Data storytelling - A PORTFOLIO OF TABLEAU VISUALIZATIONS for EFFECTIVE INFORMATION PRESENTATION

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Department of Communication
A PORTFOLIO OF TABLEAU VISUALIZATIONS
for EFFECTIVE INFORMATION PRESENTATION
Candidate: Wen-Tao (Wendy) Lu
Advisor: Dr. Hua (Helen) Wang
DATA STORYTELLING
2020 MA Graduation Project

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1. To learn how to use Tableau tools and features to visualize quantitative data;
2. To apply the Tableau visualization skills using data from an existing project –
“I, A Woman, Can Achieve Anything” in India;
3. To collect, track, and visualize data from the ongoing COVID-19 pandemic;
generate original data storytelling visualizations; and discuss effective ways
to improve information presentation to the public
Project Aims

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Department of CommunicationDepartment of Communication
AUDIENCE ENGAGEMENT
in “I, A Woman, Can Achieve Anything”
via the Interactive Voice Response System
an initiative in India to promote gender equality

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• It is an ongoing entertainment-education initiative launched in
India on the International Women’s Day (March 8) in 2014 by the
Population Foundation of India
• It leverages the power of storytelling across television, radio and
digital platforms to challenge regressive social norms and
promote gender equality, women’s empowerment and related
prosocial behaviors
• Season 1 & 2 attracted over 400 million audience across different
communication platforms: television, radio, digital and social
media, as well as on-the-ground campaign and mobilization
• Season 3 broadcast from January to September, 2020 featuring
issues related to sanitation and family planning
“I, A Woman, Can Achieve Anything”
Wang & Singhal (2018)

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• An innovative technology company, Gram Vaani, pioneered the IVRS model with the Population Foundation of
India to leverage India’s vast mobile telephony infrastructure to reach rural residents, empower local communities,
and enable prosocial actions.
• A toll-free number was advertised during the program broadcast that audience members could give a “missed” call
to receive a free callback and participate in different activities. Callers could participate in surveys and provide
feedback by pressing numeric keys and recording voice messages.
• The data used in this project include the geographic location of the IVRS callers as reported in the surveys and
educational themes they addressed in the voluntary voice recordings in the discussion section, highlighted below.
Audience Engagement via the Interactive Voice Response System (IVRS)
Season 3 telecast
Regional/Local IVRS NumberNational IVRS Number
Call Logs Caller Surveys Audience Quizzes Discussion Forum

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WHICH INDIAN
STATES WERE PEOPLE
CALLING FROM??
Using filled maps in Tableau to visualize
the locations of IVRS callers in Season 3

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Locations of Callers in the Conventional Survey on Sanitation
• Among those who indicated
their location, IVRS survey
participants called from 36
Indian states.
• Top 5 states:
Uttar Pradesh: 387
Rajasthan: 239
Madhya Pradesh: 216
Maharashtra: 204
Bihar: 176

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Locations of Callers in the Galileo Survey on Sanitation
Indian states.
• Top 5 states:
Uttar Pradesh: 137
Bihar: 105
Rajasthan: 90
Madhya Pradesh: 88
Jharkhand: 71

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Locations of Callers in the Conventional Survey on Family Planning
Indian states.
• Top 5 states:
Uttar Pradesh: 65
Rajasthan: 56
Bihar: 55
Maharashtra: 36
Jharkhand: 33

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Locations of Callers in the Galileo Survey on Family Planning
Indian states.
• Top 5 states:
Uttar Pradesh: 222
Rajasthan: 185
Bihar: 150
Madhya Pradesh: 117
Maharashtra: 88

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Locations of Callers in All Surveys
Indian states.
• Top 5 states:
Uttar Pradesh: 811
Rajasthan: 570
Bihar: 486
Madhya Pradesh: 453
Maharashtra: 388

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Locations of Callers in the Discussion Forums
Indian states.
• Top 5 states:
Uttar Pradesh: 1102
Madhya Pradesh: 409
Rajasthan: 375
Maharashtra: 348
Bihar: 333

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Locations of Callers in both Surveys and Discussion Forums
Indian states.
• Top 5 states:
Uttar Pradesh: 1,913
Rajasthan: 945
Madhya Pradesh: 862
Bihar: 819
Maharashtra: 736

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WHICH INDIAN DISTRICTS
WERE PEOPLE CALLING FROM
IN THE TOP 5 STATES?
Using bubble maps in Tableau to visualizing the
geographic hotspots of the IVRS callers in Season 3

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Locations of Callers in the Conventional Survey on Sanitation
• Callers from the top 5 states
were located in 227 districts.
• Top 3 districts:
Nagpur, Maharashtra: 83
Jaipur, Rajasthan: 60
Jabalpur, Madhya Pradesh: 56

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Locations of Callers in the Galileo Survey on Sanitation

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Locations of Callers in the Conventional Survey on Family Planning
Varanasi, Uttar Pradesh: 7
Araria, Bihar: 5

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Locations of Callers in the Galileo Survey on Family Planning

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Locations of Callers in the ALL Surveys

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Locations of Callers in the Discussion Forums
Sitapur, Uttar Pradesh: 198

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Locations of Callers in both Surveys and Discussion Forums

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HOW DID THE AUDIENCE
RESPOND TO DIFFERENT
EDUCATIONAL THEMES?
Using tree maps in Tableau to visualize the IVRS
caller contributed content categories in Season 3

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IVRS Called Contributed Discussion Content Categories (Overall)

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IVRS Called Contributed Discussion Content Categories (Educational Themes)

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IVRS Called Contributed Discussion Content Categories (Sanitation Themes)

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IVRS Called Contributed Discussion Content Categories (Family Planning Themes)

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STATISTICS TRACKING
A deeper understanding of how
the COVID-19 pandemic is unfolding
in different parts of the world

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WHERE WERE THE
MOST AFFECTED
CITIES IN HUBEI??
Visualizing the geographic hotspots
across various cities in the province

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Most Affected Cities in Hubei Province, China by January 14, 2020
Source: Health Commission of Hubei Province

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Most Affected Cities in Hubei Province, China by February 4, 2020

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Most Affected Cities in Hubei Province, China by March 3, 2020

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Animated Daily Progression of the Most Affected Cities in Hubei, China (January 10-March 31, 2020)

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WHERE ARE THE
MOST AFFECTED
DISTRICTS IN BEIJING??
Visualizing the geographic hotspots
across districts in the capital city

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Most Affected Districts in Beijing by January 21, 2020
Source: Beijing Municipal Health Commission

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Most Affected Districts in Beijing by January 28, 2020

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Most Affected Districts in Beijing by February 4, 2020

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Most Affected Districts in Beijing by March 3, 2020

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Animated Daily Progression of the Most Affected Districts in Beijing (January 20-March 31, 2020)

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HOW HAS COVID-19
SPEAD OUTSIDE OF
HUBEI PROVINCE?
Visualizing the geographic spread
across different provinces in China

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COVID-19 Spread Outside of Hubei Province in China by January 18, 2020
Source: National Health Commission of the People’s Republic of China

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COVID-19 Spread Outside of Hubei Province in China by February 4, 2020

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COVID-19 Spread Outside of Hubei Province in China by March 3, 2020

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Animated Daily Progression of COVID-19 Spread from Hubei to Other Provinces in China (January 18-March 31, 2020)

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HOW HAS COVID-19
SPREAD OUTSIDE OF
MAINLAND CHINA??
Visualizing the geographic spread
across different countries worldwide

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COVID-19 Spread Outside of China by January 14, 2020
Source: World Health Organization

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COVID-19 Spread Outside of China by February 4, 2020

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COVID-19 Spread Outside of China by March 3, 2020

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Animated Daily Progression of COVID-19 Spread Mainland China to Other Countries (January 11-March 31, 2020)

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WHAT IS THE SCALE OF
COVID-19 OVER TIME??
Visualizing the cumulative number of
confirmed cases, recovery, and deaths

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Area Map of Cumulative Confirmed Cases, Recovered Cases, and Deaths Worldwide (Feb 1-March 31, 2020)
Selected Critical Events:
A. Feb 2: First death reported outside of China
B. Feb 6: Japan confirmed the infection on Diamond Princess
C. Feb 12: There were 14,840 new confirmed cases in China
due to the government taking clinical diagnosis into
consideration
D. Feb 15: First case in Egypt/Africa; First death in
France/Europe
E. Feb 18: 500 people took off Diamond Princess in Japan
F. Feb 29: First death in the US
G. Mar 4: First death in Spain
H. Mar 6: First death case in UK
I. Mar 7: New York State declared a state of emergency
J. Mar 10: All passengers from Diamond Princess disembarked
K. Mar 11: WHO declared COVID-19 a 'pandemic’; Washington
in the US declared a state of emergency
L. Mar 12: NYC declared state of emergency
M. Mar 13: Spain and US declared state of emergency; the US
began the travel ban
N. Mar 14: Spain declared state of alarm and national lockdown;
France ordered the closure of all non-essential public places;
South Africa declared state of emergency.
O. Mar 15: Kazakhstan declared state of emergency.
P. Mar 17: First case in Montenegro, the pandemic spread
throughout Europe; Belgium declared national lockdown.
Q. Mar 18: California lockdown; Australia began travel ban.
R. Mar 20: New Zealand closed borders; US closed borders to
Mexico and Canada and U.S. closed; Argentina declared
national lockdown
S. Mar 21: Tokyo 2020 Olympics Games postponed to 2021
T. Mar 24: Ukraine declared national state of emergency
U. Mar 25: The construction of the first temporary hospital in
the NYC was completed; China lifted travel restrictions in
Hubei, except Wuhan; China placed a travel ban to all
foreigners.
V. Mar 27: Several shopping malls in Wuhan resumed business.
A B
C D E
F
G H
I
J
K
L
M
N
O
P
Q
R
T
U
V
S
Source: Worldometer

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HOW ARE THE TRENDS
DIFFERENT ACROSS
COUNTRIES WORLDWIDE?
Visualizing the confirmed cases
over time among all countries

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All countries (January 11 – March 31, 2020)

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All countries with at least 100,000 cases (January 11 – March 31, 2020)

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All countries with 10,000-99,999 cases (January 11 – March 31, 2020)

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All countries with 1,000-9,999 cases (January 11 – March 31, 2020)

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All countries with 500-999 cases (January 11 – March 31, 2020)

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All countries with 1-499 cases (January 11 – March 31, 2020)

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HOW MUCH WORSE
CAN IT GET?
Visualizing the difference in the top 10
countries in their respective first 81 days

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Different trajectories in confirmed cases for the top 10 countries (January 11 – March 31, 2020)

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Different trajectories in mortality rates for the top 10 countries (January 11 – March 31, 2020)
Note: On Feb 20, 2020,
there were a total of 2
confirmed cases and 2
deaths in Iran.

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HOW CAN MITIGATION
STRATEGIES MAKE A DIFFERENCE?
Visualizing the difference in terms of age
and gender groups in selected locations

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Confirmed Cases by Age Groups in Italy (April 24, 2020)
Source: Istituto Superiore di Sanità

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Source: Central Disaster Management Headquarters
Confirmed Cases by Age Groups in South Korea (April 24, 2020)

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Source: National Institute of Health Carlos III (ISCIII)
Confirmed Cases by Age Groups in Spain (April 23, 2020)

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Source: Taiwan Centers for Disease Control
Confirmed Cases by Age Groups in Taiwan (April 24, 2020)

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Confirmed Cases by Gender in Italy, South Korea, Spain, and Taiwan
Sources: Istituto Superiore di Sanità, Central Disaster Management Headquarters, National Institute of Health Carlos III (ISCIII), Taiwan Centers for Disease Control

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HOW TO VISUALIZE DATA FOR
STORYTELLING EFFECTIVELY?
An exercise to critique examples of graphs
used in current COVID-19 news reports

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EXAMPLES OF WHAT NOT TO DO
https://www.ecdc.europa.eu/en/cases-2019-ncov-eueea
• Almost 2/3 of the
stacked bars are not
even visible to show
the distribution
• Without any pointed
illustration, it is also
hard to interpret the
stacked bars that are
visible on the chart

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https://www.bbc.com/news/world-asia-china-51453848
• Difficult to read the world
maps and detect all of
the countries with COVID-
19 using the same color
• The list of confirmed
cases on the bottom
could have been
correlated to the shading
of their corresponding
countries in the map

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https://www.theguardian.com/world/2020/mar/13/coronavirus-pandemic-visualising-the-global-crisis?CMP=Share_iOSApp_Other
How does this pandemic compare with others?
• This graph is well
intentioned but not
effective as the key
comparison was
between COVID-19 and
other pandemics but
the visualization didn’t
include COVID-19
• The bubbles are not
proportionate based on
the number of deaths

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EXAMPLES OF MORE INFORMATIVE VISUALIZATIONS
https://www.nytimes.com/interactive/2020/world/asia/china-wuhan-coronavirus-maps.html

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Created and shared by Dominic Meagher https://docs.google.com/spreadsheets/d/1qRmq8LGyf-rel3Pfh9qlvTGDVcv5_cLFWeMEj4IPmso/edit#gid=997342016

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https://ourworldindata.org/grapher/total-confirmed-cases-of-covid-19-per-million-people?year=2020-03-31

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https://www.theguardian.com/world/2020/mar/13/coronavirus-pandemic-visualising-the-global-crisis?CMP=Share_iOSApp_Other

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https://medium.com/@andreasbackhausab/coronavirus-why-its-so-deadly-in-italy-c4200a15a7bf

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https://medium.com/@tomaspueyo/coronavirus-act-today-or-people-will-die-f4d3d9cd99ca

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REFERENCES

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COVID-19 situation reports. (n.d.). Retrieved from
https://www.who.int/emergencies/diseases/novel-coronavirus-2019/situation-
reports
Outbreak notification. Retrieved from
http://www.nhc.gov.cn/xcs/yqtb/list_gzbd.shtml
Outbreak notification. Retrieved from
http://wb.beijing.gov.cn/home/ztzl/kjyq/fk_yqtb/index.html
Information released. Retrieved from
http://wjw.hubei.gov.cn/bmdt/ztzl/fkxxgzbdgrfyyq/xxfb/
https://feiyan.wecity.qq.com/wuhan/dist/index.html#/
Real-time update: the latest development of the new coronary pneumonia.
Retrieved from https://feiyan.wecity.qq.com/wuhan/dist/index.html#/
Taiwan Centers for Disease Control . Retrieved from https://www.cdc.gov.tw/
Informe sobre la situación de COVID-19 en España. Retrieved from
https://www.isciii.es/QueHacemos/Servicios/VigilanciaSaludPublicaRENAVE/Enfer
medadesTransmisibles/Documents/INFORMES/Informes%20COVID-
19/Informe%20n%C2%BA%2025.%20Situaci%C3%B3n%20de%20COVID-
19%20en%20Espa%C3%B1a%20a%2023%20de%20abril%20de%202020.pdf
Integrated surveillance of COVID-19 in Italy. Retrieved from
https://www.epicentro.iss.it/en/coronavirus/bollettino/Infografica_24aprile%20EN
G.pdf
Coronavirus Disease-19, Republic of Korea. Retrieved from
http://ncov.mohw.go.kr/en/bdBoardList.do?brdId=16&brdGubun=161&dataGub
un=&ncvContSeq=&contSeq=&board_id=
Situation update for the EU/EEA and the UK, as of 10 March 2020 08:00.
Retrieved from https://www.ecdc.europa.eu/en/cases-2019-ncov-eueea
Coronavirus: Senior Chinese officials 'removed' as death toll hits 1,000. Retrieved
from https://www.bbc.com/news/world-asia-china-51453848
Situation update for the EU/EEA and the UK, as of 10 March 2020 08:00.
Retrieved from https://www.ecdc.europa.eu/en/cases-2019-ncov-eueea
Coronavirus pandemic: Tracking the global outbreak. Retrieved from
https://www.bbc.com/news/world-51235105
The New York Times - Coronavirus Map: Tracking the Spread of the Outbreak.
Retrieved from https://www.nytimes.com/interactive/2020/world/asia/china-
wuhan-coronavirus-maps.html
The coronavirus pandemic: visualising the global crisis. Retrieved from
https://www.theguardian.com/world/2020/mar/13/coronavirus-pandemic-
visualising-the-global-crisis?CMP=Share_iOSApp_Other
References

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https://research.lib.buffalo.edu/dataviz/home
ADDITIONAL RESOURCES
https://www.edwardtufte.com/tufte/

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ACKNOWLEDGEMENT

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WHO declared COVID-19 a Public Health Emergency of International Concern on January 30,
2020 then a pandemic on March 11, 2020. Effective information sharing with the public is critical
for emergency response and mitigation.
I am grateful to have learned practical data storytelling visualization skills through this graduation
project, especially my advisor, Dr. Wang, who nurtured my curiosity and guided me through this
project with patience.
Also many thanks to my classmates who have expressed their concerns and checked in with me
during this difficult time. Their support helped me to overcome loneliness and anxiety, and
carried me to the finish line.
Acknowledgement

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