SDAL addresses social science in new ways that will transform how we understand the world. Among our goals: creating smart and resilient cities, combatting homelessness, understanding the spread of disease and developing effective public health responses, identifying innovation drivers, and meeting the demand for educated graduates in the field.

1. Sallie Keller
Director and Professor of Statistics
Social and Decision Analytics Laboratory
http://sdal.vbi.vt.edu/
Social and Decision
Analytics Lab

2. Social and Decision Analytics Lab
• Part of Virginia Bioinformatics Institute
• Central to “Information Biology” theme
– Study of massively interacting systems, from molecular to
social phenomena
• Collaboration across VT and beyond
– Embrace VBI mantra of transdisciplinary team science

3. Social and Decision Analytics Lab Mission
• Unite statisticians and social scientists to create
quantitative methods for evidenced-based social and
behavioral analyses in support of policy decision-making
• Focus on social issues that will transform our
understanding of the world
– Resilient community ecosystems
– Influences of and how to respond to public health crises
– Economic consequences of an educated society
– Causes of social disasters such as increased urbanization and
homelessness
– Drivers of innovation

4. Social and Decision Analytics Lab Thrusts
1. Developing the Science
of “All” Data
2. Metropolitan
Analytics
3. Health Measurement
Analytics
4. Education and Labor
Force Analytics
5. Industrial Innovation
Analytics
• Theory and methods for integration new and
traditional sources of data for understanding of
the social condition
• Developing analytics to understand community
responses to shocks and the subsequent impact
on the well being of the community
• Integration of public and private sector sources of
data on medical care spending to unravel costs
and impacts for a “healthy” population
• Developing analytics to understand and shape
learning processes, workforce dynamics, and the
impact of education on labor force outcomes
• Partner with industry to move beyond business
analytics and develop information integration
technologies able to accelerate innovation

5. Science of Big Data Origins: Bioinformatics
• Modern “bioinformatics” has grown to encompass
interactions among many levels and properties of
individuals, groups and environments
• It is computationally enabled science
– Increasingly blurring the distinction between social,
environmental, socio-technical and biological domains
– Unprecedented uptake of diverse unstructured information
– In-silico capture and generation of detailed interaction
dynamics
• Relevance to strategic & tactical decision making

6. Thrust 1: Developing the Science of Big Data
Big Data and Social Systems
• Research has traditionally been
based on statistically designed
experiments and surveys
– Clean, well-controlled, limited in scale
(~103) and/or resolution
• Bringing “Big Data” to bear
– Data informed high performance
computing
– Complex socio-technical systems
– Quantitative social science methods and
practice at scale

7. New Data Flows for Social Behavioral Observing
• Data collected faster, while individuals are in the act of
behaving in real life situation
– Increase in the sensitivity of our instruments
– Increased granularity and speed of data collection
– Capture behavior that occurs infrequently
• Adapt methods to make the best use of these data
• New data streams produce new discoveries but should
not be allowed to degrade the scientific approach

8. Big Data - Doesn’t matter what its called, only matters
• It is not just about size
– “All data revolution”
– Traditional and new sources
• Statistics / analytics
– Replication, reproducibility,
representativeness
– Bias and precision
– Descriptive, association, inference
– Change point detection
• Modeling and simulation
– Scenario development
– Counterfactual analysis
what you do with it

9. Thrust 2: Metropolitan Analytics

10. Increasing Urbanization –
Opportunities for Innovation
Urban
and
rural
popula,on
by
regions,
1950,
2011
and
2050
per
cent
of
total
popula,on

11. • Planning and design
• Enhancing productivity
through education
• Enhancing the quality of
life and social engagement
• Optimize adaptability to
population trends and
impact of technology
• Managing factors
influencing climate change
• Controlling poverty
Growth in Cities –
Accelerating Role for Urban Leadership

12. Source: Joel Gurin, NYU
Data is a New Asset Class –
Fuel for the City
Characteristics
• Multi-sourced
• Observational
• Noisy
• Multi-purposed
Data Infrastructure
• For whom?
• With whom?
• To what end?

13. Environment
Meteorology, pollution,
noise, flora, fauna
People
Relationships,
location,
economics,
communication,
activities, health
Infrastructure
Condition, operations
Instrumenting a Metropolitan Area

14. Arlington County Virginia Tech Partnership
• Leverage ConnectArlington fiber optics
– 60 miles of fiber optics that connects over 90 sites
• Goal: Improve the county’s quality of life and services
while accelerating the county’s efficiency and resiliency
• Initial projects:
– Emergency 911 calls, enhancing situational awareness.
– Relationship of network of non-profit providers during
emergency events and social cohesion of neighborhoods
– Develop a mapping of the “age” of the county.
• Foundation for an urban data infrastructure platform
– Useful for research, practice, and policy option development

15. Build the Infrastructure–
Synthetic Information Platform
• Synthetic refers to information synthesis
• Ingests information
– Data
– Judgment
– Models (procedures)
• Protect information
• Capture interaction patterns
• Capture and generate dynamics
• Result: layers of information
projected onto a dynamically changing
coordinate system

16. Arlington is But One Example
• Questions should guide the development of metropolitan
analytics if they are to inform decision making
• Analytics – Provide guidance for metropolitan strategies
– To prioritize issues and create solutions
– To track performance of system components, outcomes, and
impact
– To continuously assess its position relative to the competition
and discern the factors that result in a competitive advantage
for the city
– To benchmark against previous performance, other cities,
stated goals, other characteristics
• Infrastructure will make or break the success

17. Thrust 3: Health Measurement Analytics
“In attempting to arrive at the truth, I have applied everywhere for information, but
scarcely an instance have I been able to obtain hospital records fit for any
purpose of comparison. If they could be obtained, they would enable us to
decide many other questions besides the ones alluded to. They would show
subscribers how their money was spent, what amount of good was really
being done with it, or whether their money was not doing mischief rather than
good.”
Florence Nightingale (1864)

18. Health and the Environment
Goal: Identify links between air
pollution and acute health events
at community level
• Pathophysiological link between out-of-hospital
cardiac arrest and ozone level
• Case cross-over, time stratified design
– Houston, 2004-2011
– 11,754 cases
– Predictor variable is aggregate
ozone over a 3 hour window leading
up to the event
– 20 ppb increase in ozone previous 1
to 3 hours was associated with a
4.4% increased risk
Ensor, et al., Circulation,
Volume 127(11):1192-1199

19. Growth in Health Care Spending
• Health care predicted to reach
20% of GDP by 2020
• Health care expenditures
increased ~29% (2002-2006)
• Developing a satellite account
on medical care spending
• Data include public and
private sources
Spending
on
Health
Care
as
Percentage
of
GDP
(1960-­‐2035)
Source: Congressional Budget Office.
35
30
25
20
15
10
5
0
1960
1970
1980
1990
2000
2010
2020
2030
Percentage
of
GDP
Year
All
Other
Health
Care
Aizcorbe
et
al.,
Survey
of
Current
Business
June
2012:34-­‐47

20. Growth in spending varies by disease
Growth
in
Medical
Care
Spening,
2002-­‐2006
Percent
Endocrine
70.2
Blood
68.9
Complica,ons
of
pregnancy
68.9
Residual
codes
and
unclassified
42.5
Musculoskeletal
system
38.6
Injury
and
poisoning
34.2
Genitourinary
system.
30.5
Diges,ve
system
28.2
Circulatory
system
25.6
Nervous
system
25.3
Neoplasms
24.0
Mental
illness
16.7
Respiratory
system
14.8
Skin
5.8
Symptoms
and
ill-­‐defined
2.4
Congenital
anomalies3
-­‐8.3
Infec,ous
and
parasi,c
-­‐8.7
Certain
perinatal
condi,ons
-­‐28.1

21. Thrust 4: Education and Workforce Analytics
Management of costs and
expenditures
• Be less reliant on public funding
Achieve higher standards and
greater outcomes
• Produce graduates with job-ready
skills whose collective productivity
can immediately impact the
economy
Training for higher cognitive
capabilities
• Utilize a greater diversity of
learning ecosystems
• Across diverse backgrounds,
abilities, and aspirations

22. State-of-the Art: Learning Analytics Dashboards

23. Moving Past the State-of-the-Art
Integration of theory into analytics
• Education, pedagogy, learning, and development
This eclectic approach is both a
strength and a weakness: it
facilitates rapid development
and the ability to build on
established practice and
findings, but it - to date – lacks
a coherent articulated
epistemology of its own.
Clow, 2012
What you have is a
thermometer with no theory
of action behind it. If I have
a fever, nothing here is
going to tell me how to deal
with the fever. All it’s going
to do is tell me I have a
fever.
Mark Schneider, AIR, 2013

24. Key questions for education analytics
Past Present Future
Descriptive
What happened? What is happening
now?
What will happen?
Historical Reporting Assessment
Reporting: Alerts
Extrapolations: Alerts
Associative
Inference
What variables best
explain what
happened?
What interventions
seem reasonable?
What is the best/worst
that can happen?
Relationships and
Modeling
Options Optimization,
Simulation
Causative
Inference
How and why did it
happen?
What interventions
are prescribed?
What does theory
suggest can happen?
Theory-based
Relationships and
Modeling
Recommendations Theory-based
Predictions,
Optimization,
Simulation

25. Project Talent
American
Ins,tutes
of
Research
Projects
in
2014+:
• Data
collec,on
50
years
later.
• Study
of
veterans,
twins,
and
other
groups.
• Role
of
social
media
and
technology
on
lives
(VBI
SDAL)

26. Child HANDS –
Helping Analyze Needed Data Securely
• Tracking early childhood-related
data across public agencies.
• Tracks individuals over time without
tracking personal information
• Flexible; built to bring in additional
data and connect with Virginia
Longitudinal Data System

28. Thrust 5: Industrial Innovation Analytics
Partner with industry to move beyond
business analytics and develop
information integration technologies
able to accelerate innovation through
– Demand-driven and transparent
supply chains
– Continuous feedback & tracking
– Dynamic risk analysis
– Knowledge discovery through
social media
– Diffusion of technology

29. It is a Brave New World
• We need a 10 year strategy
– The landscape is ever changing, tweet today what tomorrow?
• What should government be responsible for?
• How can non-government data influence government
statistics development and official reporting?
• What alliances need to be shaped and built
– Why should the private sector “openly” participate?
• Transparency and information sharing are critical
• Data quality and standards of use need to be formalized
• Needs are motivated by “common good questions”