Data Journalism 2: Interrogating, Visualising and Mashing
Upcoming SlideShare
Loading in...5
×
 

Data Journalism 2: Interrogating, Visualising and Mashing

on

  • 2,276 views

Session for MA students at City University's Journalism School

Session for MA students at City University's Journalism School

Statistics

Views

Total Views
2,276
Slideshare-icon Views on SlideShare
2,234
Embed Views
42

Actions

Likes
1
Downloads
34
Comments
0

3 Embeds 42

http://www.scoop.it 36
http://paper.li 5
http://twitter.com 1

Accessibility

Upload Details

Uploaded via as Adobe PDF

Usage Rights

CC Attribution-NonCommercial LicenseCC Attribution-NonCommercial License

Report content

Flagged as inappropriate Flag as inappropriate
Flag as inappropriate

Select your reason for flagging this presentation as inappropriate.

Cancel
  • Full Name Full Name Comment goes here.
    Are you sure you want to
    Your message goes here
    Processing…
Post Comment
Edit your comment

    Data Journalism 2: Interrogating, Visualising and Mashing Data Journalism 2: Interrogating, Visualising and Mashing Presentation Transcript

    • Data 2: Interrogating, visualising, mashing Online Journalism City University Paul BradshawMonday, 7 March 2011
    • Themes 5 things you need to know about each Data journalism in action WalkthroughMonday, 7 March 2011
    • Interrogating data .Monday, 7 March 2011
    • Monday, 7 March 2011
    • 5 things you need to know about interrogating data 1. Data always needs cleaning up 2. Treat the ‘source’ like a source 3. Use the right ‘average’ and percentage 4. Variation over time & space: context 5. Spreadsheet tools are your friend - but always backup copiesMonday, 7 March 2011
    • Monday, 7 March 2011
    • “What the Independent have done is confuse the UK’s deficit with our debt [making] the debt problem look around eight times worse than it is. And it used the whole of its front page to do so.” - James BallMonday, 7 March 2011
    • Monday, 7 March 2011
    • What is the data worth? Measurement doesnt answer anything if theres only one variable Statistical significance Sample size and selection Controls and the placebo effect Read up.Monday, 7 March 2011
    • 1. Variance is interesting. 2. Variance is different for different variables and in different populations. 3. The amount of variance is easily quantified. - Philip Meyer, Precision JournalismMonday, 7 March 2011
    • Getting data in the right form Data > Text to columns Find & replace Conditional formulas: =IF(condition, if met, if not) =COUNTIF(range, test)Monday, 7 March 2011
    • Walkthrough: cleaning data in Google Refine Edit cells > common transforms Edit cells > split multi-valued cells Facet > text facet Export...Monday, 7 March 2011
    • Visualising data .Monday, 7 March 2011
    • 5 things you need to know about visualising data 1. Choose the chart for the purpose 2. It can be used to spot a lead 3. Good design is when there’s nothing more to take away 4. It should be self-contained & have refs 5. Be careful with scales and classesMonday, 7 March 2011
    • or http://chartchooser.juiceanalytics.com/Monday, 7 March 2011
    • Monday, 7 March 2011
    • Monday, 7 March 2011
    • What is wrong with this picture?Monday, 7 March 2011
    • Monday, 7 March 2011
    • http://simplecomplexity.net/statistics-without-context/Monday, 7 March 2011
    • http://junkcharts.typepad.com/junk_charts/trifecta-checkup/Monday, 7 March 2011
    • Visualisation tools ManyEyes Tableau Wordle, Tagxedo BatchGeo Gephi Delicious.com/paulb/visualisation+toolsMonday, 7 March 2011
    • Walkthrough: visualising data with Google Gadgets .Monday, 7 March 2011
    • Walkthrough: visualising data in ManyEyes .Monday, 7 March 2011
    • Mashing data .Monday, 7 March 2011
    • 5 things you need to know about mashing data 1. It is what a journalist does best 2. Look for a point of connection: place? Person? Company? Date? 3. What an API can do 4. What APIs there are 5. Mashups can be live, updated or staticMonday, 7 March 2011
    • Monday, 7 March 2011
    • Monday, 7 March 2011
    • Mashup tools Yahoo! Pipes OpenHeatMap Mapalist xFruits Scraperwiki MaptubeMonday, 7 March 2011
    • Walkthrough: making mashups with Yahoo! Pipes Inputs - Fetch Feed, CSV, Data, Page, YQL, Flickr, Form Operators - Filter, Sort, Unique, Union, Count, Split, Rename, Regex, Unique, Location extractor, URL Builder Outputs - Map, Gallery, List, XML, KMLMonday, 7 March 2011
    • Walkthrough: making mashups with OpenHeatMap Format the spreadsheet Publish it as CSV Copy link Paste it at OpenHeatMap Fix any problemsMonday, 7 March 2011
    • Walkthrough: grabbing geo data with Google Refine Edit column > Add column by fetching URLs Use GREL (Google Refine Expression Language) Search web for help & examplesMonday, 7 March 2011
    • Questions? .Monday, 7 March 2011
    • Links OnlineJournalismClasses.tumblr.com Delicious.com/paulb/cityoj09 Delicious.com/paulb/datajournalism Delicious.com/paulb/visualisation Delicious.com/paulb/statistics Delicious.com/paulb/mashupsMonday, 7 March 2011
    • Lab Before the lab: play with these techniques yourself, have problems, find solutions, raise questions. Install Google Refine and Tableau on your laptop to use. - Visualise, interrogate or mash dataMonday, 7 March 2011
    • Books Kaiser Fung - Numbers Rule Your World Ben Goldacre - Bad Science Donna Wong - The WSJ Guide to Information Graphics Brian Suda - A Practical Guide to Designing with DataMonday, 7 March 2011