A key challenge faced by social organisations is the last mile gap -- communicating the insights and actions to the masses.
The problem is one of attention. Very few people spend time on anything that appears unengaging.
The problem is also one of complexity. Most of the audience is lost if the message is not communicated in the form of a simple story.
Data visualisation provides a mechanism for visually engaging stories that can can explain complex results in a simple fashion. It is seeing widespread adoption among the media, NGOs and the Government.
This Webinar discusses examples of how data visualisation has provided insights in areas of social interest, and has communicated these to a broader audience. We will what techniques and support mechanisms are available in the market today to enable visual storytelling.
http://www.eventbrite.com/e/data-visualization-for-social-problems-tickets-15044842529
4. Most discussions of decision-making
assume that only senior executives
make decisions or that only senior
executives’ decisions matter. This is a
dangerous mistake…
Peter F Drucker
Data generation and analysis are not sufficient.
Consuming it as a team and acting in cohesion is.
5. SHOW
me what is happening
with the data
EXPLAIN
to me why it’s
happening
Allow me to
EXPLORE
and figure it out
Just
EXPOSE
the data to me
Low effort High effort
High effort
Low effort
Creator
Consumer
THERE ARE MANY WAYS TO AID DATA CONSUMPTION
6. SHOW
me what is happening
with the data
EXPLAIN
to me why it’s
happening
Allow me to
EXPLORE
and figure it out
Just
EXPOSE
the data to me
7.
8.
9.
10. SHOW
me what is happening
with the data
EXPLAIN
to me why it’s
happening
Allow me to
EXPLORE
and figure it out
Just
EXPOSE
the data to me
11. EDUCATION
PREDICTING MARKS
What determines a child’s marks?
Do girls score better than boys?
Does the choice of subject matter?
Does the medium of instruction matter?
Does community or religion matter?
Does their birthday matter?
Does the first letter of their name matter?
17. DETECTING FRAUD
“
We know meter readings are
incorrect, for various reasons.
We don’t, however, have the
concrete proof we need to start the
process of meter reading
automation.
Part of our problem is the volume
of data that needs to be analysed.
The other is the inexperience in
tools or analyses to identify such
patterns.
ENERGY UTILITY
18. This plot shows the frequency of all meter readings from
Apr-2010 to Mar-2011. An unusually large number of
readings are aligned with the tariff slab boundaries.
This clearly shows
collusion of some form
with the customers.
Apr-10 May-10Jun-10Jul-10 Aug-10 Sep-10 Oct-10 Nov-10 Dec-10 Jan-11 Feb-11 Mar-11
217 219 200 200 200 200 200 200 200 350 200 200
250 200 200 200 201 200 200 200 250 200 200 150
250 150 150 200 200 200 200 200 200 200 200 150
150 200 200 200 200 200 200 200 200 200 200 50
200 200 200 150 180 150 50 100 50 70 100 100
100 100 100 100 100 100 100 100 100 100 110 100
100 150 123 123 50 100 50 100 100 100 100 100
0 111 100 100 100 100 100 100 100 100 50 50
0 100 27 100 50 100 100 100 100 100 70 100
1 1 1 100 99 50 100 100 100 100 100 100
This happens with specific
customers, not randomly.
Here are such customers’
meter readings.
Section Apr-10 May-10Jun-10 Jul-10 Aug-10 Sep-10 Oct-10 Nov-10 Dec-10 Jan-11 Feb-11 Mar-11
Section 1 70% 97% 136% 65% 110% 116% 121% 107% 114% 88% 74% 109%
Section 2 66% 92% 66% 87% 70% 64% 63% 50% 58% 38% 41% 54%
Section 3 90% 46% 47% 43% 28% 31% 50% 32% 19% 38% 8% 34%
Section 4 44% 24% 36% 39% 21% 18% 24% 49% 56% 44% 31% 14%
Section 5 4% 63% -27% 20% 41% 82% 26% 34% 43% 2% 37% 15%
Section 6 18% 23% 30% 21% 28% 33% 39% 41% 39% 18% 0% 33%
Section 7 36% 51% 33% 33% 27% 35% 10% 39% 12% 5% 15% 14%
Section 8 22% 21% 28% 12% 24% 27% 10% 31% 13% 11% 22% 17%
Section 9 19% 35% 14% 9% 16% 32% 37% 12% 9% 5% -3% 11%
If we define the “extent of
fraud” as the percentage
excess of the 100 unit
meter reading,
the value varies
considerably
across sections,
and time
New section
manager arrives
… and is
transferred out
… with some
explainable
anomalies.
Why would
these happen?
19. SHOW
me what is happening
with the data
EXPLAIN
to me why it’s
happening
Allow me to
EXPLORE
and figure it out
Just
EXPOSE
the data to me
… to inform and to entertain
20. SHOW
me what is happening
with the data
EXPLAIN
to me why it’s
happening
Allow me to
EXPLORE
and figure it out
Just
EXPOSE
the data to me
23. Based on the results of the 20 lakh
students taking the Class XII exams
at Tamil Nadu over the last 3 years,
it appears that the month you were
born in can make a difference of as
much as 120 marks out of 1,200.
June borns
score the lowest
The marks shoot
up for Aug borns
… and peaks for
Sep-borns
120 marks out of
1200 explainable
by month of birth
An identical pattern was observed in 2009 and 2010…
… and across districts, gender, subjects, and class X & XII.
“It’s simply that in Canada the eligibility
cutoff for age-class hockey is January 1. A
boy who turns ten on January 2, then,
could be playing alongside someone who
doesn’t turn ten until the end of the year—
and at that age, in preadolescence, a
twelve-month gap in age represents an
enormous difference in physical maturity.”
-- Malcolm Gladwell, Outliers
24. LET’S LOOK AT 15 YEARS OF US BIRTH DATA
This is a dataset (1975 – 1990) that has
been around for several years, and has
been studied extensively. Yet, a
visualization can reveal patterns that
are neither obvious nor well known.
For example,
• Are birthdays uniformly distributed?
• Do doctors or parents exercise the C-section option to move dates?
• Is there any day of the month that has unusually high or low births?
• Are there any months with relatively high or low births?
Very high births in September.
But this is fairly well known.
Most conceptions happen during
the winter holiday season
Relatively few births during the
Christmas and Thanksgiving
holidays, as well as New Year and
Independence Day.
Most people prefer not
to have children on the
13th of any month, given
that it’s an unlucky day
Some special days like April
Fool’s day are avoided, but
Valentine’s Day is quite
popular
More births Fewer births … on average, for each day of the year (from 1975 to 1990)
25. THE PATTERN IN INDIA IS QUITE DIFFERENT
This is a birth date dataset that’s
obtained from school admission data
for over 10 million children. When we
compare this with births in the US, we
see none of the same patterns.
For example,
• Is there an aversion to the 13th or is there a local cultural nuance?
• Are holidays avoided for births?
• Which months have a higher propensity for births, and why?
• Are there any patterns not found in the US data?
Very few children are born in the
month of August, and thereafter.
Most births are concentrated in
the first half of the year
We see a large number of
children born on the 5th, 10th,
15th, 20th and 25th of each month
– that is, round numbered dates
Such round numbered patterns a
typical indication of fraud. Here,
birthdates are brought forward
to aid early school admission
More births Fewer births … on average, for each day of the year (from 2007 to 2013)
26. THIS ADVERSELY IMPACTS CHILDREN’S MARKS
It’s a well established fact that older
children tend to do better at school in
most activities. Since many children
have had their birth dates brought
forward, these younger children suffer.
The average marks of children “born” on the 1st, 5th, 10th, 15th etc. of the
month tend to score lower marks.
• Are holidays avoided for births?
• Which months have a higher propensity for births, and why?
• Are there any patterns not found in the US data?
Higher marks Lower marks … on average, for children born on a given day of the year (from 2007 to 2013)
Children “born” on round numbered days score lower marks on average,
due to a higher proportion of younger children
27. 0%
10%
20%
30%
40%
50%
60%
0 2 4 6 8 10 12 14 16 18
# contestants
Winnermargin
More contestants did not reduce the winner margin
Karnataka, Assembly Elections 2008
29. Adult
Educat
ion
Adminisr
ative
Reforms
Agric
ultura
l
Mark
eting
Agricul
tureAnimal
Husban
dry
Coope
rative
Excis
e
Fina
nce
Fishe
ries
Fishe
ries
&
Inlan
d
wate
r
trans
port
Food &
Civil
Supplies
Fore
st
Fuel
Haz &
Wakf
Health
and
family
welfare
Higher
Educati
on
Hom
e Horticu
lture
Hous
ing
Info
rma
tion
&
Tec
hno
logy
Kannad
a &
Culture
Labo
ur
Law
&
Hu
man
Righ
ts
Major &
Medium
Industri
es
Medical
Educatio
n
Medium
and
Large
Industrie
s
Mines
&
Geolo
gy
Minor
Irrigati
on
Muz
rai
P.W.D.
Parlia
mentar
y
Affairs
and
Human
Rights
Plan
ning
Planni
ng
and
Statist
ics
Primary
and
Secondary
Education
Primary
Educati
on
Pris
on
Pub
lic
Libr
ary
Reve
nue
Rural
Developme
nt and
Panchayat
Raj
Rural
Wate
r
Suppl
y
Rural
Water
Supply
and
Sanitat
ion
Seri
cult
ure
Smal
l
Scale
Indu
strie
s
Small
Indust
ries
Social
Welfar
e
Suga
r
Textil
e
Touri
sm
Tran
sport
Transp
ortatio
n
Urban
Develo
pment
Water
Resourc
es
Woman &
Child
Developm
ent
Youth
and
Sports
Yout
h
Servi
ce &
Spor
ts
BJP focus
JD(S)
focus
INC focus
What topics did parties focus on during questions?
Karnataka, 2008-2012
30. P.W.D.
Health and
family
welfare
Reven
ue
Rural
Developme
nt and
Panchayat
Raj
Social
Welfar
e
Urban
Develo
pment
Water
Resour
ces
Minor
Irrigati
on
Fuel
Hous
ing
Agric
ulture
Primary
Educati
on
Primary and
Secondary
Education
Woman &
Child
Developme
nt
Higher
Educati
on
Hom
eCoope
rative
Fore
st
Adminisra
tive
Reforms
Labo
ur
Food &
Civil
Supplies
Tour
ism
Fina
nce
Animal
Husba
ndry
Transpo
rtation
Hortic
ulture
Muzr
ai
Haz &
Wakf
Trans
portMedical
Educatio
n
Medium
and Large
Industries
Excis
e
Major &
Medium
Industrie
s
Kannad
a &
Culture
Text
ile
Fishe
ries
Parliam
entary
Affairs
and
Human
Rights
Adult
Educati
on
Rural
Water
Supply
and
Sanitati
on
Mines
&
Geolog
y
Small
Industr
ies
Youth
and
Sports
Suga
r
Planni
ng and
Statisti
cs
Agricul
tural
Marke
ting
Rural
Water
Supply
Fisher
ies &
Inland
water
trans
port
Small
Scale
Indus
tries
Yout
h
Servi
ce &
Sport
s
Seric
ultur
e
Law
&
Hum
an
Righ
ts
Priso
n
Plan
ning
Info
rma
tion
&
Tec
hnol
ogy
Publ
ic
Libr
ary
What topics did the young & old focus on during questions?
Karnataka, 2008-2012
Young Old
31. SHOW
me what is happening
with the data
EXPLAIN
to me why it’s
happening
Allow me to
EXPLORE
and figure it out
Just
EXPOSE
the data to me
… to connect the dots for your readers
32. SHOW
me what is happening
with the data
EXPLAIN
to me why it’s
happening
Allow me to
EXPLORE
and figure it out
Just
EXPOSE
the data to me
40. EXPLORING THE MAHABHARATA
How does Mahabharata, one of the largest epics
with 1.8 million words lend itself to text analytics?
Can this ‘unstructured data’ be processed to extract
analytical insights?
What does sentiment analysis of this tome convey?
Is there a better way to explore relations between
characters?
How can closeness of characters be analysed &
visualized?
41. SHOW
me what is happening
with the data
EXPLAIN
to me why it’s
happening
Allow me to
EXPLORE
and figure it out
Just
EXPOSE
the data to me
… to allow your users to tell stories
42. VISUALISATION IS IMPERATIVE FOR
DATA → INSIGHTS → ACTION
Spot the unusual Communicate patterns Simplify decisions
43. We handle terabyte-size data via non-traditional analytics and visualise it in real-time.
A data analytics and visualisation company
gramener.com
for more examples
Notas del editor
The earliest data visualisations were seen as far back as the mid-19th century. This is a visualisation prepared by Florence Nightingale for Queen Victoria during England’s war with France. Many people had tried obtaining funding for hospitals, but most of the budget had been restricted to the war effort. This visual shows, month-on-month, the number of people that died for the war effort. In RED are the people who died out of war wounds, in BLACK the number of people that died from other war related causes and in BLUE the number of people who died due to avoidable hospital diseases. A war is won by people and the main reason England was losing people wasn't bullets or swords but diseases. Florence Nightingale used this visualisation to request funding for hospitals, got it, and England won the war.
In 1854, London suffered from a Cholera epidemic. The popular theory at that time was that cholera was caused by pollution. Dr. John Snow was sceptical about this. By talking to local residents, he identified the source of the outbreak as the public water pump on Broad Street. Dr. Snow used this map to illustrate the cluster of cholera cases around the pump. He also used statistics to illustrate the connection between the quality of the water source and cholera cases. This visual was convincing enough to persuade the local council to disable the well pump by removing its handle, is regarded as the founding event of the science of epidemiology.
Cue 1: “Of late, enabling these interactions involves a lot of big data… and consuming this data is hard…”
Cue 2: A few seconds after George Bush
Gramener is a data analtyics and visualisation company. We have the ability to process data at a small and a large scale. We analyse the data to find non-intuitive insights that lie hidden behind it and present it as a visual story that makes those insights obvious in real time.