In search of answers, we spoke with the leaders of the World Bank’s Global Program for Safer Schools (GPSS), who have recently launched an innovative tool, the Roadmap for Safer Schools. This roadmap is a guide to design and implement systematic actions to improve the safety and resilience of school infrastructure at risk from natural hazards.
When I used to work in Rwanda, I lived on a small street in Kigali. Every time I invited friends over, I would tell them to “walk past the Embassy, look out for the Church, and then continue to the house with the black gate.” The day a street sign was erected on my street was a game changer.
So how do more than two million citizens of Accra navigate the busy city without the help of street names? While some street names are commonly known, most streets do not have any official name, street sign or house number. Instead, people usually refer to palm trees, speed bumps, street vendors, etc.
But, what happens when the palm tree is cut or when the street vendor changes the location?
The absence of street names poses not only challenges for orientation, but also for property tax collection, postal services, emergency services, and the private sector. Especially, new economy companies, such as Amazon or Uber, depend on street addressing systems and are eager to cater to market demands of a growing middle class.
To address these challenges, the Accra Metropolitan Assembly (AMA), financed by the World Bank’s second Land Administration Project , is implementing a street addressing and property numbering system in Accra. Other Metropolitan areas received funding from other World Bank-funded projects for similar purposes.
Climate change is a global challenge that threatens the prosperity and wellbeing of future generations. Transport plays a significant role in that phenomenon. In 2013, the sector accounted for 23% of energy-related carbon emissions… that amounts to some 7.3 GT of CO2, 3 GT of which originate from developing countries. Without any action, transport emissions from the developing world will almost triple to reach just under 9 GT of CO2 by 2050.
Children are often told that home is where to run inside when thunders hit or when the rain comes, and that home is a safe place. However, for billions of people in the world, it is not.
By 2030, it is estimated that 3 billion people will be at risk of losing a loved one or their homes—usually their most important assets—to natural disasters. In fact, the population living on flood plains or cyclone-prone coastlines is growing twice as faster as the population in safe homes in safer areas.
Due to climate change, extreme weather and other natural hazard events hit these populations harder and more often. The 10 natural disasters causing the most property damages and losses in history have occurred since 2005. The damages and losses were highly concentrated in the housing sector. While the poor experience 11% of total of asset losses, they suffer 47% of all the well-being losses. Worse, natural disasters can lead to unnecessary losses of life, with earthquakes alone causing 44,585 deaths on average per year. This is an issue that policymakers and mayors need to address if they don’t want their achievements in poverty reduction to be erased by the next hurricane or earthquake.
The concentration of population in cities and their exposure to seismic hazards constitute one of the greatest disaster risks facing Peru and Ecuador. In 2007, a magnitude 8.0 earthquake along the southern coast of Peru claimed the lives of 520 people and destroyed countless buildings. The most recent earthquake in Ecuador, in 2016, left more than 200 dead and many others injured.
Of course, these risks are not exclusive to Latin America. Considered one of the most earthquake-prone countries in the world, Japan has developed unparalleled experience in seismic resilience. The transport sector has been an integral part of the way the country manages earthquake risk— which makes perfect sense when you consider the potential consequences of a seismic event on transport infrastructure, operations, and passenger safety.
When you think of Peru, the first city that usually comes to mind is Lima. Why? Well, because Lima is the largest city in the country, with close to 50% of the nation’s urban population living in the metropolitan area; the city also produces 45% of Peru’s GDP. While this level of concentration of population and economic activity may not be a good or bad thing, it points to some imbalances in the urban system in Peru.
In the previous blog, we discussed how remote sensing techniques could be used to map and inform policymaking in secondary cities, with a practical application in 10 Central American cities. In this post, we dive deeper into the caveats and considerations when replicating these data and methods in their cities.
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).
The buzz around satellite imagery over the past few years has grown increasingly loud. Google Earth, drones, and microsatellites have grabbed headlines and slashed price tags. Urban planners are increasingly turning to remotely sensed data to better understand their city.
But just because we now have access to a wealth of high resolution images of a city does not mean we suddenly have insight into how that city functions.
In an effort a few years ago to map slums, the World Bank adopted an algorithm to create land cover classification layers in large African cities using very high resolution imagery (50cm). Building on the results and lessons learned, the team saw an opportunity in applying these methods to secondary cities in Latin America & the Caribbean (LAC), where data availability challenges were deep and urbanization pressures large. Several Latin American countries including Argentina, Bolivia, Costa Rica, El Salvador, Guatemala, Honduras, Nicaragua, and Panama were faced with questions about the internal structure of secondary cities and had no data on hand to answer such questions.
A limited budget and a tight timeline pushed the team to assess the possibility of using lower resolution images compared to those that had been used for large African cities. Hence, the team embarked in the project to better understand the spatial layout of secondary cities by purchasing 1.5 meter SPOT6/7 imagery and using a semi-automated classification approach to determine what types of land cover could be successfully detected.
Originally developed by Graesser et al 2012 this approach trains (open source) algorithm to leverage both the spectral and texture elements of an image to identify such things as industrial parks, tightly packed small rooftops, vegetation, bare soil etc.
What do the maps look like? The figure below shows the results of a classification in Chinandega, Nicaragua. On the left hand side is the raw imagery and the resulting land cover map (i.e. classified layer) on the right. The land highlighted by purple shows the commercial and industrial buildings, while neighborhoods composed of smaller, possibly lower quality houses are shown in red, and neighborhoods with slightly larger more organized houses have been colored yellow. Lastly, vegetation is shown as green; bare soil, beige; and roads, gray.
Want to explore our maps? Download our data here. Click here for an interactive land cover map of La Ceiba.
Since the 1980s, investment in Brazil’s infrastructure has declined from 5% to a little above 2% of the country’s Gross Domestic Product (GDP), scarcely enough to cover depreciation and far below that of most middle-income countries (see figure below). The result is a substantial infrastructure gap. Over the same period, Brazil has struggled with stagnant productivity growth. The poor status of infrastructure is broadly believed to be a key reason for Brazil’s growth malaise.
The first time I heard of the Bolivian city of Trinidad was exactly 11 months ago. Although Trinidad is the 10th largest city in Bolivia, I confess I did not know much about it. The Ministry of Development Planning (MPD) had commissioned the World Bank a study on intermediate cities in Bolivia, and in my early research I learned that this was a colonial city founded in 1686 during Jesuitic Missions. Similar in its architecture and climate to the southeastern cities of my native Venezuela, Trinidad is extremely vulnerable to flooding that affect thousands of families and businesses each year.