During an All Saints’ Day mass in Lisbon in that fateful year, an 8.5-magnitude earthquake collapsed cathedrals, triggered a 20-foot tsunami, and sparked devastating fires that destroyed nearly 70% of the city’s 23,000 buildings.
The death toll was estimated between 10,000-50,000, leaving the center of a global empire in ruins, with losses equivalent to 32%-48% of Portugal's GDP at the time.
Never in the European history had a natural disaster received such international attention.
The “Great Lisbon Earthquake” had a resounding impact across Europe: Depictions of the earthquake in art and literature – the equivalent of today’s mass media – were reproduced for centuries and across several countries. Rousseau, influenced by the devastation, argued against large and dense cities in the wake of the disaster, while Immanuel Kant published three separate texts on the disaster, becoming one of the first thinkers to attempt to explain earthquakes by natural, rather than supernatural, causes.
In the years to follow, careful studies of the event would give rise to modern seismology.
Global positioning systems (GPS), real time traffic maps, accurate weather forecasts, Uber, self-driving cars…
“Geospatial,” or location-based data has existed for hundreds of years – for example, in street and topographical maps. What’s different is how quickly new information is being gathered and the more sophisticated analytics that is being applied to it, thanks to technological advances.
What was once information only found in the domain of government, military, and select private sector, even up to the 1980s and 90s, has come into broad use over the last 20 years.
This summer, some tens of millions of people in the U.S. traveled to see the total solar eclipse, including a co-author of this blog. Not only was the eclipse amazing – but the drive back from Tennessee to Washington, D.C. showed the integration and impact of geospatial information in our daily lives.
Urbanization has been one of the most significant driving forces of recent global development, with more than half the world’s population now living in cities. And this proportion will continue to rise. Add to this, the United Nation’s Sustainable Development Goal 11 that calls for “inclusive, safe, resilient and sustainable” cities.
In this edition of the Sustainable Communities Blog, Ede Ijjasz-Vasquez (@Ede_WBG), Senior Director of the World Bank’s Social, Urban, Rural and Resilience Global Practice, sat down with Dr. Shazia Siddiqi, Executive Director of Deaf Abused Women’s Network (DAWN), for
DAWN is a non-profit organization servicing the Washington, D.C., area with a mission to promote healthy relationships and end abuse in the Deaf community through providing survivors of abuse the help they need to heal and progress with lives, and through community education on how to foster positive relationships.
This wide-ranging discussion touches on several key issues that are crucial for sustainable and inclusive development and important for breaking down barriers of exclusion. Particularly given the prevalence of persons with disabilities moving to cities, the topics include how to incorporate disability inclusive technology into smart city planning, disaster risk management (DRM), and attitudes that enhance the dignity of persons with disabilities.
Much of this has to do with the lack of adequate infrastructure that can defend against the impacts of floods, sea level rise, landslides or earthquakes. . But even when cities know what it takes to become more resilient, most often they do not have access to the necessary funding to realize this vision.
It is estimated that worldwide, investments of more than $4 trillion per year in urban infrastructure will be needed merely to keep pace with expected economic growth, and an additional $1 trillion will be needed to make this urban infrastructure climate resilient. It is clear that the public sector alone, including development finance institutions like the World Bank, will not be able to generate these amounts—not by a long stretch.
The recent series of devastating hurricanes in the Caribbean has reminded the world, once again, that natural disasters are not equal-opportunity destroyers. The economically marginalized and those lacking secure land and property rights are often disproportionately affected for at least three reasons:
Imagine that you are an advisor to your country's Minister of Education. A recent earthquake damaged hundreds of schools in several cities. The minister has called for a meeting with you and asked: What can be done to prevent similar damages in the future?
So… What would you advise?
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.
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.
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.
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.
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.
The question remains:
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.