Machine learning algorithms are excellent at answering “yes” or “no” questions. For example, they can scan huge datasets and correctly tell us: Does this credit card transaction look fraudulent? Is there a cat in this photo?
But it’s not only the simple questions – they can also tackle nuanced and complex questions.
Today, machine learning algorithms can detect over 100 types of cancerous tumors more reliably than a trained human eye. Given this impressive accuracy, we started to wonder: what could machine learning tell us about where people live? In cities that are expanding at breathtaking rates and are at risk from natural disasters, could it warn us that a family’s wall might collapse during an earthquake or rooftop blow away during a hurricane?
At the Annual Meetings of the World Bank Group and International Monetary Fund in Bali, Indonesia, the World Bank highlighted the importance of human capital for economic development.
Central to the World Bank’s motivation for the Human Capital Project is evidence that investments in education and health produce better-educated and healthier individuals, as well as faster economic growth and a range of benefits to society more broadly. As part of this effort to accelerate more and better investments in people, the new Human Capital Index provides information on productivity-related human capital outcomes, seeking to answer how much human capital a child born today will acquire by the end of secondary school, given the risks to poor health and education that prevail in the country where she or he was born.
Water and sanitation data figures in Guatemala show a challenging reality. Nationally, 91 percent of the population has access to improved drinking water, an increase of 14 percent points since the establishment of the MDGs.
Despite the improvement in coverage in relative terms, in absolute terms there are still a significant number of Guatemalan households using water from precarious or unimproved sources such as unprotected wells, rivers, or lakes. In addition, water quality is a concern -- from the monitoring of 20% of the water systems in the country, 54% reported to be at high and imminent risk for human health.
It is well established in the economic literature that it’s the rich who benefit from the lion’s share of energy subsidies. Yet, it is often the poor and vulnerable who protest loudly against these reforms. Why does this happen? What are we missing?
- Latin America & Caribbean
- Venezuela, Republica Bolivariana de
- Trinidad and Tobago
- St. Vincent and the Grenadines
- St. Pierre & Miquelon
- St. Lucia
- St. Kitts and Nevis
- Puerto Rico
- French Guiana
- El Salvador
- Dominican Republic
- Costa Rica
- Bahamas, The
- Antigua and Barbuda
Trading across borders in Central America has been a severe problem for many years. In 2017, cargo trucks used to spend 10 hours to travel less than one kilometer across the borders between Guatemala and Honduras. Such delays at border crossings made trade throughout the region slow and expensive.
Four years ago, Juan Angel Sandoval, a resident of Barrio Buenos Aires in the Honduran municipality of Siguatepeque, received water at home only three times a week. His was not an isolated reality. Most of his neighbors, were in the same situation. "It was annoying because the water was not enough," says Juan Angel.
By Liliana D. Sousa
It might be surprising, but the majority of Central American households receive electricity subsidies, benefiting up to 8 out of 10 households in some cases. Without a doubt, this provides many poor and low-income families with access to affordable electricity.
Some months ago, during a visit to one of the Central American countries, while we were on a call with the head of the electricity dispatch center, we noticed by the tone of his voice, that he was becoming nervous. Shortly after, background voices could be heard on the line. They were experiencing a crisis and he quickly asked to continue our conversation at another time.
By 2030, 80% of the world’s population will be living in urban areas, following the dream of better jobs, education, and health care.
Too often, however, that dream risks remaining an urban daydream, due to natural disasters such as hurricanes, earthquakes, and floods, as well as climate change. Those of us working to help these families find a better future must focus more on ways to support efforts to protect their lives – and their livelihoods.
In the 40 years since the launch of Habitat I, governments and municipalities throughout emerging and developing countries have been proving that their cities can be not only inclusive and secure, but also resilient and sustainable. However, unless they increase their speed and scale, they are unlikely to achieve the goals of the “New Urban Agenda” and its Regional Plans, launched at Habitat III in 2016.
From our perspective helping governments in Latin America and the Caribbean, and ahead of the World Urban Forum taking place in Kuala Lumpur, Malaysia in February, let us share three key ingredients necessary to achieve that goal:
- UN Habitat
- City Resilience Program
- Global Goals
- natural disasters
- disaster risk management
- World Urban Forum
- New Urban Agenda
- Habitat III
- Sustainable Communities
- Social Development
- Urban Development
- Climate Change
- Latin America & Caribbean
- East Asia and Pacific
Can we rely only on satellite? How accurate are these results?
It is standard practice in classification studies (particularly academic ones) to assess accuracy from behind a computer. Analysts traditionally pick a random selection of points and visually inspect the classified output with the raw imagery. However, these maps are meant to be left in the hands of local governments, and not published in academic journals.
So, it’s important to learn how well the resulting maps reflect the reality on the ground.
Having used the algorithm to classify land cover in 10 secondary cities in Central America, we were determined to learn if the buildings identified by the algorithm were in fact ‘industrial’ or ‘residential’. So the team packed their bags for San Isidro, Costa Rica and Santa Ana, El Salvador.
Upon arrival, each city was divided up into 100x100 meter blocks. Focusing primarily on the built-up environment, roughly 50 of those blocks were picked for validation. The image below shows the city of San Isidro with a 2km buffer circling around its central business district. The black boxes represent the validation sites the team visited.
|Land Cover validation: A sample of 100m blocks that were picked to visit in San Isidro, Costa Rica. At each site, the semi-automated land cover classification map was compared to what the team observed on the ground using laptops and the Waypoint mobile app (available for Android and iOS).|