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Artificial intelligence for smart cities: insights from Ho Chi Minh City’s spatial development

Ran Goldblatt's picture
Zoning by Land Parcel (Source:

It’s amazing to see what technology can do these days! Satellites provide daily images of almost every location on earth, and computers can be trained to process massive amounts of data generated from them to produce insightful analysis/information. This is just one of the demonstrations of artificial intelligence (AI). AI can go beyond just reading images captured from space, it can help improve lives overall.

For urban governance, machine learning and AI are increasingly used to provide near real-time analysis of how cities change in practice – for example, through the conversion of green areas into built-up structures. By teaching computers what to look for in satellite images, rapidly expanding sources of satellite data (public and commercial), together with machine learning algorithms, can be leveraged to quickly reveal how actual city development aligns with planning and zoning or which communities are most prone to flooding. This provides insights beyond the basic satellite snapshots and time-lapse visualizations that can now be readily generated for any areas of interest.

But the barriers to applying these technologies can still seem daunting for many cities around the world. It’s not always clear how exactly to analyze this massive amount of satellite data, nor how to get access to it.

How many companies are run by women, and why does it matter?

Masako Hiraga's picture

Happy International Women’s Day! This is an important year to celebrate – from global politics to the Oscars last weekend, gender equality and inclusion are firmly on the agenda.

But outside movies and matters of government, we see the effects on gender equality every day, in how we live and work. One area we have data on comes from companies: what share of firms have a female CEO or top manager?

Only 1 in 5 firms worldwide have a female CEO or top manager, and it is more common among the smaller firms. While this does vary by around the world – Thailand and Cambodia are the only two countries where the data show more women running companies than men.

Better representation of women in business is important. It ensures a variety of views and ideas are represented, and when the top manager of a firm is woman, that firm is likely to have a larger share of permanent female workers.

What data do decision makers really use, and why?

Sharon Felzer's picture

When it comes to revolutions, the data revolution has certainly been less bloody than, say, those in the 18th and 19th centuries. Equally transformative? A question for historians.

AidData, a research and innovation lab located at the College of William & Mary in the US, set out in 2017, to identify what data decision makers in low and middle-income countries use, whose data they use, why they use it, and which data are most helpful.

What can the World Bank learn from AidData’s study, and do data from our own Country Opinion Survey Program, align with AidData’s findings?

Decoding data use: 3500 leaders in 126 low- and middle-income countries.

In 2017 nearly 3500 leaders responded to AidData’s Listening To Leaders Survey (LTL) to help uncover how, when, and why this audience uses information from a range of sources.

This rich data is featured in the report “Decoding Data Use: How do Leaders Source data and Use It To Accelerate Development” and can help any institution target important audiences. For example, what are CSOs and NGOs using most frequently, and for what purpose? How about government respondents? Development partners? The private sector? Does it differ region to region?

Here are some of the key findings:


  • Policymakers consult information from the World Bank more than other foreign/international organizations.
  • If you want opinion leaders in client countries to be aware of the Bank’s data and knowledge, bring it to their attention. If you expect them to find it through an internet search, you might be disappointed.
  • Opinion leaders are most likely to regard the knowledge and information helpful if it helps them better understand challenging policy issues and will help them develop implementation strategies in response.
  • Make sure the knowledge and information reflects the local context (be inclusive).
  • Stay focused on policy recommendations to ensure value.

Now let’s see how AidData’s findings compare with the Bank’s Country Opinion Survey Data.

First thing’s first: Accessing data

The AidData survey findings demonstrate that in the world of information and knowledge, decision makers around the world are accessing the Bank’s data.

No Risk, No Reward: The Statistics Netherlands Story

Haishan Fu's picture

Tjark Tjin-A-Tsoi is doing things differently. Before his appointment as the Director General for Statistics Netherlands in April 2014, he was the General Director of the Netherlands Forensic Institute. No doubt that’s why phrases like “actionable intelligence” and forensic analogies about “tracing data” pepper his vision for national statistics in the Netherlands. At a recent presentation here at the World Bank, Tjin-A-Tsoi shared his thoughts on what a modern statistics office looks like, how cognitive science informs data communications, and whether big data will render official statistics obsolete.

A new approach to official statistics

Almost four years after Tjin-A-Tsoi took the helm, Statistics Netherlands has been transformed. It has its own newsroom, a team of media professionals, and employs the latest cognitive science research in its quest to deliver statistical truths to the public. It recently opened a shining new Center for Big Data Statistics, and has an innovation portal for beta products which invites public feedback. One of their current beta products is a Happiness Meter, an interactive infographic that people in the Netherlands can use to calculate and compare their personal happiness score with the rest of the Dutch population.

Surgical care – an overlooked entity in health systems

Emi Suzuki's picture
Also available in: Français | Español | العربية

Five billion peopletwo thirds of world populationlack access to safe and affordable surgical, anesthesia and obstetric (SAO) care while a third of the global burden of disease requires surgical and/or anesthesia decision-making or treatment. Treating the sick very often requires surgery and anesthesia. Despite such huge burden of disease, safe and affordable SAO care is often overlooked.

Why? It may be because surgery and anesthesia are not disease entities. They are treatment modalities that address the breadth of human disease — infections, non-communicable, maternal, child, geriatric and trauma-related disease and injuries, and international development agencies have been focusing on vertical disease-based programs.

Prior to 2015, global data on surgery, anesthesia and obstetric care was virtually nonexistent. With the idea that “We can’t manage what we don’t measure”, the Lancet Commission on Global Surgery developed six Surgical, Obstetric and Anesthesia (SAO) indicators (discussed here) and collected data for them. The analysis of these data show large gaps in SAO care across countries by income groups.

There are 70-times as many surgical workers per 100,000 people in high-income countries compared with low-income countries

The SAO or “surgical” workforce is extremely small in low-income countries (1 SAOs per 100,000 population) and lower middle-income countries (10 SAOs per 100,000 population) whereas there are 69 SAOs per 100,000 population in high-income countries. The discrepancy between high-income countries and low- and middle-income countries is even greater for surgical workforce density than that of physician density.

Measuring surgical systems worldwide: an update

Parisa Kamali's picture
Photo: Chhor Sokunthea / World Bank

Five billion people—two thirds of world population—lack access to safe and affordable surgical, obstetric and anesthesia care with low and middle income countries (LMICs) taking a lead.1-3 Surgical care is a crucial component of building strong health systems and one that is often overlooked (Dr. Jim Kim UHC 2017 video). All people are entitled to quality essential health services, no matter who they are, where they live, or how much money they have. This simple but powerful belief underpins the growing movement towards universal health coverage (UHC), a global commitment under the Sustainable Development Goals (SDGs). Inherent in the framework of UHC is access to safe surgical, obstetric and anesthesia (SOA) care.

An estimated 33 million undergo financial hardship every year from the direct costs of surgical care. And those are the individuals fortunate enough to have access to care.4 Moreover, about 11% of the world’s disability-adjusted life years are attributable to diseases that are often treated with surgery such as heart and cerebrovascular diseases, cancer, and injuries from road traffic accidents.2,5 Other surgically treatable disorders such as obstructed labour, obstetric fistulas, and congenital birth defects are major causes of morbidity and mortality in the developing world.5,7 The delivery of safe and quality SOA care is critical for the realization of many of the Sustainable Development Goals, including: Good health and well-being (Goal 3); No poverty (Goal 1); Gender equality (Goal 5), and Reducing inequalities (Goal 10).

Conversations with Chatbots: Exploring AI’s Potential for Development

Haishan Fu's picture

Development work is getting more technologically sophisticated by the day. The World Bank’s Information and Technology Solutions (ITS) department recently started an Artificial Intelligence (AI) Initiative. At the launch event, we explored the role of AI in development and what it might mean for the work that we do here at the Bank. In short: AI is already here, international organizations have an important role to play, and we need to invest in our skills and expertise.

AI is already being incorporated into development projects

A growing family of Artificial Intelligence techniques are being employed in development. Using machine learning for classification and prediction tasks is becoming as routine as running regressions. Our team recently launched a data science competition on poverty prediction and has been evaluating the performance of different machine learning algorithms. This includes the use of automated machine learning where the machine itself helps to select and tune models in a way a data scientist ordinarily would.

Your Cow, Plant, Fridge and Elevator Can Talk to You (But Your Kids Still Won’t!)

Raka Banerjee's picture
Download the Report

The Internet of Things (IoT) heralds a new world in which everything (well, almost everything) can now talk to you, through a combination of sensors and analytics. Cows can tell you when they’d like to be milked or when they’re sick, plants can tell you about their soil conditions and light frequency, your fridge can tell you when your food is going bad (and order you a new carton of milk), and your elevator can tell you how well it’s functioning.

At the World Bank, we’re looking at all these things (Things?) from a development angle. That’s the basis behind the new report, “Internet of Things: The New Government to Business Platform”, which focuses on how the Internet of Things can help governments deliver services better. The report looks at the ways that some cities have begun using IoT, and considers how governments can harness its benefits while minimizing potential risks and problems.

In short, it’s still the Wild West in terms of IoT and governments. The report found lots of IoT-related initiatives (lamppost sensors for measuring pollution, real-time transit updates through GPS devices, sensors for measuring volumes in garbage bins), but almost no scaled applications. Part of the story has to do with data – governments are still struggling how to collect and manage the vast quantities of data associated with IoT, and issues of data access and valuation also pose problems.

Chart: Economic Development and the Composition of Wealth

Tariq Khokhar's picture
Also available in: Español

The composition of wealth fundamentally changes with economic development. Natural capital—energy, minerals, land and forests—is the largest component of wealth in low-income countries. Its value goes up, but its share of total wealth decreases as economies develop. By contrast, the share of human capital, estimated as the present value of future incomes for the labor force, increases as economies develop. Overall, human capital accounts for two-thirds of the wealth of nations. Read more in The Changing Wealth of Nations


Chart: Global Wealth Grew 66% Between 1995 and 2014

Tariq Khokhar's picture
Also available in: 中文 | العربية | Français | Español

Global wealth grew by 66% between 1995 and 2014 to a total of over 1,140 Trillion dollars. The share of the world’s wealth held by middle-income countries is growing — it increased from 19% to 28% between 1995 and 2014, while the share of high-income OECD countries fell from 75% to 65%. Read more in The Changing Wealth of Nations