Globally, there are over 98 mobile subscriptions per 100 people, so the chances are, you have a cell phone. Now look at your recent calls, both sent and received: Who do you call most often? Who calls you the most? Do you send, or receive more calls? All this is cell phone metadata: not the content of the calls, but ancillary information, the “who, where and when”.
It’s information that can reveal a lot about you. Your cellphone carrier already uses it to bill you, and may also be using it to target marketing or special offers at you. And with appropriate privacy protections, it can offer researchers a similar opportunity. In this week’s episode of Between 2 Geeks we ask how cellphone metadata (“call detail records”) can help researchers understand entire societies.
The 17 Sustainable Development Goals and their associated 169 targets are ambitious. They will be challenging to implement, and challenging to measure. The Atlas offers the perspective of experts in the World Bank on each of the SDGs.
For example, the interactive treemap below illustrates how the number and distribution of people living in extreme poverty has changed between 1990 and 2013. The reduction in the number of poor in East Asia and Pacific is dramatic, and despite the decline in the Sub-Saharan Africa’s extreme poverty rate to 41 percent in 2013, the region’s population growth means that 389 million people lived on less than $1.90/day in 2013 - 113 million more than in 1990
Note: the light shaded areas in the treemap above represent the largest number of people living in extreme poverty in that country, in a single year, over the period 1990-2013.
While stuck in I-66 traffic one morning, a colleague and I had a vigorous debate on the merits of open-source versus proprietary software. I was left with the realization of how much misinformation still persists about this particular subject.
This discussion prompted me to be more proactive about advocating for the adoption of open-source technology. I believe we are just beginning to explore the possibilities for these tools in reducing poverty and ensuring sustainable development.
Where should telecom providers place their towers and what frequencies should they use?
How can governments best calculate commodity imports to ensure food security?
How can communities better manage areas at risks of floods?
These are just some of the questions that organizations around the world try to answer by using open government data — free, publicly available data that anyone can access and use, without restrictions. Yet around the world, much government data is yet to be made available, and still less in machine-readable formats. In many low and lower-middle income countries, finding and using open data is often challenging. It may take a complicated request process to get data from the government, and the data may come in the form of paper-based documents that are very hard to analyze. A new study looks to better understand how organizations in low and lower-middle income countries utilize machine-readable open data.
In producing the study, the Center for Open Data Enterprise, supported by the World Bank, interviewed dozens of businesses and nonprofit organizations in 20 countries. The organizations were identified through the Open Data Impact Map, a public database of organizations that use open data around the world, and a resource of the Open Data for Development (OD4D) Network. Over 50 use cases were developed as part of this study, each an example of open data use in a low or lower-middle income country.
The typical household in many African cities cannot afford public transport fares. According to a new report, public transport in Sub-Saharan Africa's major cities is dominated by informal minibuses, and is expensive relative to household budgets making it largely unaffordable on a daily basis, especially for the poorest.
Latin American and the Caribbean accounts for only 8 percent of the world’s population, but for 37 percent of the world’s homicides. Eight out of the 10 most violent countries in the world are in the region, where there were an average of 24 homicides per 100,000 people per year in 2012. Read more in "Stop the Violence in Latin America"
Population density is one of the most important statistics for development efforts across many sectors, and since early 2016 we’ve been collaborating with Facebook on evaluating a new source of high-resolution population data that sheds light on previously unmapped populations.
With the building footprints detected by artificial intelligence (AI) over high-resolution commercial satellite imagery, the data sets provide estimates of population at 30m spatial resolution, making these maps the highest-resolution population maps ever produced. This is only possible through recent breakthroughs in computer vision due to deep learning algorithms and technological development of computer processors, as well as the increasing availability of high-resolution commercial satellite imagery.
Image 1: Naivasha, Kenya.
DigitalGlobe satellite (upper left), gridded population of the world v4 from CIESIN (upper right), WorldPop (bottom left), output from Facebook model (bottom right).
Facebook recently announced the public release of unprecedentedly high-resolution population maps for Ghana, Haiti, Malawi, South Africa, and Sri Lanka. These maps have been produced jointly by the Facebook Connectivity Lab and the Center for International Earth Science Information Network (CIESIN), and provide data on the distribution of human populations at 30-meter spatial resolution. Facebook conducted this research to inform the development of wireless communication technologies and platforms to bring Internet to the globally unconnected as part of the internet.org initiative.
Figure 1 conveys the spatial resolution of the Facebook dataset, unmatched in its ability to identify settlements. We are looking at approximately a 1 km2 area covering a rural village in Malawi. Previous efforts to map population would have represented this area with only a single grid cell (LandScan), or 100 cells (WorldPop), but Facebook has achieved the highest level of spatial refinement yet, with 900 cells. The blue areas identify the populated pixels in Facebook’s impressive map of the Warm Heart of Africa.
Facebook’s computer vision approach is a very fast method to produce spatially-explicit country-wide population estimates. Using their method, Facebook successfully generated at-scale, high-resolution insights on the distribution of buildings, unmatched by any other remote sensing effort to date. These maps demonstrate the value of artificial intelligence for filling data gaps and creating new datasets, and they could provide a promising complement to household surveys and censuses.
Beginning in March 2016, we started collaborating with Facebook to assess the precision of the maps and explore their potential uses in development efforts. Here, we describe the analyses undertaken to date by the Living Standards Measurement Study (LSMS) team at the World Bank to compare the high-resolution population projections against the ground truth data. Among the countries that were part of the initial release, Malawi was of particular interest for the validation exercise given the range of data at our disposal.
Peru welcomed 3.2 million tourists in 20 14, the highest number to date. In some regions of the country, like Cusco, tourism is a potential economic lifeline for local people, who can profit from a variety of businesses serving tourists. In 2012, the World Bank Group began working with The Government of Peru to streamline the processes around opening tourism-related businesses because excessive regulations and red tape were holding up investments in new businesses for years. Ultimately, the project shaved 3 years off the business registration process and eliminated 150 unnecessary regulations. With the streamlined regulations in place, investments in hotels in Peru are on the rise. Between 2015 and 2018, Peru is expecting US$1.2 billion in investments in new hotels, an increase from US$550 million during the period 2010-2014.