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Data Journalism - Storytelling with Data

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Data Journalism lecture - Week 5: Storytelling with Data
Lecture date: 7 Oct 2015
MA in Journalism
National University of Ireland, Galway

Title slide image from The Data Journalism Handbook

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Data Journalism - Storytelling with Data

  1. 1. DATA JOURNALISM Dr. Bahareh Heravi @Bahareh360 Week 5
 Storytelling with Data
  2. 2.   Finding  the  data   Cleaning/fixing  the  data   Analysing  the  data   Visualising  the  data   +  Wri6ng  the  accompanying  story  
  3. 3.       DATA  VISUALISATION  
  4. 4. The New York City metropolitan area is home to the largest Jewish community outside Israel. It is also home to nearly a quarter of the nation's Indian Americans and 15% of all Korean Americans and the largest Asian Indian population in the Western Hemisphere; the largest African American community of any city in the country; and including 6 Chinatowns in the city proper, comprised as of 2008 a population of 659,596 overseas Chinese, the largest outside of Asia. New York City alone, according to the 2010 Census, has now become home to more than one million Asian Americans, greater than the combined totals of San Francisco and Los Angeles. New York contains the highest total Asian population of any U.S. city proper. 6.0% of New York City is of Chinese ethnicity, with about forty percent of them living in the borough of Queens alone. Koreans make up 1.2% of the city's population, and Japanese at 0.3%. Filipinos are the largest southeast Asian ethnic group at 0.8%, followed by Vietnamese who make up only 0.2% of New York City's population. Indians are the largest South Asian group, comprising 2.4% of the city's population, and Bangladeshis and Pakistanis at 0.7% and 0.5%, respectively. / Demographics of New York, Wikipedia
  5. 5. 700 000 Source:  infogram  training  
  6. 6. John  Snow,  1854   John  Snow  Cholera  Map  
  7. 7. Florence  Nigh@ngale  Coxcomb          
  8. 8. Charles  Minard,  1812   Napoleaon’s  March  on  Moscow   Six  types  of  data:  (1)  the  number  of  Napoleon's  troops;  (2)  distance;  (3)  temperature;  (4)   the  la6tude  and  longitude;  (5)  direc6on  of  travel;  (6)  loca6on  rela6ve  to  specific  dates.  
  9. 9.   TYPE  OF  DATA  ANALYSIS   TEMPORAL   GEOSPATIAL   TOPICAL   NETWORK  
  10. 10.       TEMPORAL                                                                          When?  
  11. 11.   To  understand  temporal  distribu6on   of  datasets;     To  iden6fy  growth  rate,  latency  to   peak  6mes,  or  decay  rates;     To  see  paTerns  in  6me-­‐series  data,   such  as  seasonality  or  bursts.       Visual  Insights,  by  Katy  Borner  and  David  E.  Polley,  2014  
  12. 12. Florence  Nigh@ngale  Coxcomb          
  13. 13. Napoleaon’s  March  on  Moscow   Six  types  of  data:  (1)  the  number  of  Napoleon's  troops;  (2)  distance;  (3)  temperature;  (4)   the  la6tude  and  longitude;  (5)  direc6on  of  travel;  (6)  loca6on  rela6ve  to  specific  dates.   Charles  Minard,  1812  
  14. 14. Visual  Insights,  by  Katy  Borner  and  David  E.  Polley,  2014   hTp://scimaps.org/maps/map/history_flow_visuali_56/detail  
  15. 15. The  Guardian  
  16. 16. London:  The  Informa6on  Capital   By  James  Cheshire  and  Oliver   Uber6   2014  
  17. 17. Marriage  referendum  in  Ireland   Bahareh  Heravi,  Insight  News  Lab,  2015  
  18. 18.       GEOSPATIAL                                                                  Where?  
  19. 19.     Uses  loca6on  informa6on  to  iden6fy   posi6ons,  movements,  [trends  or   paTerns]  over  geographical  space.     Visual  Insights,  by  Katy  Borner  and  David  E.  Polley,  2014  
  20. 20. hTp://www.theguardian.com/news/datablog/interac6ve/2011/aug/10/poverty-­‐riots-­‐mapped   Mapping  London  Riots  with  poverty  
  21. 21. Language  of  Communi6es  on  TwiTer  (Europe),  David  Fischer  (2012)  
  22. 22. Map  of  science  collabora6ons  2005  -­‐  2009     Olivier  H.  Beauchesne  (2012)  
  23. 23. London:  The  Informa6on   Capital   By  James  Cheshire  and  Oliver   Uber6   2014  
  24. 24. By  Bahareh  Heravi   Irish  Times  Data  
  25. 25. hTp://www.carbonmap.org/  
  26. 26.       TOPICAL                                                      What?  
  27. 27.     Uses  text  to  iden6fy  major  topics,  their   interrela6ons,  and  their  evolu6on  over   6me,  [and  space].     Visual  Insights,  by  Katy  Borner  and  David  E.  Polley,  2014  
  28. 28. Map  of  Science   hTp://cns.iu.edu/images/teaching/ivmoocbook14/4.12.pdf  
  29. 29. London:  The  Informa6on  Capital   By  James  Cheshire  and  Oliver   Uber6   2014  
  30. 30. London:  The  Informa6on  Capital   By  James  Cheshire  and  Oliver   Uber6   2014  
  31. 31. Facts  are  Sacred   Simon  Rogers   2013  
  32. 32. London:  The  Informa6on  Capital   By  James  Cheshire  and  Oliver   Uber6   2014  
  33. 33.       NETWORK                                                      With  whom?  
  34. 34.     To  iden6fy  (highly)  connected  en66es   and  the  rela6onship  between  them;     Network  proper6es,  such  as  size  and   density;   Structure  such  as  clusters  and   backbones.   Visual  Insights,  by  Katy  Borner  and  David  E.  Polley,  2014  
  35. 35. Map  of  science  collabora6ons  2008  -­‐  2012     Olivier  H.  Beauchesne  (2014)  
  36. 36. Bahareh  Heravi,  Insight  News  Lab,  2015  
  37. 37. The  Guardian  
  38. 38. Source:  Guardian  Data  
  39. 39.       VISUALISING  THE  DATA  
  40. 40. Why  do  we  visualise?     To  tell  a  story  and  communicate     Visualise  to  analyse        
  41. 41. Bar Line Area Map More Some chart types Pie Scatter
 Plot Bubble Heat 
 map Box
 Plot Source:  infogram  training  and  Tableau  
  42. 42. Most common way to visualise data. Good to show differences in values & categories that don’t add up to 100%. Percent of spending by department, website traffic by origination site. Poor choice for showing time- series data, as the line charts have a smoother representation. Bar Comparing data 
 across categories Source:  infogram  training  and  Tableau  
  43. 43. Good for showing contrast when two or three components of something differ greatly in size. Percentage of budget spent on different departments, response categories from a survey. Poor choice if you have too many variables or if their values are similar in size. Pie Compare proportions 
 out of 100% Source:  infogram  training  and  Tableau  
  44. 44. Line Get some lengthy ! data like oil prices? Best choice for time-series data and highlighting trends, with not more than three sets per chart. Stock price change over a five- year period, website page views during a month, revenue growth by quarter. May be visually misleading when attempting to show data that is not based on time-series. Line View trends in
 Data over time Source:  infogram  training  and  Tableau  
  45. 45. A great choice to show regional differences in certain variables, when there is a clear correlation. Driving penalties by county, product export destinations by country, car accidents by postcode. Not optimal when the differences are small in size or when time- series data has to be displayed. Map To show a Geographical comparison Source:  infogram  training  
  46. 46. An effective way to get a sense of trends, concentrations, correlations and outliers. Relationship between weight of a vehicle and its max speed, speeding ticket and death rate. Not so easy to read by every day users. Scatter 
 Plot Investigate relationship
 vetween two variables Source:    Tableau  
  47. 47. Suitable for understanding your data at a glance, seeing how data is skewed towards one end, identifying outliers in your data. Not so easy to read by every day users. Box Plot To show distribution 
 of a set of data Source:    Tableau  
  48. 48. To give weight to cencentration of data on scatter plots or maps. Not so easy to understand by every day users, particularly when comparing data on two axis. Bubble To show cencentration
 of data Source:    Tableau  
  49. 49. Works well with 2-3 groups of people, objects or categories are compared, and when differences are significant. A line chart is a better option with more than three groups and when differences are small. Picto Another way of comparing 
 categories Source:  infogram  training  
  50. 50. Check  out  datavizcatalogue.com/  
  51. 51.     TOOLS  
  52. 52. Fusion     Tables  
  53. 53. Hands-on Visualise number of death per county and rate of death per county in Ireland. Start with Excel

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