Designing An Information Commons for Sustainability Science: Lessons Learned from a World Data Center

Marc LevyCIESIN

Earth Institute, Columbia University

Presentation to International Workshop on Designing Global Information Commons for Innovation in Frontier Sciences

8-10 November 2007Tokyo, Japan

World Data Center for Human Interactions in the Environment

Evolution of the Data Provider Model

Researcher

Researcher

Repository

Public

ResearcherResearcher

Public

Researcher

Researcher

Data

Cyberinfrastructure

Research community

Decision-making community

Data Domain Understanding (What do these data mean? Are they suitable for my use?)

Access

Linkage Understanding (How do these data relate to other data?)

DiscoveryReliability and continuity

Catalogs

Documentation, visualization

Integration

On-line databasesStewardship

Interoperability

Standards; georeferencing; time-stamping

Data Domain Understanding (What do these data mean? Are they suitable for my use?)

Access

Linkage Understanding (How do these data relate to other data?)

DiscoveryReliability and continuity

Interoperability

What data communities provide

Dynamics of Human-Environment Interactions

OpportunityEconomic loss

Complex change

Equilibrium

This is our world• Multiple stresses

– Economic– Demographic– Political– Change in land cover– Water scarcity– Soil fertility problems

• Complex outcomes– Livelihoods– Health– Equity– Security– Culture

Helping Users Make Wise Choices is Hard!

Presumed Practical Competence of Group as Function of Number of Disciplines

0

5

10

15

20

25

1 2 3 4 5

Disciplines

Ph.D

B.A.

High School

5th

Grade

Traditional Documentation not enough

Multi-faceted approach requiredComparative Guides

Visualizations

Common Pitfalls

Examples

Citations

http://sedac.ciesin.org/citations/ CitationIndex.html

, , , , g , , j , , , , , ( ) gPoor Livestock Keepers at the Global Level for Research and Development Targeting. Land Use Policy, 20(4): 311-322.

Tobler, W., Deichmann, U., Gottsegen, J., and Maloy, K. (1997) World Population in a Grid of Spherical Quadrilaterals. International Journal of Population Geography, 3: 203-225.

van Lieshout, M., Kovats, R.S., Livermore, M.T.J., and Martens, P. (2004) Climate Change and Malaria: Analysis of the SRES Climate and Socio-Economic Scenarios. Global Environmental Change, 14(1): 87-99.

Vassolo, S. and Döll, P. (2005) Global-scale Gridded Estimates of Thermoelectric Power and Manufacturing Water Use. Water Resources Research, 41.

Verburg, P.H., and Chen, Y. (2000) Multiscale Characterization of Land-Use Patterns in China. Ecosystems, 3(4): 369-385.

Viviroli, D. and Weingartner, R. (2004) The Hydrological Significance of Mountains: from Regional to Global Scale. Hydrology and Earth System Science, 8(6): 1016-1029.

Vorosmarty, C.J., Green, P., Salisbury, J., and Lammers, R.B. (2000) Global Water Resources: Vulnerability From Climate Change Acid Population Growth. Science, 289(5477): 284-288.

Vorosmarty, C.J., and Sahagian, D. (2000) Anthropogenic Disturbance of the Terrestrial Water Cycle. Bioscience, 50(9): 753-765.

White, M.A., Hoffman, F., Hargrove, W.W., and Nemani, R.R. (2005) A Global Framework for Monitoring Phenological Responses to Climate Change. Geophysical Research Letters, 32(L04705): 4pp.

White, M.A., Nemani, R.R., Thornton, P.E., and Running, S.W. (2002) Satellite Evidence of Phenological Differences Between Urbanized and Rural Areas of the Eastern United States Deciduous Broadleaf Forest. Ecosystems, 5(3): 260-273.

Wilson, S.J., Steenhuisen, F., Pacnya, J.M., and Pacnya, E.G. (2006) Mapping the Spatial Distribution of Global

Source: Vörösmarty et al. 2000

• 80% of future stress frompopulation

& development, not climate change!

• Future distortions of thewater cycle are inevitable

• Issue gaining momentum in global policy fora

(e.g. Millennium Assessment, World Water Assessment

Programme, MDGs)

Water Stress Changes to 2025

UNH

Less stressNo changeMore stress

•

Information on data quality is critical to judging goodness of fit

It shouldn’t even be this hard

Helping users make wise choices is a

community-building and community-

strengthening task

Moore’s Law Benefits Data Collection Processes Unequally

=/

0

2

4

6

8

10

12

14

16

18

1950 1960 1970 1980 1990 2000

Cen

sus

cost

(per

per

son,

199

0 do

llars

0.01

0.1

1

10

100

1000

10000

100000

1000000

Pro

cess

or c

ost (

$ pe

r 10

00 c

alcu

latio

ns),

log

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Census costProcessor cost

Pace of progress across data domains is very uneven.

Greater the divergence, greater the Integration Frustration

Environmentalvariability &change

Variability &change in human conditions

Exposure

Sensitivity Resilience

Adjustments & Adaptations

Resilience

System Responses

Socioeconomic Conditions

Biophysical conditions

∆

Risks & impacts

Coupled System

Exposure

Structure-functionbiosphere & change

Structure-functionpolitical economy& change

Temporal and spatial dynamics omitted

Perturbations &Stresses[environmental & socioeocnomic; kinds & sequencing

Turner, Matson, Kasperson and Kasperson, 2002

These are frequently missing links

Identify and Fill Gaps!

CIESIN, Gridded Population of the World, 350,000 census input units

73 M people. Largest urban extent

Exposure to Multiple Natural Hazards

Seismic hazards include earthquakes and volcanoes; hydrological hazards include floods, cyclones, and landslides

Growing Season and DroughtDistribution of non-poor population

Distribution of poor and extremely poor population

Millennium Ecosystem Assessment, 2005

Many more gaps to fill!• Roads • Migration• Time-series spatial data on population,

urbanziation• Spatial economic data• Soil fertility• Spatial health data

Prioritize

Assign roles

Be transparent

Persevere!

Challenge of model data

Input data sets

Harmonized data sets Model

Output data sets

ConclusionsClear ExplanationIncomprehensible

ExplanationTOUGHEST NUT

Interoperability

Standards

Develop, adopt, refine, encourage use of standards for representing and distributing data

Brute Force

Reprocess, reformat, recode data to be consistent with established framework data

Example: Household Surveys

Stewardship

• Almost always under-provided• Everyone underestimates the speed by

which data becomes invisible or unintelligible

• Inter-disciplinary, problem-oriented data especially vulnerable

Data Domain Understanding (What do these data mean? Are they suitable for my use?)

Access

Linkage Understanding (How do these data relate to other data?)

DiscoveryReliability and continuity

Catalogs

Documentation, visualization

Integration

On-line databasesStewardship

Interoperability

Standards; georeferencing; time-stamping

Conclusions• We don’t know how to do everything yet, but we

know a lot more now than a decade ago• The investments show positive economies of

scale– each step forward getting the data questions right

generates more research and policy return than previous steps

• But what remains is going to require sustained, focused effort– There’s a lot of hard stuff yet to do

• Historically, funders don’t like this kind of work– That seems to be changing

Plan for the issue-cycle

Hype

Growth

Despair

Consolidation

Origin