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Idescat on the Google Public Data Explorer
 

Idescat on the Google Public Data Explorer

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Idescat on the Google Public Data Explorer: The Why, the What and the near Future.

Idescat on the Google Public Data Explorer: The Why, the What and the near Future.

Google Public Data Explorer Day. Eurostat. Luxembourg, 30 June 2011.

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15 of 5 previous next Post a comment

  • Full Name Full Name Comment goes here.
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  • Actually I thought you knew all people who find the GPDE directory at all ;-)

    But otherwise, I agree something could be done in terms of better discovery. I was surprised when I checked google search for a few of the indicators from your data bundle (in English), but no graph showed up in the search results. Well, 'slideware' doesn't work in all cases, it's not perfect (yet) and there is still enough room for developments and our contributions :-)
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  • Katja, you are absolutely right when you say that ’reuse, searchability, malleability, mobility outweight the directory list in terms of visibility’. That said, a unique list still does not seem the right model for a dataset catalog (unless that catalog has less than 10 items!).

    And it’s not only a matter of the dataset’s visibility as a whole: more important than that is the dataset’s *contents* visibility (metrics, dimensions, locations, time...): that’s why this issue is referred to as ’discovery’ in the summary (slide 110). For example, as it is now, a user has a hard time to discover all the information available at GPDE for a certain country like Italy.

    ' I could speculate that you personally know all people who find your dataset through the directory ;-)'

    If that was the case that would prove my point :-): only those who already knew our dataset was there did actually find it. Probably, many of those interested in our information that didn’t expect it to be there and weren’t looking for it didn’t find out about it after visiting the directory list.
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  • Great presentation! I'd just like to comment slides 37 + 39. I believe it is not very relevant how your dataset scale in the Google Public Data directory. Reuse, searchability, malleability, mobility outweight the directory list in terms of visibility. I could speculate that you personally know all people who find your dataset through the directory ;-)
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  • GPDE has a good data model. But, IMHO, the user interface maps too closely that data model, which I think can lead to confusion sometimes. For instance, a filter (for example for labor market statistics’ purposes: population > 15 years old) can be treated, in the data model, as a dimension with only 1 category (and this is perfectly OK) but should be shown in the user interface as something different from a regular dimension.

    Besides, considering topics as groups of metrics doesn’t seem right. For some topics (’society’, ’labor market’, ’education’...) [see slide 71], it is not just about a metric (’population’) but about a metric * dimensions. Now, if you have many metrics, the only tool at hand to help your users is grouping them into topics. So you are forced to choose between the data model or the user interface.

    We have so many metrics that not grouping them wasn’t an option, so we had to ’cheat’ in the data model front: we made up some metrics like ’Economic activity of the population’ (or ’Knowledge of Catalan’): of course, this is not a real metric, the metric is ’population’, filtered by age and classified by employment status.

    It is very wrong to mess up the data model for user interface reasons, but we couldn’t find a better solution for this trade-off. My proposal for Google is on slide 74: forget about topics for grouping metrics; introduce the idea of ’related’ or ’derived’ metrics as a way of narrowing the metrics’ list.
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  • Dear Xavier,
    I have a friend at Facebook asking for an explanation to page 72. Can you help?
    Best regards
    Alf
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    Idescat on the Google Public Data Explorer Idescat on the Google Public Data Explorer Presentation Transcript

    • Idescat on the Google Public Data Explorer:
      The Why, the What and the near Future
      Xavier Badosa (@badosa)
      StatisticalInstitute of Catalonia (Idescat)
      Google Public Data Explorer Day
      Eurostat
      Luxembourg, 30 June 2011
    • Idescat on the Google Public Data Explorer:
      The Why, the What and the near Future
      Xavier Badosa (@badosa)
      Statistical Institute of Catalonia (Idescat)
      Google Public Data Explorer Day
      Eurostat
      Luxembourg, 30 June 2011
    • 7.5 M
      Barcelona
    • idescat
    • Dissemination
      products
      idescat
    • Dissemination
      products
      Statistics as platform
    • “Apps”
      Statistics as platform
      “O.S.”
    • General-purpose
      “Apps”
      Statistics as platform
      “O.S.”
    • General-purpose
      “Apps”
      Third-party
      “Apps”
      that solve specific needs
      Statistics as platform
    • General-purpose
      “Apps”
      Third-party
      “Apps”
      that solve specific needs
      REUSE
      Statistics as platform
    • CC BY
      REUSE
      Statistics as platform
    • CC BY
      APIs
      REUSE
      Statistics as platform
    • CC BY
      APIs
      Widgets
      ...
      REUSE
      Statistics as platform
    • CC BY
      APIs
      Widgets
      ...
      GPDE
      REUSE
    • CC BY
      APIs
      Widgets
      ...
      GPDE
      REUSE
      Very powerful tool
    • “To use again”
      REUSE
    • “To use again”
      elsewhere
      REUSE
    • in a new way
      “To use again”
      elsewhere
      REUSE
    • Malleability
      elsewhere
      REUSE
    • Malleability
      Ease of
      transformation
      elsewhere
      REUSE
    • Malleability
      Mobility
      REUSE
    • Malleability
      Ease of
      transportation
      Mobility
      REUSE
    • Malleability
      Mobility
    • Malleability
      Mobility
    • Malleability
      Mobility
    • Malleability
      Mobility
    • Malleability
      highly customizable
      Mobility
    • highly customizable
    • A single big dataset (vs. many small datasets)
    • are
      unconnected
      datasets
      worlds
      I feel so lonely!
      A single big dataset (vs. many small datasets)
    • 1
      dataset
      manysources
      A single big dataset (vs. many small datasets)
    • 1
      dataset
      many sources
    • 1
      dataset
      many sources
    • Feb. 2011
      1
      dataset
      many sources
      28
      31 DS
    • Feb. 2011
      May 2011
      1
      dataset
      many sources
      28
      31 DS
      40 DS
    • Feb. 2011
      May 2011
      1
      dataset
      many sources
      35
      28
      31 DS
      40 DS
    • Feb. 2011
      May 2011
      DOES
      NOT
      SCALE
      35
      28
      31 DS
      40 DS
    • Employment Barcelona
      Hierarchical list of places
      List of metrics
      Commonvocabularies
      List of dimensions
      Available years/months
      List of sources
      Users don’t care about datasets
    • 1
      dataset
      many sources
      data dissemination
      data visualization
    • 1
      existing
      dataset
      many sources
      data dissemination
      data visualization
    • 1
      open
      existing
      dataset
      many sources
      Machine
      processable
    • 1
      local
      open
      dataset
      many sources
    • 1
      local
      open
      dataset
      many sources
    • 988
      1
      local
      open
      dataset
      many sources
      Catalonia 1
      Counties 41
      Municipalities 946
    • 1
      local
      open
      dataset
      many sources
      annual
    • DSPL
      annual
    • Separation of
      data & metadata
      Commonsensical
      use of XML+CSV
      DSPL
      annual
    • Separation of
      data & metadata
      Commonsensical
      use of XML+CSV
      DSPL
      easy to automate
      annual
    • easy to automate
      annual
    • Full
      bundle
      easy to automate
      annual
    • Catalan municipalities indicators
      Full
      bundle
      128 files!
      <10 updated
      easy to automate
      annual
    • Full
      bundle
      Designed
      for humans
      easy to automate
      annual
    • Single
      files
      Designed
      for machines
      Write API
      easy to automate
      annual
    • The King
      of
      API
      s
    • Single
      files
      PUSH
      Designed
      for machines
      Write API
      easy to automate
      annual
    • Single
      files
      PULL
      Designed
      for machines
      Read
      easy to automate
      annual
    • DSPL
      PULL
    • DSPL
      PULL
    • DSPL
      PULL
    • local
      open
      many sources
      annual
    • 53 metrics
    • 4 topics
      53 metrics

    • 4 topics
      53 metrics
    • metrics
      x
      dimensions
      4 topics
      53 metrics
    • metrics
      x
      dimensions
      4 topics
      53 metrics
      population
      x
      employment status
    • metrics
      x
      dimensions
      4 topics
      53 metrics
      population
      x
      employment status
    • metrics
      x
      dimensions
      population
      x
      employment status
      !
      These aren’t metrics
    • Tooclose
      data model
      user interface
      !
      These aren’t metrics
    • topics
    • topics
      related metrics
      Better
      derived metrics
    • 30 dimensions
      946 mun.
      53 metrics
      41 counties
      4 topics
    • 30 dimensions
      946 mun.
      53 metrics
      41 counties
      4 topics
      3 languages
    • highly customizable
    • Malleability
      highly customizable
    • Malleability
      highly customizable
      Mobility
    • embeddable
      Mobility
    • embeddable
      Mobility
    • embeddable
      Mobility
    • embeddable
      Mobility
    • embeddable
      Mobility
    • embeddable
      Mobility
    • embeddable
      Mobility
      Reversing
      thecommunication
      initiative
    • Idescat
      Users
      Mobility
      Reversing
      thecommunication
      initiative
    • Analytics Dashboard
      embeddable
      Mobility
      Reversing
      thecommunication
      initiative
    • Analytics Dashboard
      embeddable
      Mobility
      There’s no GPDE analyticsdashboard!
    • Analytics Dashboard
      # installs, # visits/visitors
      installs with + visits/visitors
      info with + visits/visitors
      chart with + visits/visitors
      ...
    • embeddable
      Mobility
    • embeddable
      Mobility
      3S
    • 3S
      Youtubify yourself
    • 3S
    • http://www.google.com/publicdata/explore?ds=z1foifl1a0gsn2_&ctype=l
      &strail=false&nselm=h&met_y=f_pop&hl=en&dl=en#ctype=c&strail=false
      &nselm=s&met_y=f_pop_percent&fdim_y=birth_place:Abroad&scale_y=lin
      &ind_y=false&ifdim=mun&hl=en&dl=en
      http://goo.gl/XtpLa
      http://goo.gl/pd/XtpLa
      http://gp.de/z1foifl1a0gsn2_?8vH
      Shorten
      3S
    • Shorten
      3S
      Share
    • Shorten
      3S
      Share
    • Shorten
      Share
    • Support
      oEmbed
      Shorten
      Share
    • Support
      oEmbed
      via
      Shorten
      Share
    • Support
      oEmbed
      Shorten
      Share
    • Malleability
      idescat
      Mobility
      REUSE
    • Malleability
      idescat
      Mobility
      REUSE
      Google
      APIs
    • Better
      discovery
      Automatic
      updates
      Easierembedding
      A N A L Y T I C S
    • pageviews?
      visits?
      unique visitors?
      Whatabout
      ourwebsite’s
      success?
    • pageviews?
      visits?
      unique visitors?
      Success metrics?
    • pageviews?
      visits?
      unique visitors?
      Business model?
      Success metrics?
    • pageviews?
      visits?
      unique visitors?
      Business model?
      Success metrics?
    • pageviews?
      Wedon’toperate in the
      eyeballmarket
      visits?
      uniquevisitors?
      Business model?
      Success metrics?
    • pageviews?
      Wedon’toperate in the
      eyeballmarket
      visits?
      uniquevisitors?
      Weoperate in the
      reference
      market
      Business model?
      Success metrics?
    • maximum data exposure & reach
      reference
      market
    • maximum data exposure & reach
      reference
      market
      accuracypreservation
    • maximum data exposure & reach
      reference
      market
      accuracypreservation
      brandrecognition
    • ThankYou !
      Seealso:
      Statisticaldissemination 2.0
    • borman818 / Daniel Borman
      JoshBancroft
      jakevance / Jacob Vance
      Prizmatic
      Cristian Torras
      Mick ㋡rlosky
      Michelle Kinsey Bruns
      Niamor83
      Clarissa Rossarola
    • WikimediaCommons
      NASA
      http://en.wikipedia.org/wiki/File:The_Earth_seen_from_Apollo_17.jpg
      NuclearVacuum
      http://en.wikipedia.org/wiki/File:The_Earth_seen_from_Apollo_17.jpg
      Mutxamel / HansenBCN
      http://en.wikipedia.org/wiki/File:Localizaci%C3%B3n_de_Catalu%C3%B1a.svg
      Authorunknown
      http://www.taltopia.com/media/6/6374/SPERM-ART.jpg
      Maps © by Google and TeleAtlas
      PD