# Re: University of Oxford/ usecase

From: Gannon Dick <gannon_dick@yahoo.com>
Date: Tue, 21 Aug 2012 14:53:05 -0700 (PDT)
Message-ID: <1345585985.6289.YahooMailNeo@web112604.mail.gq1.yahoo.com>

Cc: "eGov IG \(Public\)" <public-egov-ig@w3.org>
Seems to me the most pressing problem for Policy Maker Worker Bees is translating anecdotal information (mash-ups) into "Normal Distributions" to present to their Boss.  The model I've come up with is a blending of Mash-ups, Statistics and RDF (Collections).  I hope to make it as easy as possible to switch between the vocabularies.  I think that the most pressing problem for Policy Makers themselves is the measurement of inequality, and for that, you count rooftops because Normal Distributions mix a lot better than they un-mix.  To measure inequality, one need only be able to separate an "Above Average" Class from a "Below Average" Class, and have the Normal Distribution for those Classes.  Mash-ups do this effortlessly, Probability Statistics is incapable, and breaks down with uncertainty in the data source.

About the Model ... the geo namespace http://www.w3.org/2003/01/geo/wgs84_pos# defines
altitude    [alt]    The WGS84 altitude of a SpatialThing (decimal meters above the local reference ellipsoid).

All the Organizations, like the University of Oxford in my Model have circular view ports (I translate the "Place" to the center of a circle).  A Circle is a degenerate Ellipse.  The difference, for counting rooftops, is that the use of a circle instead of an ellipse means that you cannot abstractly land and look in the side window.  I consider that degenerate :-P

--Gannon

________________________________
From: Paola Di Maio <paola.dimaio@gmail.com>
To: Gannon Dick <gannon_dick@yahoo.com>
Cc: eGov IG (Public) <public-egov-ig@w3.org>
Sent: Sunday, August 19, 2012 7:50 AM
Subject: University of Oxford/ usecase

Hello Gannon

Thanks a lot for cooking up a recipe to count rooftps, brings a nice
socio technical view -  and thanks for sharing the method

from where I come from (the pragmatic world), looks still a bit of a
theoretical exercise in the sense that I  find it hard to think how to
use this neat info you pull up

I mean, what can i learn from this view of this data?

exercise:

what can I do with the knowledge I gather

assume
a)
b) resident
c)  public administrator planning for urbanpolicy
d) any other

:-)

P

> The University of Oxford makes a dandy test bed for Socio-Technical Systems.
> These are systems which count rooftops and households rather than
> individuals in a population.
>
> There are three types of institutions at Oxford: Colleges, Graduate Only
> Colleges, and Halls.  Each, 44 in all, have been founded over the last
> centuries, and the founding dates are available.  No simple RDF List can
> capture this, however 4 Lists can - three Types + ANY.  The problem, for RDF
> and SKOS, is the processing of the "first" and "rest".  The model used is
> the "Standard Model" of Particle Physics - unit sized hard boxes. Much
> better is a unit sized circle (diameter=2u). This truncates much of the
> unexplainable combinatorial "fine structure" in the model  which you
> visualize as gaps in the data.  You see fine structure shadows everywhere.
> The situation can be compared to a Physician looking at a catalog of
> thousands of MRI's to find a broken leg when he/she simply needs to look at
> an MRI of your leg.
>
> Anyway, a listing of the data is here[1].   The data base and many
> supporting files are here[2]. If you load your own, you'll need root
> privileges to load the Stored Procedures (Space and Time).
>
> And last but not least, not to be outdone by the BBC's Olympic Medals and
> Dr. Brand Niemann
> ... A mashup of Oxford University Colleges & Halls in time: