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Proposal for representing Aggregate Statistical Data

From: Raphaël Troncy <raphael.troncy@eurecom.fr>
Date: Mon, 24 Jun 2019 22:06:25 +0200
To: public-semstats@w3.org
Cc: guha@google.com, "franck.cotton@insee.fr" <franck.cotton@insee.fr>
Message-ID: <75b18b11-5586-d819-ffcc-5f3c82684b14@eurecom.fr>
Dear SemStats community group,

I'm relaying this message from Guha sent today on the schema.org mailing 

The proposal can also be discussed at 


-------- Message transféré --------
Sujet : 	Proposal for representing Aggregate Statistical Data
Date de renvoi : 	Mon, 24 Jun 2019 19:10:01 +0000
De (renvoi) : 	public-schemaorg@w3.org
Date : 	Mon, 24 Jun 2019 12:09:23 -0700
De : 	Guha <guha@google.com>
Pour : 	schema.org Mailing List <public-schemaorg@w3.org>

This document can be accessed here. 

Look forward to feedback.


Representing aggregate statistics

Examples of aggregate statistical reports include those from Census 
Organizations (e.g., American Community Survey), Health Organizations 
(e.g., CDC Wonder) and many others. This is a schema, currently in use 
on DataCommons.org for representing facts stated in these reports. This 
document describes certain general mechanisms for representing 
statistical populations and associated observations. This document will 
be followed later by a companion proposal suggesting some basic common 
vocabulary useful for representing the kind of data released by the US 
Census, CDC, etc.

Our interest is not in describing a data set or mapping columns in csv 
files, but in representing the actual data itself. Other efforts have 
focused on characterizing data cubes in terms of dimensions, etc. While 
we draw upon their work, our goals are different.

Examples of the kind of statistics we would like to represent include:

1. In 2016, there were 1213 people in East Podunk, California, who were 
male, married, with a median age of 22.
2. In 2017, there were 20 deaths in Falooda County where the cause of 
death was XYZ

We will refer to ‘number of people who are male, hispanic’, ‘number of 
deaths where cause of death was XYZ’, etc. as variables. Since the 
number of possible variables increases combinatorially, clearly, we 
can’t have a properties for each variable (or worse, property for each 
variable x years). We need a way of compositional way of constructing 
variable references. We use the concept of a StatisticalPopulation to do 
this construction.

A StatisticalPopulation is a set of instances of a certain given type 
that satisfy some set of constraints. The property populationType is 
used specify the type. Any property that can be used on instances of 
that type can appear on the statistical population. An instance of 
StatisticalPopulation whose populationType is C1, which has the 
properties p1, p2, … with values v1, v2, … corresponds to the set of 
objects of type C1 what have the property p1 with value v1, property p2 
with value v2, etc. The properties numConstraints and 
constrainingProperties are used to specify which of the populations 
properties are used to specify the population. In the two examples above:

Node: SP1
type: StatisticalPopulation
populationType: Person
location: EastPodunkCalifornia
gender: Male
maritalStatus: Married
numConstraints: 3
constrainingProperties: location, gender, race

Node: SP2
type: StatisticalPopulation
populationType: MortalityEvent
location:   FaloodaCounty
causeOfDeath: XYZ
numConstraints: 2
constrainingProperties: location, causeOfDeath

SP1 is an abstract set in the sense that it does not correspond to a 
particular set of people who satisfy that constraint at a certain point 
in time, but rather, to an abstract specification, about which we can 
make observations that are grounded at a particular point in time. We 
now turn our attention to the representation of these observations.

   Instances of the class Observation are used to specify observations 
about an entity (which may or may not be an instance of a 
StatisticalPopulation), at a particular time. The principal properties 
of an Observation are observedNode, measuredProperty, measuredValue (or 
median, etc.) and observationDate (measuredProperty can, but need not 
always, be w3c rdf data cube "measure properties", as in lifeExpectancy 
example here: https://www.w3.org/TR/vocab-data-cube/#dsd-example.) In 
the two examples above:

Node: Obs1
type: Observation
observedNode: SP1
measuredProperty: age
median: “23 years”
observationDate: “2016”

Node: Obs2
type: Observation
observedNode: SP1
measuredProperty: count
measuredValue: 1213
observationDate: “2016”

Node: Obs3
type: Observation
observedNode: SP2
measuredProperty: count
measuredValue: 20
observationDate: “2017”

Observations can also have properties related to the measurement 
technique, margin of error, etc. To elaborate on Obs2 above, we can have:

Node: Obs2
type: Observation
observedNode: SP1
measuredProperty: count
measuredValue: 1213
observationDate: “2016”
marginOfError: 22
measurementMethod: CensusACS5yrSurvey

1. Care needs to be exercised when querying StatisticalPopulations, to 
make sure that the query specifies all the constraining properties.
2. We do not yet have a way of using properties which are named in the 
opposite direction e.g. we handle "alumniOf" (relating a person to an 
org), but if the only existing property was "alumni" (relating an org to 
a person).
Received on Monday, 24 June 2019 20:06:50 UTC

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