STRENGTHENING QUALITY, RELIABILITY AND SUSTAINABILITY OF NATIONAL DISASTER DATABASES

1. STRENGTHENING QUALITY, RELIABILITY AND SUSTAINABILITY OF NATIONAL DISASTER DATABASES R. Below and F. Vos EM-DAT TechnicalAdvisory Group Meeting New York, 26-27 October, 2009

2. Introduction

3. Introduction • Natural disaster events increase in magnitude and frequency, and stay a major obstacle to development • Lack of accessible, comprehensive and reliable data on disasters hinder effective disaster planning, mitigation, preparedness and reduction strategies • Urgent and continual need for disaster impact data collection and sharing, in order to safe lives, alleviate suffering and reduce economic losses • Systematic collection of disaster impact information is an important tool for Governments and Institutions in charge of relief and recovery and for disaster risk analysis and reduction

4. Introduction • EM-DAT reference tool for global analysis of disaster occurrence and impact but: No analysis on disaster occurrence and impacts on a smaller, intra-country spatial scales • National and sub-national database initiatives exist but: Visibility, access, inter-operability, reliability are limited by methodological and operational approaches

5. Context of the Study • Supported by USAID/OFDA • Project Leader: CRED • Component of the Global Risk Identification Program, GRIP/UNDP activities « EnhancedLoss Data » • The collaboration aimsat sharing knowledgebetween national and sub-national disasterdatabases in order to improvevisibility, accessibility and applicability of databases • By providingtechnical support, the studyaimsatstrengtheningexistingmethodological and operationalapproaches of disasterdatabases

6. Goals and Objectives • Goal: • Strengthening disaster databases in the Asian region in order to improve information dissemination on disaster losses • Objectives: • Identify strengths and weaknesses of selected databases • Provide guidelines and technical support for reinforcing its quality • Establish recommendations to improve internal database coherence and inter-operability • Explore concrete research for joint analysis related to disasters in the country

7. Disaster Data Collection Initiatives, Asia Region • National and sub-national databases • Bangladesh: Disaster Incidence Database • China (P Rep): National DisasterDatabase • India: Orissa, Tamil Nadu, UttarPradeshDisasterDatabases • Indonesia: Disaster Data and Information • Iran (Islam Rep): DisasterDatabase • Maldives: DisasterDatabase • Nepal: DisasterInventory, Information Management System • Philippines: CalamidatDatabase • Sri Lanka: Disaster Information System • Thailand: National DisasterDatabase • Vietnam: Damage and NeedsAssessment System • Regionaldatabases • ADRC • OSADI As stated in the report « TSCHOEGL L., with BELOW R., GUHA-SAPIR D. (2006). An analytical review of selected data sets on natural disasters and impacts. Paper prepared for the UNDP/CRED Workshop on improving Compilation of Reliable Data on Disaster Occurrence and Impact, Bangkok, 2-4 April 2006

8. National and sub-National Disaster Data Collection Initiatives, Asia Region • DesInventar Model • India: Orissa, Tamil Nadu, Uttar Pradesh Disaster Databases • Indonesia: Disaster Data and Information • Iran (Islam Rep): Disaster Database • Maldives: Disaster Database • Nepal: Disaster Inventory, Information Management System • Sri Lanka: Disaster Information System • Thailand: National Disaster Database • Other Model • Bangladesh: Disaster Incidence Database • China (P Rep): National Disaster Database • Philippines: Calamidat Database • Vietnam: Damage and Needs Assessment System • Thailand: National Disaster Database

9. Benefits and gains Elaborate the database to capture in most efficient and effective way disaster impacts in the country i.e. improve establishment of high-risk disaster areas Reinforce data quality in order to build analytical capacity i.e. publications, optimalization of data and tools for annual statistical review on disaster impact Support budgetary requests from national budget i.e. evidence-base for early-warning systems

10. Collaboration Activities

11. Collaborative activities (1) Identification of strengths and weaknesses of the database, with focus on opportunities to reinforce the database : Discuss the methodological and operational procedures and constraints of the database by interviewingthe different staff members involved in the disaster database management and data handling Selected data set analysis Compare data from EM-DAT and (sub) national database for a limited geographical area and time span by comparative data analysis (CRED) Explore concrete areas to collaborate on joint analyses related to disasters in country Goals: Knowledge sharing and applicability

12. Collaborative activities (2) 4. On site and remote technical assistance by CRED for the implementation of recommendations On-site and at distance Goals: strengthening methodological and operational approaches “General Guidelines for the Development of Disaster Databases and the Compilation of Reliable Data” – manuscript Reporting by CRED Goals: Strengthening methodological and operational approaches applicability 6. Joint meeting with all concerned institutes Organized by CRED and GRIP/UNDP, to discuss and evaluate experiences and progress in the database strengthening collaboration Goals: Visibility, knowledge sharing, interoperability

13. Methodologies

14. Quality Assessment Process • Identification of disaster databases initiatives in the Asian region • Development of a Quality Framework and Questionnaire • Pilot-study (MünichRe and Vietnam) • On-site visits in 7 countries: questionnaire • Quantitative and qualitative comparison exercise • Identification of collaborative areas • Reporting to the country • Establishment of recommandations and guidelines

15. Objectives and Methods of Interviewing • Qualitative study: • Very limited number of samples, in depth knowledge sought-after, technical assistance per case, • Explorative study, cyclical-interactif process of collection and analysis • Objectives: 1. Discover theoretical methodology and hindering factors (e.g. choice of entry level) 2. Discover practical operational functioning and hindering factors (e.g. number of staff) 3. Discover level of standardization and interoperability (e.g. use of common standards and definitions) 4. Assess accuracy and reliability of data (e.g. completeness of data)

16. Quality assessment process Whatisquality? • The quality of something depends on a set of inherent characteristics and a set of requirements and how well the former complies with the latter (ISO 9000) • Quality is the totality of features and characteristics of a product or service that bears on its ability to satisfy given needs (ANSI/ASQ) • Whatis data quality? • Consistently meeting all knowledge worker and end-customer expectations in all quality characteristics of the information products and services required to accomplish the enterprise mission (internal knowledge worker) or personal objectives (end customer) (IAIDQ) • Must be seen in context of the objectives of the database • Must be directed to the users’ needs

17. Quality Framework (1)

18. Quality Framework (2) Based on: • CRED/GRIP Meeting report Database Quality Assessment/Quality Control Mechanisms, Munich Feb 29 2008 • International Monetary Fund (IMF) Data Quality Assessment Framework 2003, www.dsbb.imf.org/Applications/web/dqrs/dqrsdqaf/ • FAO Statistical Data Quality Framework: A multi-layered approach to monitoring and assessment. Conference on Data Quality for International Organizations, Wiesbaden, Germany, 2004 • World Bank Development Data Group and UNESCO Institute for Statistics. A framework for assessing the quality of education statistics. 2003 (www.uis.unesco.org/TEMPLATE/pdf/SCB/DQAF%20for%20education%20statistics.pdf) • Wang R.Y. and Strong D.M. Beyond accuracy: What data quality means to data consumers, Journal of Management Information Systems 12 (4), pp 5-34, 1996

19. Quality Framework (3)

20. Databases selected and visited • DesInventar Model India: Orissa, Tamil Nadu, UttarPradeshDisasterDatabases Indonesia: Disaster Data and Information Iran (Islam Rep): DisasterDatabase Maldives: DisasterDatabase Nepal: DisasterInventory, Information Management System Sri Lanka: Disaster Information System • Other Model Bangladesh: Disaster Incidence Database China (P Rep): National DisasterDatabase Philippines: CalamidatDatabase Thailand: National DisasterDatabase Vietnam: Damage and NeedsAssessment System

21. Comparative study: Objective and Methods Comparing selected datasets (EM-DAT and National/Sub-National database), in order to study: Similarities and differences between databases structures and methodology Coverage of information Correlation Methods: Generation of cell matrix where disaster records are cross-referenced Take into account the different architecture and methodology of each dataset: 1 single record (EM-DAT)/multiple records in sub-national database Criteria of entering disaster events in the database Identification of common variables

22. Methodology Selection of datasets Time period (2000-2008) Restricted to natural disasters No limitation in geographical area (national level) Comparison of contents of data elements and definitions Selection of variables (data, disaster type, deaths, affected, economic losses) Analysis of scope and completeness of information Comparison of the level of agreement of data from both datasets Interoperability

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