Another year has passed, and we are only 11 years away from the goalpost of the 2030 Agenda for Sustainable Development (Agenda 2030).
In the past few years, knowledge sharing has moved to the center of global development as a third pillar complementing financial and technical assistance. Agenda 2030 calls for enhancing “knowledge sharing on mutually agreed terms,” while the Addis Ababa Action Agenda on Financing for Development encourages knowledge sharing in sectors contributing to the achievement of the SDGs.
For cities, this means that
The solid waste management sector offers opportunities for private entrepreneurship, resource conservation, and inclusiveness for marginalized populations; however, it also presents significant challenges in terms of technical, financial, and institutional capacities.
This is a highly fertile, verdant place… You're at the foot of a volcano.
Globally, around 68.5 million people have fled their homes from conflict or persecution either as refugees, internally displaced persons, or asylum seekers. Contrary to what some may think, most of the displaced people don’t live in camps. In fact, it’s estimated that about 60%–80% of the world’s forcibly displaced population lives in urban areas.
The “urban story” of forced displacement is often compounded by its hidden nature. Compared to those displaced in camps, it is more difficult to track the living conditions of those displaced in urban areas, obtain precise numbers, and many are not recipients of humanitarian assistance.
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?
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.
How do we build inclusive cities for all?
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.
Across the globe, more than 20 million children from conflict-affected countries are out of school.
Take Syrian refugees in Turkey, the country that hosts more individuals fleeing from armed conflict than any other in the world.
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?