1.
Social Observatories:
Envisioning Regional Data
Centers for the 21st Century
Sandra Hofferth
University of Maryland
April 30, 2014

2.
The Challenge to SBE sciences
 Globalization
 Social media
 Declining response rates to surveys
 Rapid social and environmental changes
 Rapid shifts in the economy and in social
groups
 Many current challenges are local

3.
Key SBE Scientific Questions
 Opportunity and mobility: Place-based studies can help us better
understand important issues such as the local sources of social inequality
and disadvantage
 Place-based local studies can document the organization of
neighborhoods and institutions, distribution and quality of schools,
access to medical clinics and facilities, and employment
opportunities in the informal sector
 Adaptation and Change: Place-based studies are better for examining
responses to natural, economic, and social shocks (e.g., hurricane
Katrina) – but we need to be in the communities before these events
occur
 Behavior change: Studies of multiple places can contribute to our
knowledge of the context in which micro-level behavior occurs: Could
run experiments in different sites to see how the results vary across
contexts. This has rarely been done.

4.
Data for People and Places
 There is increased interest in linking different types of
data, particularly to situate people in place. Many data
sets do not provide the option to link with place at a fine
(e.g. tract, neighborhood) level.
 Included are individual data with great detail or
granularity. Other data come from a variety of sources –
administrative, local land use, census.
 There is a need to collaborate across disciplines.
 We need tools to design better policy instruments that
address human variability at the local level.

5.
Origins of the Observatory Idea
 NSF sponsored some 8 workshops with members of the
scientific community from 2005 to present, originally
focusing on cyber-infrastructure for the social, behavioral
and economic sciences
 Recent ones: December 2010, Oct. 2011, Feb 2012, and
May 2012
 Ten of us submitted a grant proposal to NSF for a
Research Coordinating Network, the Social Observatories
Coordinating Network (SOCN); we are funded for 3
years to obtain feedback from the scientific communities
about this idea and produce a recommendation..

6.
What is an Observatory?
 Each observatory or regional data center would
be an entity, whether physical or virtual, that is
charged with collecting, curating, and
disseminating data from people, places, and
institutions in the United States.
 These centers must provide a basis for inference
from what happens in local places to a national
context and ensure a robust theoretical foundation
for social analysis.

7.
Why the Observatory Approach?
 Observatories have a long history in the natural and
ecological sciences, and they have served as points around
which those communities have come together to strengthen
their disciplines
 The large national longitudinal surveys can be thought of as
a type of observatory, and have provided valuable data that
are standardized and consistent but, with some exceptions,
they cannot provide a detailed picture of a local area.
 AND there are growing problems with their ever rising
costs, and with declining response rates across many of the
surveys
 AND many questions of interest to parts of SBE disciplines
are not addressed by these surveys

8.
How will the Observatories be
Nationally Representative?
 To accomplish these objectives, we
propose to embed these regionally-based
data centers in a nationally representative
population-based sample that would enable
the observatory data to be aggregated in
such a way as to produce a national picture
of the United States on an ongoing basis.

9.
The Basic Problem
 People are highly concentrated in places
 Many places have few people
 Key question: Do you use place or
population as the basis of the design?

10.
Our Proposal:
 A sample of about 400 census tracts would be selected to
represent the U.S. population while also fully capturing the
diversity that characterizes local places.
 A unified centralized framework but distributed model
 Each observatory would be responsible for gathering information
in a preselected set of census tracts
 The entire set of information gathered by all the observatories
would provide a national sample to address core questions
common across the observatories.
 In addition, each regional observatory could develop a set of
priorities for research that differ from those of other
observatories.

11.
What types of data could be collected?
 Administrative sources (to identify people within tracts):
 Voting records, USPS address files
 Motor vehicle files
 Reverse phone directories
 Vital statistics
 Wage files
 Credit card data,
 Medicaid/welfare/food stamps data
 Data from sensors – air quality, noise, smartphones, time, exposures, distance
 Aggregate census data for tracts
 Survey data, ethnographic data, experiments
 Social media data (location-specific)
 Census/ACS for validation

12.
How would the observatory system
facilitate access to all its data?
 We will examine different models of data
sharing and confidentiality - from
restricted access (Census RDCs) to remote
access and contracts.
 We will be holding a workshop to address
the issues of confidentiality in data sharing
and linking.

13.
Examples of centers and
applications
 Chicago, Il
 National Neighborhood Indicators Project
 Portland, Oregon
 Household Environmental Impacts and
Exposure

14.
Integrated Database of Child and
Family Programs in Illinois
 Robert Goerge, Urban Center for
Computation and data,
 Chapin Hall, University of Chicago

15.
Overall Objective
 To reduce the burden of multi-problem
families who contribute the most (86%) to
cost of social services in the city:
 Unemployed parents
 Low socioeconomic status
 Welfare program participants
 Single-parent families
 Mothers who had their first child as an adolescent

16.
Chapin Hall Integrated Data Base
1990-present
 Schools - PreK, Head Start, Public schools
 UI wage and benefit records
 SSA, TANF, SNAP, Child care subsidies
 Foster care, child maltreatment
 Medicaid providers, claims, population
 CPD arrests, juvenile court, incarceration

17.
Method: Improved Targeting
 Through an extensive mapping process, the
city knows exactly where the bulk of
problematic families live.
 They are concentrated in a few census
tracts in the city
 Can focus on those areas for programs.

18.
National Neighborhood Indicators
Partnership (NNIP)
 Thomas Kingsley
 Urban Institute

19.
NNIP
 Collaborative effort since 1995
1. Urban Institute & local partners; now 37 cities
2. All partners build and operate neighborhood
level information systems; administrative data
from multiple sources
 Purpose
1. Strengthening neighborhoods
2. Promoting collaboration
3. Improving local decision-making

20.
National Neighborhood Indicators PartnersAtlanta
Austin
Baltimore
Boston
Camden
Chattanooga
Chicago
Cleveland
Columbus
Dallas
Denver
Des Moines
Detroit
Grand Rapids
Hartford
Indianapolis
Kansas City
Louisville
Memphis
Miami
Milwaukee
Minneapolis-St. Paul
Nashville
New Haven
New Orleans
New York City
Oakland
Philadelphia
Pittsburgh
Portland
Providence
Sacramento
Saint Louis
San Antonio
Seattle
Washington, DC

21.
Neighborhood level –
social/economic/physical
 Employment
 Births, deaths
 Crimes
 TANF, Food Stamps
 Child care
 Health
 Schools
 Property sales & prices
 Property ownership
 Code violations
 Assessed values
 Tax arrears
 Vacant/abandoned housing
 City/CDC plans
Parcel level –
physical/economic

22.
23e.g. http://datadrivendetroit.org/projects/cdad/

23.
Coalition for a Livable Future:
Greater Portland Pulse Project
 Institute of Metropolitan studies
 University of Portland
 Meg Merrick, PhD

24.
Background & Objectives
• University-community partnership
• Created a relational database infrastructure
housed at Portland state – Regional Equity
Atlas
– 111 variables and 64 indicators in ‘real time’
• For bi-state regional planning
• Interactive web tool and download capacity
• Expansion for state-level efforts

25.
https://clfuture.org/equity-atlas

26.
Linking Local Consumption to Global
Environmental Impacts
 Klaus Hubacek
 Department of Geography
 University of Maryland

27.
Objectives
 Understanding of what drives peoples’
consumption activities
 Estimating household environmental
impacts and exposure
 Locally and globally

28.
Linking Local Consumption to Global Impacts

29.
What are the Barriers?
 Differences in data collection from place to
place, state to state
 Data alignment between projects, linking
database architectures, web services,
people
 Human capital in government to do this
 Legal problems in sharing data

30.
“Good luck getting the data sharing agreement
through our lawyers….”

31.
Benefits of this Approach
 But – many of the problems have been
locally worked out.
 All three examples successfully engaged
their communities and met community
needs.
 Along with a national coordinating center
this model could be a valuable contribution
to the national data infrastructure.

32.
What do we gain from this
platform?
 A national framework for studying local
contexts for social dynamics
 A national SBE cyberinfrastructure to serve
21st century society
 A national framework for interdisciplinary
collaboration

33.
Thank you
 Sandra Hofferth
hofferth@umd.edu
 http://socialobservatories.org