1. Preface

In the year 869, the residents of Miyato-jima (Japan) felt a strong earthquake. Knowing that a tsunami might follow, many fled to the top of the nearest hill. Those who had fled in another direction watched in horror as the first wave from the sea combined with a second wave that came over the rice paddies, washing over the hill and pulling the helpless villagers out to sea. Those that survived that day constructed a monument on the hill, telling the story of how two tsunami waves fused together. For 1142 years, the shrine served as a warning future generations not to flee to the top of that particular hill. When the 11 March 2011 earthquake struck off the coast of Sendai, residents of Miyato-jima remembered the story and fled further inland, where they watched two tsunami waves crash over the shrine. (ref LATimes, Japan’s 1,000-year-old warning).

This story is an outlier—an example of collective memory reaching across 50 generations. In contrast, humans tend to remember how to plan for events that happen frequently with relatively low intensity, like seasonal flooding. Beyond isolated examples like the Miyato-jima shrine, there exists no catalogue of ancient disasters that provides sufficient data to model the periodicity or impact of infrequent events.

There are many cycles in deep history which are invisible to science, because many powerful cycles of nature are not measured in years but millennia. Volcano may erupt in 100, 1000, 10,000, 100,000 or 1,000,000 year cycles. Earthquakes may have similar periodicities. The historical record dates (at most) a few thousand years. Accurate scientific measurements of such low-frequency, high-intensity events are sparse and spotty until only the past few decades.

At the same time, the underlying causes of risk are changing. While the world is becoming more interconnected and interdependent, new challenges are making it less likely that wisdom received from previous generations will prudently inform decisions about the present and future (Suarez, 2013). From urbanization and climate change to population growth and technology, emerging risks are altering traditional approaches to disaster risk management. Among the large cities exposed to cyclones and earthquakes, the population will double from 2000 to 2050 (0.68 billion to 1.5 billion) (NHUD, 2012). At the same time, these cities will create more concrete and asphalt–making them less permeable and more liable to flood. Models of climate change estimate that damage from cyclones will increase 50-125% over the 21st century. Climate change is unfolding at an accelerating pace–a rate of change that will make prediction always behind. Such patterns of natural hazards require rethinking current understandings of assessing and managing risk.

Introduction to Understanding Risk and Risk Information

Understanding dynamic risks requires better information. Currently, many governments have very limited information about their exposure to natural hazards, and therefore cannot effectively manage their vulnerabilities. In many countries, base maps have not been updated in 50 or more years, the census may be more than 10 years old, and there may be little data to distinguish those buildings that have been built to withstand natural hazards and those that have not.

Without data, vulnerable societies cannot make different choices. They cannot prepare for expected failures or make reasonable (often low-cost) investments in mitigating risks. They cannot enforce building codes and zoning restrictions that create resilience in the face of expected adversity. They cannot compare their historical understandings of a risk (inherited with the wisdom of their society) with new understandings of a dynamic planet, where climate risks are changing historical understandings and opening them to hazards that they have not previously faced.

Understanding New Risks Requires New Approaches

Within this changing context, GFDRR is taking a new approach: It is rare for the leaders in these communities to have the data they need to see the invisible risks, hidden in history or behind concrete.

Data is not enough

That said, having data is not enough to create changes to behavior. To alter mental model of the risks across a population, data needs to be available to everyone, and knowledge of how to analyze and apply those data need to be widespread. Data need to be open–legally open, in terms of intellectual property licenses that permit them to be reused, repurposed, and redistributed without cost, as well as technically open, so that any software can open them, manipulate them, and save new analyses in open formats. More importantly, the data needs to be collected, analyzed, and curated by the people in the place facing the risks. Only through this process of having the data be available to all and curated by those who are potentially affected can behavior fully change.

Open Data for Resilience: Building Open Data Ecosystems around Risk Management

In 2011, the Global Facility for Disaster Reduction and Recovery created the Open Data for Resilience Initiative (OpenDRI) to help people in vulnerable regions better understand the historical and changing risks they face from natural hazards. OpenDRI is a partnership of governments and international institutions that are building a deeper understanding of risk by sharing information about their hazards, exposure, vulnerability, and risks.

This guide outlines practices that a network of peers is developing. All of the partners to this guide are working to build better data around the Disaster Risk Management (DRM) cycle. Science agencies are developing better models of earthquakes, cyclones, floods, droughts, tsunamis, volcanic eruptions, and other hazards. Governments are cataloguing the exposure of their built environments to those hazards, at the same time that they are exploring the vulnerability of their populations to the direct and indirect effects of disasters. In parallel, donors are looking at how best to help their clients target investments.

Who is this Guide for?

This guide is for those who wish to apply open data approaches to develop a deeper understanding of how to prepare, reduce, and transfer the potential risks from natural hazards. It is aimed as much to government officials and community leaders as it is to to international organizations, non-governmental organizations, and donors.

Goals of the Guide

This guide intends to:

1. Share disaster risk management activities that have harnessed or promoted open data
2. Explore shared challenges in using open data to increase resilience of societies who are facing risks from natural hazards
3. Start to grow consensus around how to implement open data initiatives inside of client governments
4. Articulate the workflows, partnerships, tools, and practices around OpenDRI
5. Collate shared documents that can be used as templates for future implementations of OpenDRI practices.

A Note on Style of this Guide

The guide is written as an assembly of concise descriptions of practices around OpenDRI. This approach allows for several dynamics:

1. Easy Reading
2. Simplified Translation
3. Multiple Layouts: A3, A4, A5, and web.
4. Easy reference in the form of a growing body of knowledge
5. Simpler version control around a “small pieces, loosely joined” model.

The guide also focuses on cataloguing the set of practices that have emerged over the management of open data, the curation of community maps, and the rise of practices around risk communication that illustrate the potential impact of natural hazards on a given place. Each of these practices is set forth as closed wiki: a module in a larger whole that authorized individuals can edit as they learn and improve upon the original idea of OpenDRI. Overall editorial decision making will remain with GFDRR until a governance structure can be established to perform this function.

Collective Intelligence around Open Data for Resilience

The intention of this guide is to make the work itself a collective effort. It is seeded from the experience of a handful of partners, and shared with communities that work in open data or hope to emulate this approach. Curators will continue to steward the content, ensuring that added knowledge reflects the community’s shared experiences.