Susan L. Cutter, Christopher T. Emrich, and Christopher G. BurtonHazards & Vulnerability Research Institute

University of South CarolinaColumbia, SC USA

scutter@sc.edu

Baseline Indicators for Disaster Resilient Communities

CARRI WorkshopJuly 14-15Broomfield, CO

Data issues

Availability of variables (national, regional)

Recent values (esp. Census)

Role of census undercounts

Coverage of variables (spatial & temporal)

How to quantify unquantifiable?

Example: Reliance on old socio-demographic data: SoVI

SoVI for SC uses 32 variables based on 2000 Census

American Community Survey provides more recent information (~2005)– Places >65,000

– Four variables unavailable (nursing home residents/capita; % rural farm populations; % social security recipients; % populations living in urban areas

– Only 20/46 SC counties have ACS updates

SoVI 2000/SoVI ACS comparisons SoVI score not significantly different Spatial representation changes: function of changes in % native

American; % Asian, % Hispanic, median age, people per housing unit; % renters, % unemployment, per capita income, % rich, % poverty, % agricultural employment, median contract rent; median housing value

Creating the Baseline: Steps Theoretical or conceptual framework Indicator selection criteria

– Assessment of measurement error– Imputation of missing data

Index structure (inductive or statistical vs. deductive or theory)

Data transformation (per capita; density, absolute)

Normalization (scaling & standardization)

Multivariate analysis Weighting (for each component or variable)

Aggregation (how combine into final product)

Sensitivity analysis

Resilience Indicators:Components for Baseline

Ecological

Social

Economic

Infrastructure

Institutional capacity (mitigation)

Community competence

CARRI white papers; Norris et al. 2008; Cutter et al. 2008

Theoretical framework

Ecological

Variable Source Effect on Resilience

% Land area in 100-year flood plain Cutter et al. 2008 negative

% Land area subject to SLR Cutter et al. 2008 negative

% Soil erosion Cutter et al. 2008 negative

% Green space/undisturbed land Cutter et al. 2008 positive

% Urban (access variable) Cutter et al. 2008 positive

% Forested land cover (wildfire potential)

Cutter et al. 2008 negative

% Land with hydric soils (liquefaction) Cutter et al. 2008 negative

% Wetland loss (ecosystem services) Gunderson 2009 negative

Indicator selection

SocialVariable Source Effect on

Resilience

Racial/ethnic inequality (Abs. value of difference in % black & % white)

Norris et al. 2008; Cutter et al 2008

negative

Educational inequality (Abs. value of difference less than 9th grade & college)

Norris et al. 2008; Morrow 2008

negative

Physicians/10,000 (health access) Norris et al. 2008 positive

Elderly (%) Morrow 2008 negative

Social vulnerability index (SoVI) Morrow 2008; Cutter et al. 2008; Tierney 2009

negative

Transport challenged (% no vehicle) Tierney 2009 negative

Communication challenged (% no phone) Colten et al. 2008 negative

Language competency (% ESL) Morrow 2008 negative

Crime rate (per 10,000) Colten et al. 2008 negative

Special needs (% pop with disabilities) Heinz Center 2002 negative

Health coverage (% pop with coverage) Heinz Center 2002 positive

Population wellness (% black infant mortality rate)

Norris et al. 2002, 2008 negative

Variable Transformations

Economic

Variable Source Effect on Resilience

Housing capital ( difference % white homeowner and % black homeowner)

Norris et al. 2008 negative

Homeowners (%) Norris et al. 2008; Cutter et al. 2008

positive

Employment (%) Mileti 1999 positive

Median household income Norris et al. 2008; Cutter et al. 2008

positive

Poverty (%) Norris et al. 2008; Morrow 2008; Enarson2007

negative

Single sector employment (% primary sector + tourism)

Berke & Campanella2006

negative

Female labor force participation (%) NRC 2006 positive

Business size (% large >100 employees) Norris et al. 2008 positive

Institutional

Variable Source Effect on Resilience

Recent hazard mitigation plan (yes/no)

Burby et al. 2000; Godshalk 2007

positive

NFIP policies (per occupied housing unit)

Tierney et al . 2001 positive

Storm Ready participation (yes/no) Multi-hazard Mitigation Council 2005; Tierney et al . 2001

positive

Municipal expenditures (fire, police, emergency services as a %)

Sylves 2007 positive

Infrastructure

Variable Source Effect on Resilience

Mobile homes (%) Cutter et al. 2003 negative

Shelter capacity (% rental vacancy) Tierney 2009 positive

Medical capacity (hospital beds/10,000) Auf der Heide and Scanlon2007

positive

Building permits for new construction (#) NRC 2006 negative

Evacuation potential (arterial miles/mi2) NRC 2006 positive

Evacuation potential (# highway bridges) General knowledge negative

Housing age (% built 1970-1994) Mileti 1999 negative

Community Competence

Variable Source Effect on Resilience

Political fragmentation (# local governments and special districts)

Norris et al. 2008 negative

Previous disaster experience (PDD, yes or no)

Cutter et al. 2008 positive

Social connectivity (VOADs yes or no) Morrow 2008; Norris et al. 2005

positive

Dependency ratio (debt/revenue) Cutter et al. 2003 negative

International migration (%) Morrow 2008 negative

Sense of place (% borne in state and still live here)

Vale & Campanella 2005 positive

Social capital (churches/capita) Morrow 2008; Tierney 2009

positive

Social capital (% registered votersvoting in 2004 election)

Cutter et al. 2003 positive

Internal migration (% outmigration) Vale and Campanella2005

negative

Scaling Example

=

=

=

=Common scaling techniques:Z-scoresLinear scaling, Min-max transformationLinear scaling, maximum value transformation

Normalization

Putting it all together

Linear min-max scaling: X-min/max-min

Purpose: scale values from 0 to 1 where 0 reduces resilience; 1 increases resilience

Scores theoretically range from -27 to +21; actually range from -3.54 to +4.71

Rank order of counties

Most Resilient

Jasper

Lee

Saluda

Williamsburg

Dillon

Least Resilient Lexington

Kershaw

Greenville

Calhoun

Horry

Mapping Results

46

4544

43

42

1

2

3

4

5

Category Weighting

Common weighting schemes:Equal weightsUnequal weights

Weighting

Ecological17%

Social25%

Economic17%Institutional

8%

Infrastructure14%

Community competence

19%

Unequal Weights

Ecological16%

Social16%

Economic17%

Institutional17%

Infrastructure17%

Community competence

17%

Equal Weights

New map with equal weights

Components of Resilience

Identifying the dimensional drivers

County Ecological Social Economic Infrastructure Institutional Community Competence

Score

Lexington 0.07 -0.13 0.33 -0.17 0.65 0.18 0.93

Greenville 0.08 -0.16 0.24 -0.17 0.71 0.10 0.80

Charleston -0.28 -0.13 0.15 -0.15 0.65 0.21 0.45

Beaufort -0.40 -0.18 0.22 -0.16 0.86 -0.10 0.24

Lee -0.13 -0.42 0.02 -0.16 0.50 0.35 0.16

Mean values for each dimension

SC strong on institutionalIllustrates opportunities for intervention

Next steps

Refine present methodology (variables, analytical techniques)

Different aggregation schemes (PCA)

National comparisons (coastal counties, other regions)

Downscale to sub-county (communities, census tracts)

Couple baseline indicators with process-oriented capacity building for disaster resilience

Thanks and come for a visithttp://webra.cas.sc.edu/hvri/