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Visual Insights of the
World’s Largest Democracy
By B.Ganes Kesari & Shravan Kumar
ODC 2014, ISB
History of Elections in India
Interactively explore the historical view of General Elections
AGENDA
Some Interesting Insights
Discuss key insights across various perspectives
Counting Day Unfolds
How real-time visual analytics were served on the Live Results
What do Elections remind us of ?
Massive Rallies..
Myriad Symbols & Colours..
Seasonal businesses..
High-decibel campaigning..
…and Victory laps.
~300 Parties fielding 8000 candidates
~800 Mn Registered Voters
~1 Mn booths served by 20 Mn people
~21,000 Votes/sec of live results
Varied data on several parameters
A Big Data problem… in every sense
But General Elections also entail…
Can we visualize the results of
every single Lok Sabha election
since Independence?
https://gramener.com/election/parliament
On the other hand, which party
has the most losses in Lok Sabha
elections?
https://gramener.com/election/parliament#story.ddp
Which constituency had the most
number of candidates ever?
https://gramener.com/election/parliament#cartogram?METRIC=CANDIDATES&YEAR=1996
http://ibn.gramener.com/cartogram/?ST_NAME=Tamil%20Nadu
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
0 2 4 6 8 10 12 14 16 18
# contestants
Winnermargin
More contestants did not reduce the winner margin
Karnataka, Assembly Elections 2008
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
0 2 4 6 8 10 12 14 16 18
# contestants
Runner-upmargin
More contestants did reduce the runner-up margin
Karnataka, Assembly Elections 2004
Does age make a difference?
Do old candidates win less often?
1%
2%
4%
6%
9%
11%
14%
11%
16%
18%
22% 22%
33%
0%
10%
20%
30%
40%
25-30 30-35 35-40 40-45 45-50 50-55 55-60 60-65 65-70 70-75 75-80 80-85 85-90
0
500
1000
1500
2000
2500
Win %
The number of winning candidates as a % of
candidates in the age group
Candidates
The number of candidates in each
age group
LokSabha(2004onwards)
2%
4%
6%
9%
12%
15%
17%
15%
16%
18% 18%
20%
27%
0%
10%
20%
30%
25-30 30-35 35-40 40-45 45-50 50-55 55-60 60-65 65-70 70-75 75-80 80-85 85-90
0
2000
4000
6000
8000
10000
12000
14000
Win %
The number of winning candidates as a % of
candidates in the age group
Candidates
The number of candidates in each
age group
Assemblyelections(2004onwards)
What about wealth? How is that
distributed?
http://times.gramener.com/candidates/
RS 1 LAKH
RS 10 LAKHS
RS 1 CRORE
RS 10 CRORES
RS 100 CRORE
RS 1,000 CRORES
RS 10,000 CRORES
LIVE ELECTION ANALYSIS
Our CNN-IBN
Microsoft Election
Analytics Canter, which
you can see at
www.bing.com/electio
ns or http://ibn.grame
ner.com/live, served
over 10 million
requests on 16th May
2014 — the day of India
election results.
This is one of the
largest real-time
visualisations that we
(and perhaps many
others) have attempted
LIVE ELECTION ANALYSIS
How did the regional
parties fair??
Was it only the BJP
who managed clean
sweeps?
ADMK in TN
BJD in OD
TRS in TG
TMC in WB
Managed historic wins.
Video recreating
how the Election results unfolded
We handle terabyte-size data via non-traditional analytics and visualise it in real-time.
Gramener visualises
your data
Gramener transforms your data into concise dashboards
that make your business problem & solution visually obvious.
We help you find insights quickly, based on cognitive research,
and our visualisations guide you towards actionable decisions.
A data analytics and visualisation company
Thank You! ganes.Kesari@gramener.com
shravan.kumar@gramener.com

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Visualizing the Indian Elections

Editor's Notes

  1. Gramener is a data analytics 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.