How would you define the area of Indonesia’s capital city, Jakarta?
a: Simply using the administrative boundaries of the Special Capital Region of Jakarta?
b: Based on the extent and density of population?
c: Using nighttime lights data?
d: Or, what about a definition based on commuting flows as used in the U.S. approach to defining metropolitan statistical areas?
Globally, a growing number of cities spill across their administrative boundaries, meaning that many urban issues now need to be addressed at a metropolitan level. However, to do this, it is first necessary to delineate the “true” extent of a metro area. How else, after all, will policymakers be able to identify which local governments need to work together to provide transport and other essential public services?
For example, a Cairo-based startup called “Swvl” is disrupting commuting in the In the Middle East and North Africa region by mapping out commuters’ travel directions and enabling app-based, affordable bus rides that can compete with on-demand ride-hailing.
This is a question that cities around the world are trying to answer, as the 2030 Agenda for Sustainable Development advances disability-inclusive development – and makes a strong case for more sector-specific programming that is inclusive of persons with disabilities and leaves no one behind.
New York City is leading by example to ensure that the voices of persons with disabilities are represented.
At one point, it was considered one of the most dangerous cities in the world. From 1990 to 1993, more than 6,000 people were murdered annually. Drive-by shootings were regular and indiscriminate, stemming from warfare between gang lords, drug criminals, and para-military groups. The need for change was urgent and led to radical urban experimentation.
The city’s political and business leaders recognized that Medellín’s security issues could not be dealt with through policy measures alone. They initiated a series of radical programs to reshape the social fabric of the city’s neighborhoods and to mobilize the poor.
City planners began addressing the problem of endemic violence and inequity through the design of public spaces, transit infrastructure and urban interventions into marginalized neighborhoods. Key to their approach was a commitment to making the public realm a truly shared space, and a faith that they could transform Medellín’s public spaces from sites of segregation and warfare into spaces where communities would come together.
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?
That’s how long recent estimates suggest it would take in some developing countries to legally register all land – due to the limited number of land surveyors in country and the use of outdated, cumbersome, costly, and overly regulated surveying and registration procedures.
But I am convinced that the target of registering all land can be achieved – faster and cheaper. This is an urgent need in Africa where less than 10% of all land is surveyed and registered, as this impacts securing land tenure rights for both women and men – a move that can have a greater effect on household income, food security, and equity.
Perhaps one of our answers can be found in rural Tanzania where I recently witnessed the use of a mobile surveying and registration application. In several villages, USAID and the government of Tanzania are piloting the use of the Mobile Application to Secure Tenure (MAST), one of several (open-source) applications available on the market. DFID, SIDA, and DANIDA are supporting a similar project.
As one of the most urbanized African countries and the largest economy in Southern Africa, South Africa is a popular and important destination for migrants and refugees from all over Africa, and increasingly, from parts of Asia.
There is a unique space where you can encounter everyone from developers of self-driving cars in Silicon Valley to city planners in Niamey to humanitarian workers in Kathmandu Valley: the global OpenStreetMap (OSM) community. It comprises a geographically and experientially diverse network of people who contribute to OSM, a free and editable map of the world that is often called the “Wikipedia of maps.”
What is perhaps most special about this community is its level playing field. Anyone passionate about collaborative mapping can have a voice from anywhere in the world. In the past few years, there has been a meteoric rise of locally organized mapping communities in developing countries working to improve the map in service of sustainable development activities.
The next opportunity to see the OSM community in action will be the November 14th mapathon hosted by the Global Facility for Disaster Reduction and Recovery (GFDRR)’s Open Data for Resilience Initiative (OpenDRI). Mapathons bring together volunteers to improve the maps of some of the world’s most vulnerable areas, not only easing the way for emergency responders when disaster strikes, but also helping cities and communities plan and build more resiliently for the future.