Data Visualization for Social Problems
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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
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Data Visualization for Social Problems
- 1. DATA VISUALIZATION FOR SOCIAL PROBLEMS S Anand, Chief Data Scientist, Gramener
- 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
- 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?
- 12. 0 5,000 10,000 15,000 20,000 25,000 30,000 35,000 40,000 0 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95 100 TN CLASS X: ENGLISH
- 13. TN CLASS X: SOCIAL SCIENCE 0 5,000 10,000 15,000 20,000 25,000 30,000 35,000 40,000 0 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95 100
- 14. TN CLASS X: MATHEMATICS 0 5,000 10,000 15,000 20,000 25,000 30,000 35,000 40,000 0 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95 100
- 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
- 28. 0% 10% 20% 30% 40% 50% 60% 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
- 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
Editor's Notes
- 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.