
the
demo.
Where
we're
walking
into
this
demo,
I
had used
gathered
data
from
a
variety
of
state
and
publicly
available
sources.
Used
query
builder
to
build
a
large
data
table
inspected
for
outliers
and
missing
data.
The
one real
outlier
is
the
city
of
Boston,
which
is
just
unique.
T
hat's
excluded
from
all
the
analysis.
A
little
bit
of
missing
this,
but
nothing
terrible.
I'm
going
to
be
showing
you
a
JMP
project.
Let
me
switch
gears,
move
into
the
demo
and
I
hope
that
I
do
this
correctly.
What
we're
looking
at
is
my
data
table
of
351
cities
and
towns.
The
first
several
columns
are
identification,
size
of
The Select Board,
their
legislative
option
name
of
community.
The
next
20
columns
are
our
predictors.
Just to
round
us
a
bit,
if
we
look
at
some
basic
descriptives
of
the
communities,
towns
in
Massachusetts
tend
to
be
on
the
small
size.
The
medium
is
only
10,000
people.
Sharon
is
quite
near
the
mean
community
size.
Terms
of
legislative
function,
74%
use
open-
town
meetings,
so we
are
in
good
company,
and
in
terms
of
the
size
of
The
Select Board
which
is,
another
thing,
the
governance
committee
is
looking
at
just
about
50/
50.
Half
of
the
town's
with
a
Select
Board
have
three
members,
half
have
five.
We've
got
these
20
predictors.
One
issue
that
comes
up
pretty early
in
the
analysis
is the
linearity.
Here
I
have
five
variables
that
all
speak
to
the
size
and
the
electric,
the
size
of
the
town.
You
can
see
that
there
are some
very
strong
correlations.
We generally
speaking,
don't
want
to
deal
with
so
much of the
linearity.
One
way
out
is
principal
components
analysis.
At
this
point,
not
quite
ready
to
jump
into
clustering,
but
want
to
take
those
20
columns
and
distil
them
down,
conserve
as
much
information
as
possible,
but
reduce
the
redundancy
and
collinearity
across
columns.
To
do
that,
principal components
analysis
is
an
excellent
option.
I
don't
have
the
ability
today
to
give
a
full
crash
course
in
principal
components
analysis,
but
we
can
see
that
we
have
variables
that
seem
to
be
overlapping
in
terms
of
their
message.
We
also
can
see
that
when
you
give
a
PCA
20
columns,
it
initially
comes
up
with
20
components.
The
first
few
of
which
seem
to
capture
most
of
the
variability.
We
have
to
make
a
decision
about
how
many
principal
components
to
use
and
what
they
represent.
For
this,
the
screen
plot
is
helpful
and
we're
looking
for
a
kink
or
an
elbow
in
the
plot.
That
seems
to
happen
somewhere
down
here,
around
4, 5, 6
components.
If
we
consult
the
loading
matrix
to
see
how
different
variables
associate
load
into
different
components,
we
can
begin
to
subjectively
assign
meaning
to
the
components.
I'll
cut
to
the
chase.
We
selected
six
principal
components
as
being
informative
for
the
purposes
of
cluster
analysis,
and
came
up
with
some
interpretations
that
made
sense
to
us.
Things
like
how
big
is
the
town?
How
affluent
is
the
town?
How
fast
is
it
growing?
Now
we're
ready
for
clustering.
Back to
JMP.
There
are
two
basic
approaches
to
clustering.
You
might
think
of
them
as
a
top-
down
and
a
bottom-
up.
Both
of
them
take
the
raw
data,
standardize
it,
and
then
compute
Euclidean
distances
for
each
pair
of
rows
in
the
data
table
for
each
pair
of
communities.
Taking
into
account
six
factors,
six
principle
components.
Size,
affluence,
education,
things
like
that,
which
ones
are
similar
to
Sharon
in
hierarchical
clustering?
The
report
starts
us
off
with
a
dazzling
graph
that
with
350
rows,
this
is
hard
to
interpret.
Let's
begin
we'll
come
back
to
it.
Begin
with
something
that's
a
little
easier
to
interpret,
which
is
the
cluster
summary.
In
the
hierarchical
method,
JMP
has
found
for
us
16
clusters.
I
can
tell
you
because
I
peaked
that
Sharon
turns
out
to
be
in
cluster
15.
Sharon
and
23
other
towns.
For
example,
if
you
scan
down
the
affluence
column,
we
see
that,
again
these
are
standardized
scores,
these
are
the
most
affluent
towns
in
the
state.
If
we
come
over
here
to
the
growth
column,
and
this
is
largely
growth
in
the
housing
units
and
population.
The
least
growth,
in
fact,
some
negative
growth.
If
we
come
back
up
here.
Now,
having
looked
at
the
cluster
summary,
all
of
the
clusters
have
been
identified
and
colored.
JMP
gives
us
a
cut
point.
If
we
zoom
in
on
cluster
15.
Let's make that a little bigger.
Sharon
is
here
in
the
center.
It's
nearest
Euclidean
neighbor is
Winchester,
which
is
about
an
hour's
drive.
We
now
have
a
provisional
list
of
towns
to
consult
with.
All
right,
so
that's
a
crash
course
in
the
hierarchical clustering.
I'll
move
out
to
the
K-Means.
Hierarchical
is
bottom
up.
We
start
with
350
individual
towns
as
clusters.
We
interrogate
the
distance
matrix,
all
the
parallelized
distances,
find
those
two
towns
that
are
nearest
to
each
other,
they
form
a
cluster.
We
take
the
mean
distance
of
that
cluster.
Now
we're
either
looking
for
the
next
two
nearest
towns
or
the
next
town
that's
closest
to
that
cluster,
and iteratively
process
for
the
tree
until
we
have
one
gigantic
cluster
of
350 towns.
With
K-M
eans
clustering.
We
flip
the
process.
We
start
with
350 towns
in
one
cluster
and
then
begin
slicing
and
dividing
in
multiple
dimensions.
In
this
approach,
same
utility
and
distances,
same
distance
matrix,
we
end
up
with
Sharon
being
in
cluster
number 4,
with
a
full
compliment
of
33 towns.
We
automatically
get
a
cluster
means
picture.
Again,
very
affluent
low
growth,
not
necessarily
the
lowest,
but
low
growth
again.
We
get
slightly
different
results.
I
think
in
the
interest
of
time,
I
will
show
you
one
other
graph.
There's
various
things
to
look
at,
but
let's
look
at
the
parallel
coordinate
blocks.
What
is
this tool?
We
have
16
clusters,
and
by
the
way
in
K-Means,
it's
up
to
the
user
to
specify
the
number
of
clusters.
I
chose
16
as
a
starting
point
because
that's
what
hierarchical
gave
us.
H
ere
we
are,
cluster
four.
The
dark
brown
line
is
Sharon.
Here
we
see
the
six
characteristics,
the
six
principal
components.
F
or
example,
if
we
compare,
how
is
cluster
4
different
from
cluster 3
let's
say,
or
cluster
5,
maybe
similar
in
sizes.
Cluster
five,
less
affluent.
Property
values
are
a
little
lower.
Permanent
population
refers
to
their
communities
with
universities,
hospitals,
prisons,
so
forth,
vacation
homes,
snowbirds
who
leave
for
the
winter.
Towns
differ
in
terms
of
their
permanent
populations
in
cluster
three,
much
lower.
Here's
where
we
find
our
university
towns.
I
just
popped
up
the
town
of
Shirley,
Massachusetts
as
the
state's
largest
maximum
security
prison.
I
don't
know
if
we
consider
these
folks
permanent
residence
or
not,
but
any
event.
We've
done
two
different
clustering
methods.
Let's
take
a
look
at
how
the
results
compare.
I
saved
within
each
clustering
method,
the
cluster
assignments
for
each town,
created
binary
variables.
Are
you
in
the
same
cluster
as
Sharon
or
are
you
in
a
different
cluster?
I
also,
just
as
an
aside,
JMP
has
lots
of
wonderful
built
in
geographic
maps.
It
does
not
have
a
built-
in
map
showing
municipality
borders
within
the
state
of
Massachusetts.
But
it
turns
out
that
with
JMP
it's
fairly
easy
to
create
a
new
geographic
map.
I
was
able
to
do
this
without
very
much
work
at
all.
Here
are
the
results
of
hierarchical
clustering,
cluster
15.
Sharon
is
here.
It's
similar
towns
are
in
blue.
I
was
pleased
to
see
that
my
little
tiny
hometown
of
Marbleh
ead
is
similar
to
the
place
I
moved
to.
Hierarchical
clustering
15 gives us these 24
towns.
K-Means clustering
and
some
more
towns,
but
there's
an
awful
lot
of
overlap.
I
also,
just
out
of
curiosity,
look
to
identify
the
33
towns about 10% of the state
that
is
most
similar to Sharon.
This
is
a
larger
group.
Again,
an
awful
lot
of
repeats.
A
lot
of
repeats.
That
last
approach
also
gave
us
some
other
advantages.
I
want
to
now
shift
back
to
PowerPoint
and
talk
about
some
of
those.
O
ne
last
point
before
I finish
the
demo.
We
were
also
curious
to
ask...
Mostly
our
goal
was,
who
shall
we
interview?
Who
shall
we
call
in
to
meet
publicly
with
our
committee?
But
while
we're
at
it,
let's
see
what
our
peers
do
in
terms
of
governance.
State-wide,
open-town
meetingâ
OTMâ
dominates
74%
and
there
is
no
dominant
fourth
size.
If
we
look
at
who's
in
our
cluster,
let
me
use
K-Means,
because
it's
a
little
bit
larger
group.
That
74%,
jumps
up
to
85%
with
time
meeting.
By
a
two
to
one
margin,
towns
have
five-member
Select
Boards.
Now,
this
isn't
definitive
as
to
channel
my
mother.
If
all
the
other
towns
jumped
off
the
Empire
State
Building,
we
wouldn't
necessarily
want
to
jump
off.
But
it's
interesting
to
note
that
the
towns
most
similar
to
Sharon
favor
the
five-member
Board
and
are
even
more
inclined
to
open-
town
meeting.
With
that,
let
me
get
back
to
some
conclusions.
So what
did
we
learn?
One
thing
we
learned
was
the
geographic
proximity
is
uninformative
in
those
maps,
none
of
the
abutting
towns
came
up
blue.
Our
most
similar
communities
are
not
our
next
door
neighbors.
As
I
just
noted,
open-town
meeting
in
five-
member
boards
really
predominate.
So
what
did
we
do?
This
work
actually
happened
several
months
ago.
We
were
able
to
prioritize
our
outreach,
begin
contacting
those
towns
most
similar
to
us.
Many
were
extremely
cooperative
and
shared
a
lot
of
information
and
data.
We
also
didn't
want
to
assume
that
open-town
meeting
was
the
only
way
to
consider,
so we
wanted
to
talk
to
people
with
representative
or
councils.
Those
Euclidean
distances
became
instructive
in
terms
of,
okay,
none
of
our
immediate
neighbors,
closest
neighbors
use
town
council
or
representative
town
meeting.
But
which
RTM
town,
which
council
town
is
most
like
us?
And we
contacted
those
folks
as
well.
W
e
went
from
having
to
contemplate
outreach
to
350
towns
in
a
limited
amount
of
time
and with no
money and staff,
to focused sampling
method.
Then
because
town
officials
talk
to
one
another
and
they
are
professionally
active,
that
led
us
to
other
interviewees.
With
that,
I
think
that's
about
my
time.
I
hope
this
has
been
interesting
and
constructive.
Thank
you
for
coming
and
I
hope
you
enjoy
the
rest
of
the
program.
