Lecture

Data Preparedness: Improving Response through Information Management

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Description

This lecture discusses the importance of data preparedness in disaster response, emphasizing the need for accurate and reliable data collection before disasters. It explores identifying information needs, establishing standards for data collection, and building local capacity. The lecture also addresses information gaps and overloads faced by different groups involved in disaster response, highlighting the challenges of heterogeneous data sources and outdated information. Strategies for addressing these challenges include leveraging science and technology for data visualization, integration, and utilizing mobile technologies for data collection in developing countries.

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.

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Related concepts (32)

Data integration

Data integration involves combining data residing in different sources and providing users with a unified view of them. This process becomes significant in a variety of situations, which include both commercial (such as when two similar companies need to merge their databases) and scientific (combining research results from different bioinformatics repositories, for example) domains. Data integration appears with increasing frequency as the volume (that is, big data) and the need to share existing data explodes.

Data science

Data science is an interdisciplinary academic field that uses statistics, scientific computing, scientific methods, processes, algorithms and systems to extract or extrapolate knowledge and insights from noisy, structured, and unstructured data. Data science also integrates domain knowledge from the underlying application domain (e.g., natural sciences, information technology, and medicine). Data science is multifaceted and can be described as a science, a research paradigm, a research method, a discipline, a workflow, and a profession.

Data

In common usage and statistics, data (USˈdætə; UKˈdeɪtə) is a collection of discrete or continuous values that convey information, describing the quantity, quality, fact, statistics, other basic units of meaning, or simply sequences of symbols that may be further interpreted formally. A datum is an individual value in a collection of data. Data is usually organized into structures such as tables that provide additional context and meaning, and which may themselves be used as data in larger structures.

Data collection

Data collection or data gathering is the process of gathering and measuring information on targeted variables in an established system, which then enables one to answer relevant questions and evaluate outcomes. Data collection is a research component in all study fields, including physical and social sciences, humanities, and business. While methods vary by discipline, the emphasis on ensuring accurate and honest collection remains the same.

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.

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