Hi sumandro, I've worked with this data generated by ERosen looking for
ptwiki stats and I think I can help you.
Given a period of time you can get the total edits of a country and the
count for edits in all countries in that period. With this data you can
generate the "country fraction" and then if you multiply the city fraction
by its country fraction, you get the city "global fraction".
On Tue, May 14, 2013 at 5:43 PM, sumandro <mail(a)ajantriks.net> wrote:
Thanks a lot for the appreciation.
As Sajjad mentioned, we have already obtained a edit-per-location dataset
from Evan (Rosen) that has the following column structure:
*start* and *end* denote the beginning and ending date for counting the
number of edits, and *ts* is time stamp.
The *fraction*, however, gives a national ratio of edit activity, that is
it gives the ratio of 'total edits from that city for that language
Wikipedia project' divided 'total edits from that country for that language
Wikipedia project'. Hence, it cannot be used to understand global edit
contributions to a Wikipedia project (for a time period).
It seems that the original data (from where this dataset is extracted)
should also have the global fractions -- total edit from a city divided by
total edit from the whole world, for a project, for a time period.
Would you know if the global fractions can also be derived from the XML
dumps? Or, even better, is the relevant raw data available in CSV form
On Wednesday 15 May 2013 12:32 AM,
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Date: Tue, 14 May 2013 19:40:00 +0200
From: "Erik Zachte" <ezachte(a)wikimedia.org>
To: "'A mailing list for the Analytics Team at WMF and everybody who
has an interest in Wikipedia and analytics.'"
Subject: Re: [Analytics] Visualizing Indic Wikipedia projects.
Content-Type: text/plain; charset="iso-8859-1"
Awesome work! I like the flexibility of the charts, easy to switch metrics
and presentation mode.
1. WMF has never captured ip->geo data on city level, but afaik this is
going to change with Kraken.
2. Total edits per article per year can be derived from the xml dumps. I
have some csv data that come in handy.
For edit wars you need track reverts on an per article basis, right? That
can also be derived from dumps.
For long history you need full archive dumps and need to calc checksum per
revision text. (stub dumps have checksum but only for last year or two)
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