In 1999, when a few enthusiasts agreed to meet annually in an effort to base interventions on land, on solid empirical evidence rather than ideology, few would have expected this effort to have such a lasting impact. Twenty years on, the small gathering has morphed into a conference, bringing together over 1,500 participants from governments, academics, civil society and the private sector to discuss the latest research and innovations in policies and good practice on land governance around the world.
The World Region
Photo: Courtesy of Safe Water Network
World Water Day is always a good time to take stock of where we are in achieving the water-related Sustainable Development Goals (SDGs). Most PPPs relate to relatively large investments in major infrastructure run by utilities. But in the developing world’s rapidly growing small towns and urban peripheries, we need something else.
Enter safe (also called small) water enterprises, an exciting group of dedicated social entrepreneurs who are beginning to gain traction providing high quality water to communities not served by utilities. For example, our friends at Safe Water Network recently announced they are now serving more than a million people in India and Ghana (more about that in this blog.) A 2017 report by Dalberg suggested a potential market of 3.9 billion people for safe water enterprises.
On the eve of International Women’s Day, I was at a UN WOMEN side event in NYC when my phone started buzzing with well wishes for a happy women’s day from my friends in Asia, filling me with — ambivalence. To be honest, the day always leaves me with mixed feelings: despite the great strides that the world has made in women’s rights in various ways, for me, it’s also a reminder of how so many women still don’t enjoy our basic human rights.
As we’ve returned from women’s day to what in many ways is still a man’s world, I wanted to share three thoughts about the intersection of women’s rights with our data world today.
Editor's note: This blog post is part of a series for the 'Bureaucracy Lab', a World Bank initiative to better understand the world's public officials.
It is a well-known, if unacceptable, fact that women globally earn significantly less than men for doing the same work. In the United States, women famously earn “79 cents to the dollar a man earns”, and similar disparities hold across developed and developing countries for wage labor (WDR, 2012).
To some, artificial intelligence is a mysterious term that sparks thoughts of robots and supercomputers. But the truth is machine learning algorithms and their applications, while potentially mathematically complex, are relatively simple to understand. Disaster risk management (DRM) and resilience professionals are, in fact, increasingly using machine learning algorithms to collect better data about risk and vulnerability, make more informed decisions, and, ultimately, save lives.
Artificial intelligence (AI) and machine learning (ML) are used synonymously, but there are broader implications to artificial intelligence than to machine learning. Artificial (General) Intelligence evokes images of Terminator-like dystopian futures, but in reality, what we have now and will have for a long time is simply computers learning from data in autonomous or semi-autonomous ways, in a process known as machine learning.
The Global Facility for Disaster Reduction and Recovery (GFDRR)’s Machine Learning for Disaster Risk Management Guidance Note clarifies and demystifies the confusion around concepts of machine learning and artificial intelligence. Some specific case-studies showing the applications of ML for DRM are illustrated and emphasized. The Guidance Note is useful across the board to a variety of stakeholders, ranging from disaster risk management practitioners in the field to risk data specialists to anyone else curious about this field of computer science.
Machine learning in the field
In one case study, drone and street-level imagery were fed to machine learning algorithms to automatically detect “soft-story” buildings or those most likely to collapse in an earthquake. The project was developed by the World Bank’s Geospatial Operations Support Team (GOST) in Guatemala City, and is just one of many applications where large amounts of data, processed with machine learning, can have very tangible and consequential impacts on saving lives and property in disasters.
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Over the past decade, the practice of disaster risk management (DRM) has evolved and matured. From mainly focusing on disaster response, local and international actors alike now emphasize the importance of preparedness and prevention – saving lives and avoiding losses even before disaster strikes.
Heavy rain and severe flooding brought the city of Colombo, Sri Lanka, to its knees. In China’s Yangtze River Basin, rivers spilled their banks, inundating towns and villages. In Mobile Bay, Alabama, strong ocean waves carried away valuable coastline.
In each of these locations, disasters caused by natural hazards seemed beyond human control. But instead of focusing only on building more drains, seawalls and dams, these governments turned to nature for protection from the disasters. Several years later, the urban wetlands, oyster reefs and flood plains they helped establish are now keeping their citizens safe while nourishing the local economies.
Energy commodity prices increased nearly 5 percent in February, led by oil (+8 percent), the World Bank’s Pink Sheet reported.
Non-energy prices gained 2 percent, in response to large price increases in metals and minerals.
Agricultural prices changed little, as increases in food and raw material prices (+0.5 percent each) were balanced by declines in beverages (-1.3 percent).
Fertilizer prices declined more than 2 percent, led by an 8 percent slide in DAP.