Linked Open Government Data Analytics

© Information Systems Lab - 2013
http://islab.uom.gr
Linked Open Government Data Analytics
Evangelos Kalampokis, Efthimios Tambouris,
Konstantinos Tarabanis

© Information Systems Lab, University of Macedonia
Aim of the paper
 Introduce the concept of Data Analytics on top of
distributed statistical linked OGD
 Describe the technical prerequisites
 Demonstrate the end-user value

Open Government Data
 More than 180 Open Government Data portals around the globe
provide data that “can be freely used, reused and redistributed by
anyone”

OGD impact
 The majority of existing
applications exploits a
single dataset and
visualizes data on a
map.
 Expected OGD
potential has not yet
realized

Importance of Data in modern societies
 Business Intelligence
 Evidence based policy-making
 Academia

Open Statistical Data
 A big portion of Open Government Data concerns statistics such as
population figures, economic and social indicators
 For example, the majority (5867 out of 6098 datasets) of the data
published on the EU Open Data Portal are of statistical nature

 But…although OGD enables free access to everyone, data is often
isolated (e.g. due to the available formats)
Data Silos
http://www.flickr.com/photos/rachelrusinski/526260022

Vision: Linked Open Government Data Analytics
 Combining statistical OGD that were previously closed in disparate
sources
 Performing data analytics on top of combined data
 Gaining unexpected and unexplored insights into different domains
and problem areas.

Combining Statistical Data
 Requires effort to:
– Discover data (e.g. datasets sharing common joint points and thus allow
for further analysis)
– Collect data
– Clean data (timely, accurate, relevant data)
– Transform data (common formats)
– Integrate data (interoperability, levels of granularity etc.)
– Visualize and statistically analyze (semi-automatic according to the type
of variables and measures)
 We need to shift this effort from end-users to data-providers
http://www.flickr.com/photos/tetsumo/3586864217

Connecting Data Silos
 We need an infrastructure that will enable connecting data silos over
the Web and thus reducing the effort required for statistical data reuse
 This is where Linked Data comes in…
http://www.flickr.com/photos/sethwoodworth/2303531107

Linked Data
 Items in a dataset are identified using URIs
 URIs are dereferenceable using HTTP
 RDF links to other URIs in other datasets are included

Technical Prerequisites
 Metadata for data discovery
 Vocabularies
 Code lists, concept schemes and classifications
 Typed links (e.g. olws:sameAs) between
– Dimensions definitions
– Values of dimensions
– Categories of measures

RDF data cube vocabulary

The UK Elections Case
 Objective:
– To gain insights regarding UK elections through OGD
 Starting point:
– Data regarding the results of two UK general elections from 2005 and
2010 – in both national and constituency level (Open Data in Guardian)
 OGD:
– We need to discover data that could be analyzed together with the
election results data (i.e. that share common joint points)

OGD
 Source:
– Data from data.gov.uk
 Datasets:
– Unemployment and poverty between 2005-2010 in the UK parliament
constituencies
– In this paper we concentrate on unemployment due to space limitations

Data Conditioning: Linked Data Creation

Linked Data Analytics
 Enables the semi-automatic visualization and performance of
statistical analyses based on :
– Joint points (i.e. variables that are described at a parliament constituency
level)
– Type of variables (e.g. Regression in the case of continuous and
classification in the case of categorical)

Logistic regression Classification Analysis
 Measures the relationship between a categorical dependent variable
and one or more continuous independent variables by converting the
dependent variable to probability scores through the logistic function
 Identify the relationship of unemployment rate of a parliament
constituency and the probability P(A) a particular political party to win
the elections in the constituency

P(A) 
1
1ey

y  c0 c1x1 ...cn xn

Visualization Unemployment & Labours Results (2005)
 The probability for the
Labour Party to win in a
constituency increases as
the unemployment rate of
the constituency increases
 In constituencies with
unemployment rate > 5%
the Labour Party has
strong probability to win
 In 2005 the average
unemployment rate was
3.35%

Visualization Unemployment & Labours Results (2010)
 The pattern is the same
but was moved to the right.
 The average
3.35% in 2005 and 7.5% in
2010

Visualization Unemployment & Cons Results (2005)
 In 2005 the average
3.35%
 If unemployment rate > 5%
then Conservatives have
very small probability to
win

Visualization Unemployment & Cons Results (2010)
 In 2010 Conservatives do
not win in constituencies
with unemployment rate
>13%
 However the average
unemployment rate
increased from 3.5% to
7.5%
 The logistic regression
pattern is the same

Statistical model creation
 Logistic function that measures the probability P(A) for a party to win in
a specific parliament constituency
 For example, consider the Labour Party in the 2010 elections
 x is the unemployment rate of the constituency.
 In a constituency with 12% unemployment rate the probability for the
Labour Party to win is P(A)=0.8

P(A) 
1
1  ey

y  3.823 0.437 x

Conclusion and Future Work
 Significant efforts for developing tools and applications facilitating
Open Government Data (OGD) publishing and reuse
 OGD has not yet realized the full potential.
 Today, data analytics employ data closed in isolated systems
 We claim that the real value of OGD will emerge from performing Data
Analytics on top of combined statistical datasets
 Linked Open Government Data Analytics show the road ahead
 Future work includes development of a platform enabling semi-
automatic identification of important relations between variables
described in distributed datasets

Acknowledgments
 The work presented in the paper is partly funded by

Linked Open Government Data Analytics

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