Lecture

Knowledge Management: Disaster Risk Reduction

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Description

This lecture explores knowledge management in the context of disaster risk reduction (DRR) and the role of science and technology. It covers the definition of knowledge management, examples of tools, education, evaluation processes, and the importance of technology adaptation. The instructor emphasizes the Sendai Framework for DRR, the cognitive pyramid of knowledge management, and the IKM4DRR Scorecard. Additionally, it discusses the integration of DRR in formal and informal education, using various tools like puppet shows, theater, and games. Evaluation methods, such as context analysis and big data, are also highlighted, showing how science and technology support knowledge management in DRR.

In MOOC

A Resilient Future: Science and Technology for Disaster Risk Reduction

This course aims to introduce participants to existing and emerging technologies suitable for disaster risk reduction while promoting the overall aim of sustainable development. The course focuses on three main natural hazards- floods, landslides and earthquakes. It also discusses the challenges and limits of adapting and adopting technologies depending on context (Global North and Global South) with examples from Switzerland, Nepal, Colombia, Philippines and other countries.

Official source

Related concepts (23)

Disaster risk reduction

Disaster risk reduction (DRR) sometimes called disaster risk management (DRM) is a systematic approach to identifying, assessing and reducing the risks of disaster. It aims to reduce socio-economic vulnerabilities to disaster as well as dealing with the environmental and other hazards that trigger them.

Evaluation strategy

In a programming language, an evaluation strategy is a set of rules for evaluating expressions. The term is often used to refer to the more specific notion of a parameter-passing strategy that defines the kind of value that is passed to the function for each parameter (the binding strategy) and whether to evaluate the parameters of a function call, and if so in what order (the evaluation order). The notion of reduction strategy is distinct, although some authors conflate the two terms and the definition of each term is not widely agreed upon.

Lazy evaluation

In programming language theory, lazy evaluation, or call-by-need, is an evaluation strategy which delays the evaluation of an expression until its value is needed (non-strict evaluation) and which also avoids repeated evaluations (by the use of sharing). The benefits of lazy evaluation include: The ability to define control flow (structures) as abstractions instead of primitives. The ability to define potentially infinite data structures. This allows for more straightforward implementation of some algorithms.

Data and information visualization

Data and information visualization (data viz or info viz) is the practice of designing and creating easy-to-communicate and easy-to-understand graphic or visual representations of a large amount of complex quantitative and qualitative data and information with the help of static, dynamic or interactive visual items.

Scientific visualization

Scientific visualization (also spelled scientific visualisation) is an interdisciplinary branch of science concerned with the visualization of scientific phenomena. It is also considered a subset of computer graphics, a branch of computer science. The purpose of scientific visualization is to graphically illustrate scientific data to enable scientists to understand, illustrate, and glean insight from their data.

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