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Computer Assisted Journalism

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Getting story ideas from news releases. Tools - Tasks. Newsgroups / Mailing Lists ... for Dutch Media over the world. Violence per 1000 for Dutch police ... – PowerPoint PPT presentation

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Title: Computer Assisted Journalism


1
Computer Assisted Journalism
  • Ideas, Innovations, Intentions
  • BBC 4 July 2001

2
Tools - Tasks
  • Internet Websites
  • Fact checking
  • Locating background material
  • Finding official sources to contact
  • Getting datasets to analyze
  • Second-sourcing information
  • Getting story ideas from news releases

3
Tools - Tasks
  • Newsgroups / Mailing Lists
  • Finding people with experience
  • Getting ideas about angles on a story
  • Hearing what people are talking about the
    buzz
  • Getting relevant articles on a topic

4
Tools - Tasks
  • Analytical Tools Spreadsheets, Databases,
    Mapping
  • Putting isolated events into context
  • Doing proactive reporting not just reactive
  • Ensuring accountability of government data
  • Finding aberrations in the data / clusters that
    need to be reported getting tips from the data

    story

5
Train Wrecks
  • Check the background / get facts, details
    Connex / British Rail sites
  • Get articles on other train wrecks archive,
    online files from other newspapers
  • Get reactions from train passengers newsgroup

6
Train Wrecks
  • Put event into context 56 such incidents last
    month an increase of 21 on the previous May
    spreadsheet or database
  • Put event into context Part II Which routes
    are they happening on? Where are the most
    dangerous areas? - mapping

7
Where youd get the data
  • Keep your own recording
  • Examples of datasets built by newsroom /
    researchers
  • Get the data
  • Independent study by WS Atkins
  • Health and Safety Executive list
  • Signals Passed at Danger list

8
Spreadsheets
  • For calculating change over time, comparisons
    between units
  • For recalculating budget changes on the fly
  • For doing your own analysis
  • For organizing information

9
Asylum requests in the Netherlands
10
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11
Spreadsheet examples
  • Election night voter turnout
  • Census data
  • Drug raid reports
  • Crime reports
  • Zoning votes

12
Mapping Spot Trends
  • Seeing Data Geographically
  • Data that contains location
  • Directly, such as address, lat/long, etc.
  • Indirectly, such as postal code or name of
    district
  • The kinds of stories to be told
  • Census / demographics
  • Crime
  • Environmental

13
Mapping Story Ideas
  • Where are the arson fires in your city?
  • How does one county compare with another (welfare
    benefits, cancer rates, minority residents)?
  • Which schools have the highest test scores or
    highest teacher absences?
  • Columbus Found that the least-experienced
    teachers in elementary schools were clustered in
    inner-city

14
Where to get data
  • National Digital Archive of Datasets
  • http//ndad.ulcc.ac.uk/
  • London crime statistics http//www.met.police.uk/p
    olice/mps/mps/press/stats.htm
  • Environment Agency
  • http//www.environment-agency.gov.uk

15
Some useful data map displays
  • Choropleth maps
  • Graduated color
  • Unique value
  • Dot map
  • Graduated symbol
  • Unique symbol
  • Dot density
  • Chart
  • Pie
  • Bar

16
Dr. John Snows cholera map
17
Births/Population (ratio)
18
Dot map (single symbol)
19
Dot map (graduated symbol)
20
Unemployment in EU15
21
Biggest Political Parties in Netherlands
22
Traffic Accidents in Utrecht
23
Restaurants in Utrecht
24
Rich and Poor Neighborhoods at Utrecht
25
Reporters for Dutch Media over the world
26
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27
Violence per 1000 for Dutch police districts
28
Maps Online
  • Census maps
  • LA Times
  • Washington Post

29
Goals
  • Find stories before they are stories
  • Bring the story home to the reader, to the
    readers home
  • Get angles no one else is covering
  • Self-reliance not depend on their take
  • Provide interactive content online enhance and
    deepen story package

30
Issues
  • Checking credibility of sources
  • Determining reliability of data
  • Differences between US and European data
    collection policies
  • Verifying currency and completeness of the
    records collected

31
(No Transcript)
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