This talk combines two passions of mine: data and trail organizations! Presented at the California Trails & Greenways Conference, May 2013.

1. CREATING,
ENJOYING,
AND
MAINTAINING
TRAILS:
“WHAT’S
DATA
GOT
TO
DO
WITH
IT?”
Linda
G.
George,
Ph.D.
Photo:
Mt.
Tallac
above
S.
Lake
Tahoe

2. OVERVIEW
• “Big
Data”
– Basic
deJinitions
– Examples
– Steps
– Skills
– &
about
trails…

3. GOALS
FOR
THE
SESSION
• Understand
more
about
this
global
phenomenon
• Spark
new
ideas
for
your
use
of
data,
whether
you’re
in
a
small,
medium,
or
large
organization
• Give
you
pointers
to
helpful
resources

4. WHAT
IS
“BIG
DATA”?

5. (WARNING…)

6. WHAT
IS
“BIG
DATA”?
• An
explosion
in
the
amount
of
data
available
• Inexpensive
ways
to
store
it
• Sheer
quantity
changes
what
we
can
do
“The
deluge”

7. WHAT
IS
“BIG
DATA”?
Graphic:
Diya
Soubra.
3Vs:
Gartner,
2001

8. WHAT
IS
BIG
DATA?
The
0’s
• Megabyte
• Gigabyte
• Terabyte
• Petabyte
• Exabyte
1,000,000
1,000,000,000
1,000,000,000,000
=
1k
GB
1,000,000,000,000,000
1,000,000,000,000,000,000

9. Data
generated
in
one
minute
on
the
Internet,
ca.
2011

10. WHAT
IS
“BIG
DATA”?
• Structured
data
–
traditional,
has
a
set
format)
• Unstructured
data
-­‐
forum
posts,
blogs,
ratings,
websites,
environmental
sensors,
books,
videos,
…
– Breakthroughs
in
analyzing
unstructured
data
Source:
mediabistro.com

11. WHAT
IS
“BIG
DATA”?
• “Big
Data”
is
not
completely
about
the
data:
it
reJlects
a
paradigm
shift.
• Data
has
new
prominence
in
the
decision
making
process
of
individuals
and
organizations.
• New
technologies
have
emerged
through
companies
like
Google
and
Yahoo!
• These
technologies
can
be
useful
to
other
organizations,
large
and
small.

12. ISSUES
AND
CONCERNS
• Assumption:
Data
+
Technology
=
“Actionable
Insights,
Magic
Ponies,
and
Superpowers”

13. ISSUES
AND
CONCERNS
• Privacy
• Bias
• Risk:
Jinding
patterns
and
connections
where
none
exist
Source:
hCp://m.xkcd.com/552/

14. WHERE
IS
IT?

15. BIG
PLAYERS
• Google,
Facebook,
NetJlix,
Etsy,
eBay,
Yahoo,
Yelp,
LinkedIn,
Orbitz,
Twitter,
…
Walmart,
Zions
Bancorp.,
the
medical
research
world,
…

16. “THE
CLOUD”
• Where
is
all
of
this
data
gathered,
stored,
and
analyzed?
• Amazon
Web
Services
– Large,
Jlexible
storage
and
computing
power
– A
place
to
store
large
quantities
of
data;
an
alternative
to
in-­‐house
storage

17. IN-­‐HOUSE
STORAGE
• Heating/cooling
system,
Google
-­‐
Oregon

18. IN-­‐HOUSE
STORAGE
• Google
servers
in
Georgia

19. EXAMPLES

20. THE
“BIG
GUYS”
Facebook:
950,000,000
users,
generating
500+
TB
of
new
data
daily:
visiting
a
page,
uploading
a
photo,
reading
an
update
via
link.

21. THE
“BIG
GUYS”
Thomas
Guides
Google
Maps
-­‐>

22. HEALTH
CARE
• Genomic
research

23. HEALTH
CARE
• Research
on
drug
side
effects
and
interactions

24. EMERGENCY
RESPONSE
• 10TB
of
data
assisted
the
FBI
in
investigating
the
Boston
Marathon
tragedy:
call
logs,
city
cameras,
local
businesses,
gas
stations,
media
outlets,
and
spectators
–
videos
and
photos

25. TRAILS
-­‐
?

26. TRAILS…
• NPS
Visitor
Centers
“The
technology
should
help
people
have
an
enhanced,
deeper,
more
meaningful
connection
with
the
real
thing”
(J.
Washburn,
NPS)

27. TRAILS…
• 2/14/13:
Outdoor
Industry
Association
released
a
state-­‐by-­‐
state
reports
on
the
economic
beneJits
of
recreation.
http://www.outdoorindustry.org/
advocacy/recreation/economy.html

28. TRAILS…
• Economic
beneJits
–
quantify
in
new
ways?
– Tourism
– Events
– Property
value
– Health
care
savings
– Jobs
and
investment
– Consumer
spending
(equipment,
horses,
bikes)
(from
americantrails.org)

29. TRAILS…
• Florida
DEP,
OfJice
of
Greenways
&
Trails
– The
state’s
trail
corridor
data
was
updated
through
online
comments
from
individuals
and
organizations,
who
were
later
able
to
view
data
interactively
online.
– “In
less
than
twelve
months,
the
trail
opportunity
corridor
data
for
the
entire
state
was
updated.”
(5
years
ago)

30. “INTERNET
OF
THINGS”

31. THE
“INTERNET
OF
THINGS”

32. • The
“quantiJied
self”:
blood
pressure,
sleep,
body
mass,
exercise,
etc.
Data
from
a
person’s
daily
actions
and
behavior.
Busterbenson.com

33. QuanJfiedself.com

34. GLASSES,
WRISTWATCHES…

35. GLASSES,
WRISTWATCHES…
• http://www.google.com/glass/start/how-­‐it-­‐feels/

36. TOOLS:
FIRST
STEPS

37. TOOLS
Google:
website
analytics
• How
many
people
look
at
your
site?
• How
do
people
Jind
it?
• What
are
they
looking
at?
• What
do
we
want
them
to
do
on
the
site,
and
are
they
doing
those
things?

39. A/B
TESTING

40. xkcd.com/773‎

41. Facebook
data

42. FACEBOOK
INSIGHTS
Implications
for
advocacy,
programs
and
fundraising:
• How
many
people
on
facebook
know
about
your
organization
and
care
about
it,
and
how
deeply?
• What
do
they
care
about
the
most?
• What
communications
reach
the
most
people?
• What
do
you
know
about
your
facebook
fans?
source:
socialbright.org

43. TWITTER

44. HOW?
Photo:
The
New
Yorker

45. HOW?
General
guidelines:
• Start
small,
build
on
successes
–
iterative
• Consider
“medium
data”:
you
don’t
need
lots
of
data
to
do
something
new
• Leave
room
for
experiments,
failures:
explore
–
hypothesize
–
test
–
repeat
• Celebrate
successes!

46. 1.
DEFINE
YOUR
GOAL
Key
result
areas:
– Increase
volunteer
hours
a
speciJic
amount?
– Achieve
a
new
fundraising
goal?
– Create
a
compelling
argument
for
a
trail
proposal?
– Understand
more
about
park
or
trail
use:
access
to
entrance,
weekly/seasonal
patterns,
…?

47. 2.
COLLECT
– Transaction
information:
memberships,
event
registrations,
certiJications,
etc.
– Social
data:
website
analytics,
social
media
sharing
– Sensor
data,
GPS
data,
census
data
Can
various
types
of
data
–
from
inside
and
outside
of
the
organization
-­‐
be
pulled
together
in
a
new
way?

48. 3.
ANALYZE
• What
can
it
tell
you?
– Spend
more
time
learning
from
your
data
than
gathering
it.
– “Insights
require
reJlection,
not
just
counting

49. 4.
ACT
• Use
insights
to
enhance,
revise
and
innovate
programs
and
services.
For
example…
• Tailor
your
use
of
social
media
for
your
audience
• Create
online
communities,
encourage
interaction
and
dialog
to
meet
identiJied
issues
or
needs
• Help
tell
the
story
about
how
you’re
making
a
difference
in
your
community

50. GETTING
STARTED
www.socialbrite.org

51. WHO?

52. SKILLS
Source:
DrewConway.com

53. VISUALIZATION…
• Here
is
Marvin
the
Martian.
Caption
“The
Jirst
image
has
now
been
received
from
Curiosity
on
Mars”
http://www.facebookstories.com/
stories/2200/data-­‐visualization-­‐
photo-­‐sharing-­‐explosions

54. VISUALIZATION:
HOW
MARVIN
THE
MARTIAN
WENT
VIRAL

55. STAFFING
• If
you
don’t
have
any
“data
geeks”
on
staff
–
how
about
volunteers?

57. “CANOPY”
PROJECT
FOR
NYC
PARKS

58. CONTESTS
• Bike
sharing
program
in
Boston:
“Hubway
Data
Visualization”
challenge
• User
engagement
+
results
• Example:
russellgoldenberg.com/hubway/

59. RESOURCES
–
BRIEF
OVERVIEW

60. “OPEN
DATA”
Google.com/trends

61. DATA
SOURCES
• Data.gov
United
States:
raw
data,
geo
data,
and
tools)
• U.S.
Census
http://www.census.gov
• Universities,
such
as
http://www.icpsr.umich.edu
• Open
portals
to
scientiJic
literature,
e.g.
nature.com
• The
Guardian
www.guardian.co.uk/data
(data
sets,
ideas,
tools)
• Sites
that
gather
links
to
data
sets,
such
as
datahub.io,
Infochimps,
Factual

62. DATA
SOURCES
• Less
obvious:
– Social
network
proJiles
– Social
commentary:
user
forums,
twitter,
facebook
“likes”
– Activity-­‐generated
data:
mobile
device
log
Jiles,
sensor
data,
application
logs,
…
– “Scraping”
websites
– Commercial
data
providers

63. READING
• http://measurenetworkednonproJit.org
• Fundraising
Analytics:
Using
Data
to
Guide
Strategy
• Head
First
Data
Analysis:
A
Learner’s
Guide
to
Big
Numbers,
Statistics,
and
Good
Decisions
• Building
Data
Science
Teams

64. TECHNICAL
SKILLS
• Tools
vary,
depending
on
your
questions
– Excel
– Python*
– R
statistical
software*
– Database
software
such
as
MySQL*
*
Open
source:
free

65. TECHNICAL
SKILL
DEVELOPMENT
• Coursera,
EdX,
Udacity,
Khan
academy,
…
“MOOCs”
• “Hackathons”
in
local
communities
• Meetups
• Kaggle.com
• College
courses
and
certiJicate
programs

66. COMMENTS?
• Discussion:
– New
ideas,
things
to
try
with
data
in
your
organization?
– Any
particular
challenges
you’d
like
to
address?

67. GOALS
• Understand
more
about
“big
data”
• Spark
ideas
for
using
data
in
new
ways,
whether
you’re
in
a
small,
medium,
or
large
organization
• Give
you
pointers
to
helpful
resources

68. THANKS!

69. TYPES
OF
ANALYSES
•
Analytics
– Google
Analytics
for
your
website
– Data
mining*
– Sentiment
analysis
– Sensor
data
(&
phones/devices,
etc.)
– Biostatistics
– Machine
learning:
train
computers
to
Jind
patterns*
– Data
science*
– Natural
language
processing
– Signal
processing
– Business
analytics
– Econometrics
*
Large
Volume,
Variety,
and
Velocity
of
data