[OpenSpending] Fwd: [Scoda-dev] Network analysis of landgrabbing data

Wed Dec 5 19:03:50 UTC 2012

Hi Tom,

There's a very well-structured
book<http://www.amazon.com/Social-Network-Analysis-Applications-Structural/dp/0521387078>on
social network analysis (by Wasserman and Faust) which clarifies all
the
important methods and definitions in SNA. It may seem frightening (some 800
pages) but it's highly recommended for beginners as well. (I'm not a
network expert myself but learned a lot from this book when K-Monitor
started to play with different datasets to map the network of Hungarian
politicians and businesspeople.)

Besides gephi, you may also want to try cytoscape<http://www.cytoscape.org/>or
thejit <http://philogb.github.com/jit/>.

Best,
Julia Keserű

2012/12/5 Tobias Eigen <tobias at saidia.org>

> Hi Tom,
>
> iScale has experience with network mapping - you can contact them.
> http://scalingimpact.net/innovations/strategy-planning-scaled-impact
>
> Cheers,
>
> Tobias
>
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>
> Tobias Eigen
> 9670 Olympus Beach Rd NE
> Bainbridge Island, WA 98110
> landline: +1-206-842-1467
> mobile: +1-206-375-7984
> skype: tobiaseigen
>
> Appointments <http://meetme.so/tobiaseigen>
>
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>
>
> On Wed, Dec 5, 2012 at 7:01 AM, Lucy Chambers <lucy.chambers at okfn.org>wrote:
>
>> Might anyone here be able to help with this? It's about Network
>> Analysis - which I know is used regularly by people tracking
>> companies, media neutrality etc.
>>
>>
>> ---------- Forwarded message ----------
>> From: Tom Longley <tom at tacticaltech.org>
>> Date: Wed, Dec 5, 2012 at 6:48 AM
>> Subject: [Scoda-dev] Network analysis of landgrabbing data
>> To: scoda-dev at lists.okfn.org
>>
>>
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>>
>> Hi there --
>>
>> The plot so far: I've put together two gentle introductory courses as
>> part of Tactical Tech's work with the School of Data. The first, on
>> data cleaning; the second on descriptive analysis. Both use
>> spreadsheets. They're in the final run now. The data used in the
>> course is about large scale land acquisition in the developing world
>> by hedge funds for the biofuel industry
>> (http://datahub.io/dataset/grain-landgrab-data).
>>
>> For the third course, I'm looking at at network analysis using a foss
>> tool called Gephi. With limited tinkering you make make neat things,
>> for example this showing the "land grabbing" transaction network
>>
>>
>> https://cloud.tacticaltech.org/public.php?service=files&file=%2Ftom%2Ffiles%2FSchool%20of%20Data%2Fexchanges.png
>>
>> (the Gephi file for this is here:
>>
>> https://cloud.tacticaltech.org/public.php?service=files&file=%2Ftom%2Ffiles%2FSchool%20of%20Data%2Fgrain_landgrab_20121205.gephi.zip
>> )
>>
>> *just remove the .zip extension - I added to this as our cloud
>> objected to .gephi as an extension*
>>
>> Gephi certainly makes graphing more accessible, and I feel "graph
>> thinking" is such a useful basic skill in examining issues like this
>> one, and others involving companies and finance.
>>
>> However, so far I have found the tool very challenging to use. This is
>> in part because of its byzantine UI, which can be overcome with a bit
>> of patience. However, the big issue is that its functions and the way
>> it behaves are unfathomable: it's hard to know what to expect if for
>> example you apply a Yifan-Hu Proportional algorithm  to a dataset. You
>> could apply it a 100 times and still not be any wiser about what it is
>> doing and why you'd choose it over a Force Atlas.
>>
>> So I'm looking for some help: do you know anyone who can work with me
>> to be a tourist guide about network analysis, explaining the concepts
>> to me in plain language, and helping me de-mystify this for learners
>> at the School. I may be able to cover the cost of a day of someone's
>> time to help me out (confirmation pending on this).
>>
>> Best regards,
>>
>> Tom
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>>
>> --
>> Lucy Chambers
>> Project Coordinator,
>> School of Data & OpenSpending
>> Open Knowledge Foundation
>> Skype: lucyfediachambers
>> Twitter: @lucyfedia
>>
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-- 
Keserű Júlia
K-Monitor
+36-1-789-5005, +36-20-457-2346
keseru.julia at k-monitor.hu
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