policy meets social & decision informatics · social and decision analytics laboratory social...

SOCIAL AND DECISION ANALYTICS LABORATORY

SOCIAL AND DECISION ANALYTICS LABORATORY

Stephanie Shipp, Deputy Director and Research Professor [email protected]

Social and Decision Analytics Laboratory http://sdal.vbi.vt.edu/

Policy Meets Social & Decision Informatics

An Overview of

The Fields Institute for Research in Mathematical Sciences Workshop on Big

Data for Social Policy

June 12, 2015

SOCIAL AND DECISION ANALYTICS LABORATORYSOCIAL AND DECISION ANALYTICS LABORATORY

Overview of Big Data and Social Policy Workshop

Day 1 – Big Data and Official/Government Statistics Day 2 – Network Models and Agent Based Modeling Day 3 – Living Analytics and Privacy Day 4 – Urban Analytics


Defining Big Data is not easy…..

•  “The definition of Big Data is an imprecise description of a rich and complicated set of characteristics, practices, techniques, ethical issues, and outcomes all associated with data.” AAPOR 2015.


It all starts with data: The All Data Revolution

1.  Volume 2.  Velocity 3.  Variety


Then there was 4

1.  Volume 2.  Velocity 3.  Variety 4.  Veracity


And 5

1.  Volume 2.  Velocity 3.  Variety 4.  Veracity 5.  Value


Now 7

1.  Volume 2.  Velocity 3.  Variety 4.  Veracity 5.  Value 6.  Variability 7.  Visualization


All Data & Social Informatics

•  Slogging thru the Hard Stuff –  People are in all the loops –  Problems are not ‘puzzles’ to be

solved

•  Need questions to drive the informatics


Big Data and Official/Government Statistics - Disruptive Innovations -- Game Changers

- •  1790 - First U.S. census •  1871 - First national census for country of Canada •  1894/1902 – U.S. Bureau of Labor Statistics & Census Bureau created •  1930s – Statistically designed surveys •  1971 – Statistics Canada created •  1970s – Quiet revolution (role of analysts for policy) •  1980s - Increasing deployment of longitudinal surveys •  1990s – Network analysis, ABMs, GIS, machine learning, ….. •  2010s – Big All Data revolution and applications to govt at all levels, companies, and

academia


Big Data and Social Policy – Opportunities Compared to survey data, big (digital, organic, all) data can improve:

•  Small area estimates •  Unit cost is low •  Real-time availability •  Finer granularity of data •  Tracking and understanding behavior •  Opportunities for natural experiments •  Increased capacity - advances in computational methods - ABM, network

analysis, machine learning, GIS, other computer and statistical methods that can ingest and process massive amounts of data


Big Data and Social Policy Challenges •  Lack of ‘common good’ approach to access data (Groves) •  Lack of skills – need for a data savvy population •  No control over data quality; unknown error structure •  Lack of standards - data quality framework, replicability, other… •  Lack of transparency •  Privacy and confidentiality –

–  Proposed Canadian legislation to amend human rights law to clarify that discrimination based on genetic tests is prohibited


Real Questions vs. Perceived Question Type

Jeffery T. Leek, and Roger D. Peng Science 2015;347:1314-1315


Lessons of the Past Keep Are Still Relevant Today!

Competing Ingredients •  Random Innovation •  Systematic Thinking for Social Action


Lessons of the Past Keep Getting Re-Printed!

Competing Ingredients •  Random Innovation •  Systematic Thinking for Social Action

"Theory without data is myth; data without theory is madness.” Phil Zuckerman


Mash It Up with Bioinformatics

ontariowildflower.com

Locust Photo by Ocean/Corbis

bioweb.uwlax.edu

DNA is Middleware

Dynamics will overrun global optimization

CAD CallSummary

Unit MovementSummary

IncidentSummary

NFIRS,Med Report

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Loyal Customers or Loyal Loca)ons?


Ensor, et al., Circulation, Volume 127(11):1192-1199 American Heart Association

Expanding to Population Dynamics: Houston, TX

High Performance Computing


Data

Data Storage and

Warehousing

Air Quality Model


Data Data Storage and

Warehousing

Synthetic Population Information Model

Database

Database

Monitor Readings

LandScanHERENCESACS

U.S. Census

American Community

Survey

National Household

Travel Survey

Population synthesis using Iterative

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Activity assignments using Fitted Value

Methods

Location choice using Gravity Models

Baseline Synthetic

Population for Houston

Concentration Levels by Hour

and Activity Location

Temporal and Geographic

Concentrations using Inverse

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in silico Platform for Environmental CouplingPersonal Exposure Model

Exposure Calculations by Individual and

Activity Location

Inputs

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Individual

Individual Temporal and Geographic

Activity Patterns


Ozone Concentrations

10:00 am, August 26, 2008 High Performance Computing


Data

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Data Data Storage and

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Database

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Population synthesis using Iterative

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Baseline Synthetic

Population for Houston

Concentration Levels by Hour

and Activity Location


Concentrations using Inverse

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in silico Platform for Environmental CouplingPersonal Exposure Model

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Inputs

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Activity Patterns


Ozone Concentrations

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•  Ingests informa)on –  Data –  Judgment –  Models (procedures)

•  Protect informa)on •  Capture interac)on paHerns •  Capture and generate dynamics •  Result: In-‐silica experimental

pla0orm

Need to Build the Infrastructure – Synthe'c Informa'on Pla2orm

SYNTHETIC PLATFORM

Individual

ECONOMY

Harnessing Real World Data

Exploratory Analysis

Textual Analysis

Network Analysis

Agent-Based Modeling Dynamic

Simulation

Machine Learning

Consumer Price Index GDP Unemployment Rate

To Empower Policy and Decision Making

References American Associa)on of Public Opinion Research, AAPOR Big Data Task Force, AAPOR Report on Big Data, February 12, 2015 Execu)ve Office of the President. 2014. “Big Data: Seizing Opportuni)es, Preserving Values.” Washington DC. Retrieved January 28, 2015 (hHp://1.usa.gov/1hqgibM). Keller, Sallie Ann, Steven E. Koonin, and Stephanie Shipp. 2012. “Big Data and City Living – What Can It Do For Us?” Significance, 9(4): 4-‐7 Lazer, David M., Ryan Kennedy, Gary King, and Alessandro Vespignani. 2014. “The parable of Google Flu: Traps in big data analysis.” Science, 343(6176): 1203-‐1205. Leek, Jeff. 2014a. “Why big data is in trouble: they forgot about applied sta)s)cs.” Simplystats blog, May 7. Retrieved January 28, 2015 (hHp://bit.ly/1fUzZO1). New York Times Editorial Board. 2014. “BeHer Governing Through Data.” New York Times, August 19. Retrieved January 28, 2015 (hHp://ny).ms/1qehhWr). OECD. 2013. New Data for Understanding the Social Condi)on.OECD Global Science Forum. February. Okun, Arthur M. Equality and efficiency, the big tradeoff. Brookings Ins)tu)on Press, 1975. Rivlin, Alice M. Systema)c thinking for social ac)on. Vol. 3. Brookings Ins)tu)on Press, 1971 United Na)ons. 2014. A Suggested Framework for the Quality of Big Data, Deliverables of the UNECE Big Data Quality Task Team, December, 2014

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