Bringing Geoss Services Into Practice For Beginners Geonode Tutorial

This tutorial complements the “Bringing GEOSS services into practice” workshop available at http://www.geossintopractice.org.

The "Bringing" GEOSS services into practice" workshop aims at teaching participants how to install, configure and deploy a set of open source software to publish and share data and metadata through GEOSS using OGC and ISO standards.

Geocomputation with R is for people who want to analyze, visualize and model geographic data with open source software. It is based on R, a statistical programming language that has powerful data processing, visualization, and geospatial capabilities. The book equips you with the knowledge and skills to tackle a wide range of issues manifested in geographic data, including those with scientific, societal, and environmental implications. This book will interest people from many backgrounds, especially Geographic Information Systems (GIS) users interested in applying their domain-specific knowledge in a powerful open source language for data science, and R users interested in extending their skills to handle spatial data. The book is divided into three parts: (I) Foundations, aimed at getting you up-to-speed with geographic data in R, (II) extensions, which covers advanced techniques, and (III) applications to real-world problems. The chapters cover progressively more advanced topics, with early chapters providing strong foundations on which the later chapters build. Part I describes the nature of spatial datasets in R and methods for manipulating them. It also covers geographic data import/export and transforming coordinate reference systems. Part II represents methods that build on these foundations. It covers advanced map making (including web mapping), "bridges" to GIS, sharing reproducible code, and how to do cross-validation in the presence of spatial autocorrelation. Part III applies the knowledge gained to tackle real-world problems, including representing and modeling transport systems, finding optimal locations for stores or services, and ecological modeling. Exercises at the end of each chapter give you the skills needed to tackle a range of geospatial problems. Solutions for each chapter and supplementary materials providing extended examples are available at https://geocompr.github.io/geocompkg/articles/. Dr. Robin Lovelace is a University Academic Fellow at the University of Leeds, where he has taught R for geographic research over many years, with a focus on transport systems. Dr. Jakub Nowosad is an Assistant Professor in the Department of Geoinformation at the Adam Mickiewicz University in Poznan, where his focus is on the analysis of large datasets to understand environmental processes. Dr. Jannes Muenchow is a Postdoctoral Researcher in the GIScience Department at the University of Jena, where he develops and teaches a range of geographic methods, with a focus on ecological modeling, statistical geocomputing, and predictive mapping. All three are active developers and work on a number of R packages, including stplanr, sabre, and RQGIS.

This handbook is a resource for enhancing disaster resilience in urban areas. It summarizes the guiding principles, tools, and practices in key economic sectors that can facilitate incorporation of resilience concepts into decisions about infrastructure investments and urban management that are integral to reducing disaster and climate risks.

Using Ghana as a case study, this work integrates economic and political analysis to explore the challenges and opportunities of Africa's growth and transformation.

'Economic losses from natural disasters totaled $92 billion in 2015.' Such statements, all too commonplace, assess the severity of disasters by no other measure than the damage inflicted on buildings, infrastructure, and agricultural production. But $1 in losses does not mean the same thing to a rich person that it does to a poor person; the gravity of a $92 billion loss depends on who experiences it. By focusing on aggregate losses—the traditional approach to disaster risk—we restrict our consideration to how disasters affect those wealthy enough to have assets to lose in the first place, and largely ignore the plight of poor people. This report moves beyond asset and production losses and shifts its attention to how natural disasters affect people’s well-being. Disasters are far greater threats to well-being than traditional estimates suggest. This approach provides a more nuanced view of natural disasters than usual reporting, and a perspective that takes fuller account of poor people’s vulnerabilities. Poor people suffer only a fraction of economic losses caused by disasters, but they bear the brunt of their consequences. Understanding the disproportionate vulnerability of poor people also makes the case for setting new intervention priorities to lessen the impact of natural disasters on the world’s poor, such as expanding financial inclusion, disaster risk and health insurance, social protection and adaptive safety nets, contingent finance and reserve funds, and universal access to early warning systems. Efforts to reduce disaster risk and poverty go hand in hand. Because disasters impoverish so many, disaster risk management is inseparable from poverty reduction policy, and vice versa. As climate change magnifies natural hazards, and because protection infrastructure alone cannot eliminate risk, a more resilient population has never been more critical to breaking the cycle of disaster-induced poverty.

Recent years have witnessed considerable speculation about the potential of open data to bring about wide-scale transformation. The bulk of existing evidence about the impact of open data, however, focuses on high-income countries. Much less is known about open data’s role and value in low- and middle-income countries, and more generally about its possible contributions to economic and social development. Open Data for Developing Economies features in-depth case studies on how open data is having an impact across the developing world-from an agriculture initiative in Colombia to data-driven healthcare projects in Uganda and South Africa to crisis response in Nepal. The analysis built on these case studies aims to create actionable intelligence regarding: (a) the conditions under which open data is most (and least) effective in development, presented in the form of a Periodic Table of Open Data; (b) strategies to maximize the positive contributions of open data to development; and (c) the means for limiting open data’s harms on developing countries.