Community Disaster Resilience Index
Also known as: CDRI, Climate Disaster Resilience Index, Community Resilience Index
The Community Disaster Resilience Index (CDRI) is a survey-based, multi-level composite-index method for assessing the resilience of communities to disasters, developed in the action-oriented form by Jonas Joerin, Rajib Shaw, Yukiko Takeuchi, and Ramasamy Krishnamurthy and applied in Chennai, India. CDRI decomposes resilience into a hierarchy: a small set of dimensions (commonly physical, social, economic, institutional, and natural), each split into parameters, each measured by several variables scored on a Likert scale. Variables are combined into parameter scores, parameters into dimension scores, and dimensions into an overall index, with weights typically elicited from stakeholders or experts. Unlike secondary-data indices, CDRI is built to be participatory and diagnostic — its purpose is to reveal which dimension of resilience is weakest in a given community so that action can be targeted there.
Key highlights
- Produces a decomposable, multi-level profile that pinpoints the weakest dimension and parameter for targeted action.
- Participatory by design, incorporating resident and stakeholder knowledge through survey scoring and elicited weights.
- Flexible skeleton that adapts to local context while retaining a comparable structure across communities.
- Action-oriented loop supports repeated measurement to track whether interventions improve resilience.
Intuition
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How it works
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When to use it
Use CDRI when you want a participatory, diagnostic assessment of community resilience that pinpoints which dimensions are weakest and supports local action planning, especially where primary survey and stakeholder data can be collected. It suits neighborhood-, ward-, or community-scale work in which engaging residents and officials in scoring and weighting is feasible and valued, and where comparison across a manageable number of units guides resource allocation. CDRI is less appropriate when only secondary data are available across many places (a normalized index like BRIC fits better), when stakeholder weighting would introduce unacceptable subjectivity, or when the goal is to predict losses from a specific event rather than to profile standing capacities. Its Likert-and-weight structure should be paired with documentation of anchors and weights and with sensitivity checks.
Strengths & limitations
- Produces a decomposable, multi-level profile that pinpoints the weakest dimension and parameter for targeted action.
- Participatory by design, incorporating resident and stakeholder knowledge through survey scoring and elicited weights.
- Flexible skeleton that adapts to local context while retaining a comparable structure across communities.
- Action-oriented loop supports repeated measurement to track whether interventions improve resilience.
- Likert scoring is ordinal and subjective, so aggregated scores can be sensitive to respondent framing and anchor definitions.
- Stakeholder-elicited weights encode value judgments that may differ across assessors and bias comparisons.
- Primary data collection is resource-intensive, limiting the number of communities that can be assessed.
- Linear weighted aggregation may obscure interactions and thresholds among resilience components.
Common pitfalls
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Applications
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Frequently asked
How is CDRI different from BRIC?
Both are composite resilience indices, but they differ in data and purpose. BRIC is built from secondary census and administrative data, uses min-max normalization and equal weights, and is designed for comparable scoring across many places. CDRI is built from primary survey and stakeholder scoring on Likert scales with elicited weights, and is designed to be participatory and diagnostic at the community scale, pinpointing the weakest dimension for action. CDRI trades some comparability and scalability for local grounding and actionability.
Why does CDRI use a hierarchy of dimensions, parameters, and variables?
The hierarchy makes resilience both measurable and decomposable. Variables are concrete enough to score directly, parameters group related variables into meaningful sub-themes, and dimensions span the major facets of resilience. Aggregating upward yields an overall index, while reading downward reveals exactly which components drag the score down. This two-way structure is what lets CDRI move from a single number to a targeted action priority.
How are the weights in CDRI determined?
Weights are usually elicited from stakeholders or experts, reflecting local judgments about which variables and parameters matter most for resilience in that setting. Some applications use structured methods such as pairwise comparison or the analytic hierarchy process to derive consistent weights. Because weights embed value judgments, good practice is to document how they were obtained and to run sensitivity analyses showing how much the rankings and the identified weakest dimension depend on the chosen weights.
Sources
- 1.Joerin, J., Shaw, R., Takeuchi, Y., & Krishnamurthy, R. (2012). Action-oriented resilience assessment of communities in Chennai, India. Environmental Hazards, 11(3), 226-241.
- 2.Cutter, S. L., Ash, K. D., & Emrich, C. T. (2014). The geographies of community disaster resilience. Global Environmental Change, 29, 65-77.
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Cite this page
ScholarGate. (2026, June 23). Community Disaster Resilience Index. ScholarGate. https://scholargate.app/disaster-studies/community-disaster-resilience-index