Mosquito Breeding Sites With a Data Analytics App
In Colombia, 27% of people live in poverty and more than 7 million are considered internally displaced people (IDPs). These people fled their homes because of a long-running civil war and guerrilla attacks. Alongside rampant poverty and displacement, Colombia struggles with mosquito-borne diseases, reporting 1,400 cases of Zika in a single year and more than 84,664 cases of dengue fever. Worldwide, more than 1 million people die of mosquito-related illnesses each year. Premise, a company that specializes in data analytics, partnered with Colombia’s government and the United States Agency for International Development (USAID) to fight Colombia’s deadly Zika outbreaks and mosquito breeding sites with a data analytics app.

The Problem

At the beginning of 2016, South America was in the midst of Zika and dengue fever outbreaks. In 2019, more than 2 million South Americans contracted dengue, and at least 720 people died. Both Zika and dengue are mosquito-borne diseases that Aedes aegypti mosquitoes primarily spread. Aedes aegypti mosquitoes are also the main transmitters of yellow fever and chikungunya. These mosquitoes contract the virus by biting into people who already have an infection. Then, the mosquitoes spread the virus further by continuing to bite others. Only female mosquitoes are able to bite people, which is why only female Aedes aegypti mosquitoes transmit the virus.

In 2015-2016, Colombia had the second-largest Zika outbreak in the world. Cali, a city of 2.4 million people, accounted for more than 20% of the country’s cases. Some Colombians live in slum areas that lack proper sewage and garbage disposal systems, sanitation and running water. These areas are especially attractive to mosquitos, and during heavy rainfall, the health situation worsens because the slums experience flooding, creating stagnant water and puddles close to people’s homes.

Premise’s Mission

In 2017, Premise, a predictive data analytics company based in San Francisco, conducted its first phase of internal vector monitoring of Cali. The company records, georeferences and photographs mosquito breedings sites with a data analytics app, aiming to increase awareness across Colombian cities and give communities a way to fight mosquito-borne diseases. During the first phase, Premise digitally recorded 40,000 sewers and put them into the system; in the second phase, which began in 2018, Premise received funding from USAID.

As part of Premise’s work in Colombia, 7,000 people participated in a citizen network project, through which the community actively collaborated in monitoring and destroying mosquito breeding sites. Soon after Premise took off, 108,000 homes received inspections and more than 70,000 mosquito breeding sites were demolished — often by app users, who poured chlorine on the sites. The average number of breeding sites in people’s homes decreased from three to less than two in only one year.

Premise’s data science led to organized mosquito-management practices, such as vector control (killing larvae to decrease the population of male mosquitoes) and vector surveillance (keeping mosquito densities under close watch). Premise recorded 54,000 direct sewage openings that had a high likelihood of mosquitoes, and thanks to data granularity, locations of mosquito breeding sites were outlined down to the street intersections. The data analytics app not only tracked down mosquito hotspots and the origins of disease transmission but also gave civilians access to key records and methods to reduce mosquito breedings sites.

Citizen Participation

One reason for Premise’s success was the participation of local communities in its Citizen Network pilot project. Citizens directly contributed to monitoring Zika outbreaks and expanded the frequency charts and other collected data. In 2018, 2,911 citizens in Cali were actively engaged in Premise’s project, and thousands of people continue to complete Premise’s tasks each month, such as taking pictures of mosquito breeding sites, for small money prizes, which Premise sends via Bitcoin or bank deposit.

With the support of the USAID and local Colombian citizen networks, Premise is able to monitor and control Aedes aegypti mosquito breeding sites with a data analytics app. This innovative app decreases dangerous and deadly epidemics across Colombia, and soon, Premise may expand its mission across South America to help other nations in need of mosquito-borne disease control.

Anna Sharudenko
Photo: Flickr

BlueDot, a Canadian artificial intelligence company, alerted its customers of an outbreak more than a week before the WHO notified the public of the COVID-19 outbreak. The company uses programs driven by artificial intelligence to analyze large amounts of information with the goal of discovering disease outbreaks. This company – and many others like it – could be key in helping thousands of people navigate COVID-19.

What is Artificial Intelligence?

Artificial intelligence is a branch of computer science focused on intelligence displayed by machines. There are both pros and cons associated with the development of artificial intelligence. However, with the possibility of COVID-19 pushing 50 million more people into poor households in 2020, many countries are doing everything they can to harness this developing technology.

Artificial Intelligence, COVID-19 and Poverty

People in impoverished communities are facing a serious dilemma: should they continue to work and potentially catch COVID-19 or stay home and face hunger or malnutrition?

There is currently no vaccine for the virus, and lockdowns and social distancing measures are effective but economically harmful. Most people in poverty do not have the financial savings to support themselves. Similarly, restrictions have the potential to push already unstable economies in less developed countries into a recession. Fortunately, artificial intelligence is providing new ways to support people in such challenging times.

4 Ways Artificial Intelligence Can Help Impoverished Communities During COVID-19

  1. Satellite images and phone data are assisting in identifying communities in need of financial assistance. Policymakers in Togo, a West African nation, teamed up with UC Berkeley to find ways to use satellite images and phone data to identify the country’s most impoverished communities and provide aid. A similar program is already in use in various African countries. The NGO GiveDirectly partnered with a local phone company to give governmental assistance to subscribers who live in impoverished communities. The government contacts citizens and offers them a cash transfer. In March alone, GiveDirectly made payments totaling over $2.5 million to 13,806 recipients.
  2. The technology could help researchers analyze COVID-19 data and make clinical decisions. A doctor from Kashmir is using artificial intelligence to detect patterns in large amounts of COVID-19 data. Currently, there is an overwhelming influx of public health data surfacing. In addition, with the virus’s potential to push more people into poverty, there is a need to analyze and evaluate the data quickly. The doctor is also working with local professionals to discover innovative ways to provide healthcare in the country.
  3. Developing countries have started using artificial intelligence for surveillance and social control. Nations like Ecuador, Kenya, Peru and South Africa are using surveillance technologies to ensure citizens are using social distancing measures. South Africa implemented a “real-time contact tracing and communication system.” The software used to create the system was originally intended to detect rhinoceros poaching hotspots in national parks.
  4. Artificial intelligence makes it possible to accurately screen many people at a time from a distance. China has used the technology to install distanced fever-screening systems in railway and subway stations. Beijing’s Qinghe Railway Station houses one of the systems, which can “examine up to 200 people in one minute without disrupting passenger flow.” Many developing countries are densely packed, and many people in those countries have poor access to healthcare. Screening large numbers of people in a short period of time can have a positive impact on the fight against COVID-19 in developing countries.

The race to harness artificial intelligence is on around the globe. Artificial intelligence has the potential not only to alleviate the impacts of COVID-19 on developing countries but around the world. The public database Kaggle is sponsoring the COVID-19 Open Research Dataset Challenge. Its hope is that experts around the world will come together to find new ways to use artificial intelligence techniques. Ultimately, this will produce new insights to assist in the global fight against COVID-19.

Araceli Mercer
Photo: Flickr

Poverty in Big DataIt is impossible to remedy the causes of poverty without enough data to make accurate assessments for formulating solutions. There is little infrastructure in fragile countries and developing nations, making data collection difficult. Gaps in data can exist that are a decade wide. Infrequent studies conducted with only a single method of surveyal are inadequate. If there are not multiple methods of gathering data, the data will be skewed, because there will be no means of comparison for bias.

New methods have been developed to gather data remotely. These methods rely on finding signs of poverty in big data. Big data is a term for the massive amounts of data collected by computers. Poverty in big data can be detected by using self-learning artificial intelligence known as machine learning programs.

Cell Phone Data

While smartphones often remain out of reach for the impoverished, basic cell phones are a staple of life even for those living in developing nations. In fact, the greater part of sub-Saharan African countries own mobile phones. For example, in Tanzania, the country with the lowest reported number of phones, 75 percent of the population still owns a mobile device. In South Africa, the country with the highest reported number of phones, only nine percent of the population lives without a mobile phone. Another study on Rwandan households also found that mobile phones were more common than televisions or computers, ubiquitous items to the American household.
Because of these factors, there is an abundance of cell phone data (CPD) even in regions that typically lack data on poverty. According to a study done by the World Bank Group in Guatemala, CPD interpreted through machine learning can yield sufficiently accurate data of urban areas. CPD can be used to determine the location of a person’s home and how far they typically travel. With this data, researchers can see who is likely to travel to a location and who has a means of transportation for getting there.

Satellite Imagery

Civil unrest and harsh conditions can make it dangerous to gather data on poverty in some regions. These factors can disincentive data collection and cause years of gaps in survey data. A new remote method of analyzing public data on physical regions has helped demystify treacherous terrain. Satellite images of the Earth’s terrain, also known as Earth observations, display signals of wealth in a region. By measuring the luminosity of man-made light at night, researchers can make estimates of the economic status of an area. A proven correlation between illuminated areas, electric power consumption, and a country’s GDP justify these estimates. This is a fast and efficient method of obtaining data from a country that has seen natural disasters or civil war.

Social Media

The digital footprint of social media users, or lack thereof, can be useful in estimating data on the development of areas. According to the Pew Research Center, 53 percent of those in emerging nations use social media. Internet use correlates with the GDP per capita of a country, so the rising numbers of users are promising. However, sub-Saharan Africa and India are falling behind the rest of the world.

Finding poverty in big data through machine learning has proven to be informative and safe for researchers. The relatively unobtrusive nature of conducting studies in this manner makes sure that locals do not feel disturbed or angered. Remote and impersonal studies such as these also avoid issues such as under-reported poverty in illiterate households and over-reported poverty from those asked to recall their consumption.

– Nicholas Pirhalla
Photo: Flickr

Data Privacy in Africa

In the developed regions of the world, data privacy has been a topic of public discourse for some time. From the European Union’s adoption of the General Data Protection Regulation (GDPR) to smaller laws that have passed in many U.S. states, the developed world has recognized that data privacy laws are important to modern digital society. Now, as the burgeoning tech industries in many developing countries push them into fast-paced versions of the West’s digital revolution, many developing countries are also beginning to put similar laws into effect. In particular, data privacy in Africa has become a major concern as the region steps into the digital age.

Data Privacy in Africa

In June of 2019, leaders from across Africa gathered in Ghana for the groundbreaking Africa International Data Protection and Privacy Conference. At this conference, African leaders such as Ghanaian Vice President Dr. Mahamudu Bawumia, U.N. Special Rapporteur on Right to Privacy Joe Cannataci, and Chairperson of the Information Regulator of South Africa Pansy Tlakula spoke about ways to advance data privacy in Africa. Topics ranged from convincing African nations to fall in step with international laws about data privacy to integrating data privacy laws with religious groups in Africa.

This conference came at a crucial time in the development of data privacy in Africa. Sub-Saharan Africa alone is expected to add more than 250 million internet users by 2025, and the Sub-Saharan mobile industry is expected to add $185 billion to GDP by 2023. Despite this growth in internet use, the continent is currently behind on data privacy laws. Only 17 out of 54 countries in Africa have passed data privacy laws, and 15 African countries have yet to ratify the African Union’s Convention on Cybersecurity and Data Protection. The leaders assembled at the conference hoped to change this. “Data protection in Africa is a prerequisite” to joining the Fourth Industrial Revolution, said Hon. Vincent Sowah Odotei, Ghana’s deputy minister of communications, in his final remarks of the conference.

Improved data privacy in Africa has several benefits. According to the Global System for Mobile Communications Association, improving data privacy, “allows countries to trust each other and enforcement bodies to cooperate. In turn, this can boost the economy by allowing data to flow within the region and it is more attractive for external investors who prefer not to be confined to keeping data in one place.”

How a Lack of Data Privacy Harms Poor Communities

Beyond large-scale economic benefits, improved access to data privacy will have specific benefits for low-income Africans. A 2017 study from Washington University in St. Louis found that poor people are more vulnerabilities when it comes to data privacy, facing vulnerabilities such as a greater likelihood of having their personal data used against them and more devastating consequences from identity theft. Poorer people are also much less likely to have basic digital literacy skills, thus increasing their vulnerability to digital threats, with 64 percent of poor Americans reporting that they do not have a good understanding of how the privacy policies of websites they visit apply to them.

Michele Gilman, one of the authors of the study, said in an interview with The Borgen Project that data privacy is tantamount to improving the lives of those in poverty. Gilman said, “Technology can be a tremendous resource for people living in poverty to access services and opportunities as a ladder out of poverty—but without controls or regulation, it can also further entrench poverty.”

Gilman pointed out that identity theft can wreak particular havoc for people living in poverty. When people living in poverty are victims of identity theft, according to Gilman, their lack of a social safety net coupled with the sudden loss of most of their financial assets can lead to dire consequences. Because poor people tend to lack the resources to undo the consequences of identity theft, the American Bar Association reports that they are more likely to be wrongfully arrested and hounded by collection agencies for crimes they didn’t commit and loans they didn’t take out. This is all in addition to the usual consequences of identity theft, which can take months to resolve. The Bureau of Justice Statistics found that in the U.S., 43 percent of households that were victims of identity theft made less than $75,000 per year. In South Africa almost half the consumer population either has been, or knows someone who has been, the victim of identity theft.

Gilman also illuminated the broader threat that a lack of data privacy can pose for those in poverty. Big data coupled with societal discrimination can lead to low-income people systematically being denied access to resources and they are more often targeted by government surveillance. For instance, 40 percent of colleges and universities use applicants’ social media profiles to make decisions through a process known as social analytics, where algorithms go over applicants’ social media behavior as well as who they are friends with in order to determine their qualifications to enter.

Up to 27 percent of poor social media users don’t use any settings at all to make social media profiles private, and because poorer students tend to rely more on financial aid, there is a concern that social media analysis will allow universities to selectively avoid recruiting low-income students. In a similar vein, police departments have begun to use a process known as threat scoring, where they analyze crime statistics to determine how likely a given individual is to commit a crime using data from social media and other sources, essentially creating guilt by association.

Effectiveness of Data Privacy Laws

In places where data privacy laws have already taken effect, the results have been significant. Since the passing of the GDPR, record numbers of data breaches that otherwise would have gone unreported, have been reported to the relevant authorities, with 36,000 breaches reported in 2018 compared to between 18,000 and 20,000 in 2017. Countries around the world, from Brazil to Hong Kong, have passed GDPR-like bills, and many other countries are looking to follow suit. The implementation of these laws has not been without hiccups—many businesses in the EU have struggled with the implementation of new regulations, and the EU has been slow to actually enact fines for companies that break GDPR rules—but in the end, these laws will help to dismantle the structures that keep people in poverty.

Data privacy laws protect low-income people from negative consequences such as identity theft and algorithmic discrimination. The creation of laws to increase data privacy in Africa, therefore, will increase protection for Africans who are being kept in poverty by lenient data privacy regulations. As the region’s tech develops, its laws are also developing to ensure that increased access to technology also means increased possibility to alleviate poverty.

– Kelton Holsen
Photo: Flickr

mobile phone developmentWith simple communication, monitoring and data collection, the full capabilities of mobile phone technology in developing countries are being put to work. Keep reading to learn more about the benefits of mobile phone development in developing countries.

Monitoring

Monitoring and regular, real-time updates on the conditions of everything from crops to the spread of disease are a huge help for organizations dedicated to mobile development. Farmers can use a wireless sensing network (WSN) to monitor crop and soil conditions as well as irrigation systems for better water management. Simple, inexpensive and low-powered sensing nodes communicate information directly to farmers’ mobile devices. Farmers can also use their mobile devices to check and monitor rising and falling market prices.

In 2013, UNICEF partnered with Ugandan farmers to track and monitor the spread of banana bacterial wilt, a disease that threatens bananas, one of Uganda’s major food staples. Through mobile phone polling, UNICEF was able to map the areas of farmland where bananas were infected and bring that vital information directly to farmers.

Health workers are also utilizing mobile monitoring particularly to track and prevent the spread of infectious diseases. Innovative Support to Emergencies Diseases and Disasters (InSTEDD) is a data collection software used to record incidents of communicable disease. Health departments in Thailand and Cambodia have piloted an early warning disease surveillance initiative. Using SMS, InSTEDD has been used to track diseases at the local and national level. Health officials hope that the use of such mobile development will help them track, prevent and prepare for potential disease outbreaks.

Communication and Information Delivery

SMS provides a cheap and fast means of communication. Although a very basic messaging service, it is compatible with even the cheapest mobile phones. Even this simple text service is being put to work to improve lives around the world. In 2014, IntraHealth International and UNICEF created mHero, a two-way mobile phone-based communication system. Using SMS, ministries of health exchange real-time information and data with health workers in the field. This timely flow of communication helps health workers perform better-informed care and provides them with reliable support.

Rapid communication is also being used to alert residents in Bangalore, India to water availability. In Bangalore, people may have to wait up to 10 days for water to be available. NextDrop is a phone-based program that uses text messaging to notify residents when their water will next be available. With 75,000 registered users, NextDrop communicates vital, timely information about the water availability, so that residents need not waste their days waiting.

Data Collection

Polling, surveys and civilian reports have long been used to supply organizations with information about the populations they are serving to provide better and more efficient aid. Mobile phones reduce the need for face-to-face interviews to collect data as well as cut costs of landline calls, allowing health workers to reach more people in less time. With larger pools of responders, health surveys inform officials of a more complete summary of the population. The Performance Monitoring and Accountability 2020 (PMA2020) is a global survey project with the goal of providing women and girls with access to modern contraceptive methods by 2020. Through household surveys, PMA2020 collects fertility data to estimate the total fertility rate of a given country.

UNICEF created their own reporting system using mobile devices called U-Report. This messaging and reporting tool empowers users to speak out about issues that matter most to them. Active in 53 countries and with more than 6 million users, U-Report has been used to engage in issues from employment discrimination to child marriage. Data is then shared with policymakers so that they can make informed decisions. U-Report can be used with multiple messaging services including SMS so that even users with basic mobile phones can participate. The service is free and anonymous to encourage as many users as possible to report. UNICEF utilized U-Report’s messaging system to send alerts to users living in the path of Hurricanes Irma, Jose and Maria and using SMS shared vital information with families during the major floods in Abidjan, Côte d’Ivoire.

With the help of mobile devices, almost every corner of the world is reachable, from the poor living in the largest cities to the most rural communities. Aid organizations are making vital use out of the communication and data collection capabilities to help those who are most in need. Mobile development is helping to ensure that everyone has the tools and information to make informed decisions, ask for assistance, and pull themselves out of poverty.

– Maya Watanabe
Photo: Flickr

Data CollectionMillions of people across the world suffer from extremely impoverished living conditions and nations and organizations around the world have committed to greatly reducing this number by 2030. Surprisingly, data collection has and will continue to play a crucial role in this process.

In the last few decades, the world has experienced a significant decline in the portion of the global population that may be considered extremely poor. But how do we know this? Data collection is extremely important in determining a baseline for poverty as well as measuring successes in measures to eradicate it.

Data collection has taken several forms throughout the years, becoming more accurate and streamlined. However, there is still room for improvement in streamlining efforts, which takes human power, technology investments and funding. In short: without data collection, ambitious efforts toward ending global poverty may drag on or stall altogether.

Surveys are a primary means of data collection. Statistical groups see this as the best measure of current lifestyle conditions of those living in poverty. These types of surveys can measure levels of income, familial distribution, education, employment, gender ratios, birth rates and death rates across a large representative portion of any country’s population.

The coverage and frequency of these surveys has increased over time, making measurements that much more precise. However, this data still remains largely incomplete in many areas due to migration, refugee situations, and minimal access simply due to the level of danger.

Innovations in technology are helping to close inherent gaps in survey systems when it comes to data collection on poverty. Automating surveys make the collection even more accurate and organized as well as can become more widespread, reaching the once-unreachable. Cell phones and computers with Internet capabilities have carved out a new path for data collection, as they are accessible to most extremely poor countries. These technologies are also more fiscally responsible for the distributors in the long run.

Data collection is extremely important in continuing the battle against extreme poverty, to help better understand the problem at hand: what may be working, what is not, and what corrections will potentially make a huge impact.

Casey Hess

Photo: Flickr

Collect Poverty DataIn order to provide an impoverished area with the necessary aid, extensive and detailed data needs to be collected. Data can identify specific regions in poverty along with the types and causes of poverty in those vicinities. Yet, traditional pen and paper data collection is time consuming and error prone. Using technologies such as mobile phones and tablets as well as developing new information-collecting technology is a way to collect poverty data that can solve the glitches data collection has suffered in the past.

In the past, enumerators, or data collectors, would travel house to house and conduct paper surveys in order to acquire information on those living in poverty. These answers would then be manually transferred onto a computer.

Now, enumerators are using tablets that send survey answers to a centralized system. The tablets also have a GPS system that tracks the enumerators’ processes and makes sure they are in the right area. The tablets also allows for enumerators to record video interviews. This provides a visual context for the living conditions in certain impoverished areas.

Mobile phones are another great resource for data collecting. The World Bank’s Listening to Africa initiative uses cellphones to send out surveys as well as to monitor crises. The initiative plans to pass out phones and solar chargers to all respondents who don’t already own them. Mobile surveys provide a cheap way to gather frequent data from a large amount of people. Crises such as famines and natural disasters can be reported and monitored in real time as well by calling those in affected areas.

New information gathering technology is also being developed to make data collecting easier. Satellite imagery is being used to measure how many people live in poverty in certain areas and assess living conditions of these populations. Likewise, Smart Survey Boxes are being installed in households to automatically monitor power outages and energy quality in areas like Tajikistan.

With extensive data that’s up to date, the causes of and solutions to poverty can be better understood. Using technology to collect poverty data may be the solution to providing better aid to the world’s poor.

Hannah Kaiser

Photo: Flickr

Machine Learning
In today’s information age, the most abundant resource has quickly become information itself, more specifically data. By 2010, the world had created 1.2 zettabytes (1.3 trillion gigabytes), an equivalent to 75 million 16 GB iPads. By 2016, the world completely surpassed this, creating 90 percent of all data in just the last two years. 2.5 quintillion bytes of data are created every day.

These numbers are much too large for any single person to comprehend, but with the help of technology and machine learning the data can help optimize transportation in cities, predict the stock market and diagnose diseases, along with a vast amount of other tasks. Big data has largely been a tool for the developed world; however, there is plenty of potential for it to become a key factor in ending poverty.

In August, Stanford researchers published a paper on using satellite imaging and machine learning to track and measure poverty throughout Africa. Accurate measurement of poverty in Africa was extremely lacking as “39 out of 59 African countries conducted fewer than two surveys to measure poverty” between 2000 and 2010, according to The World Bank.

Previous strategies for measuring poverty also included tracking mobile phone usage and satellite photos of lights at night; however, phone data was not always available and nighttime light data could not differentiate between poverty and extreme poverty levels.

Instead, the Stanford researchers used daytime images of development, such as paved roads, farms, metal roofs, along with nighttime light intensity data to measure poverty. They input the data into a computer model to map out poverty levels throughout the test countries, Nigeria, Uganda, Tanzania, Malawi and Rwanda. Using methods such as these, African governments and NGOs will be better equipped to design policy and find areas most affected by poverty.

Furthermore, one of the most famous machine learning tools is IBM Watson, a supercomputer that uses advanced software to answer questions. In 2014, IBM launched Project Lucy, a mission to bring Watson to Africa and use the artificial intelligence to help solve the problems surrounding “health care, education, water and sanitation, human mobility and agriculture.”

More generally, scientists predict that machine learning has the potential of predicting the future and keeping watch over society. More specifically, the technology has the capability of forecasting underperforming crops in developing countries and situations that will call for an international convention.

Using biometric data, governments, especially that of India, hope to identify all citizens and ensure they can receive subsidies and benefits, helping to end inefficiency and corruption.

Machine learning is clearly a revolutionary technology, but its true potential is still unclear. So far, it acts as an aid to researchers, aggregating data and producing summaries.

However, machine learning could even advance to levels of innovating on its own. For example, instead of diagnosing a disease, machine learning could help find the cure to one. In the next decade or so, the world will wait and see where this amazing technology can take it.

Henry Gao

Photo: Flickr

Monitoring Global Poverty

While taking action is an important part of fighting global poverty, it is also critical that international organizations correctly assess the situation through different methods of data collection and analysis. Monitoring global poverty is crucial for ending it.

Since the World Bank’s first census in 1975, attempts to monitor global poverty levels have widened in both scope and methodology. The invention of PovcalNet in the 1980s enabled researchers to access the poverty distributions of 191 countries online. However, the diversification of research methods entailed as much inconsistency as convenience, as data collected by different teams seemed to suggest entirely different results.

Since one organization cannot survey all the households of the world, analysts often collect survey results from the governments of different countries. This introduces inconsistencies into investigation methods, including differing methods of selecting and interviewing sample populations.

When measuring qualitative measures such as household participation, patterns of consumption and perception towards poverty, long-term participatory observation can be more appropriate than surveys, as the wording of questions can manipulate the results.

After data is collected, it is classified and represented into charts or graphs, where more complications can occur. There exist many statistical methodologies, including parametric, non-parametric and lognormal, and countries differ on how to define poverty in various environments.

To standardize data collection and facilitate monitoring global poverty, the World Bank has been urging nations to adopt the National Strategies for the Development of Statistics (NSDS), emphasized at the Marrakech Action Plan for Statistics in 2004.

NSDS requires not only economic support, but political cohesion between departments and local communities in each country. The NSDS Knowledge Base will compile research techniques and provide 100 indicators to the progress of Sustainable Development Goals so that results from different countries can be comparable.

Difficulties of standardization often derive from insufficient infrastructure, such as the failure to register all citizens on census, and requires a long-term investment. In such cases, innovative measures can improve cost-benefit efficiency.

The UN’s Data for Development report from 2015 suggests using satellite imagery and mobile-phone-based data collection. Instead of designing a separate survey, data from social media and mobile call traffic can be repurposed as an indirect indicator. In East Africa, for example, mobile technology is expected to cut up to 60 percent of the cost of traditional paper surveys.

Haena Chu

Photo: Flickr

Big Data Fight Against Poverty
Big Data, as its name would suggest, refers to large sets of unstructured and structured data generated at high speeds from digital and traditional data sources around the globe. The big data movement has gained momentum over the years, particularly in the business sphere, but experts have also realized that insights derived from big data have implications for the fight against poverty.

In the agricultural space for example, The Forum for Agricultural Research in Africa has found that farmers in Africa barely produce what they need to get by.

Food Policy experts have found that helping these farmers in Africa and other parts of the world produce more food is key to lifting millions out of poverty. One of the key ways of attaining this goal, according to the experts, is by providing farmers, scientists and entrepreneurs in the agricultural sector with adequate access to data and information generated at agricultural research centres worldwide.

Ft Magazine highlights how big data is being used to mitigate the harmful effects that accompany natural disasters. It explains that in the aftermath of the devastating Haiti earthquake of 2010, researchers at the Karolinska Institute of Columbia University successfully managed to track the locations of 600,000 displaced people using data mining techniques.

The article in Ft Magazine also illustrates how analyzing data from social media sites like Twitter and Facebook can provide early warning systems for both human and natural disasters. For example, increased references to food or ethnic strife on these sites can serve as indicators of possible famine or civil unrest.

Mark Van Rijmenam, the founder of Datafloq, further empathizes the use of Big data in the disaster response field, saying “Big data offers, for example, the possibility to predict food shortages by combining variables such as drought, weather conditions, migrations, market prices, seasonal variation and previous productions.”

In the area of public health and sanitation, Van Rijmenam talks about harnessing data from call detail records to map variations in the population of low-income dwellers in order to direct efforts at building water pipes and latrine facilities for the slum dwellers. This effort will see improved sanitation in such areas, bringing about better health.

A pilot program by the World Bank in Tanzania called SMS for life has generated major improvements in the distribution of malarial medical stock. By getting clinical workers to send an SMS with their stock count every week, the program has enabled senior coordinating staff to re-stock clinics more accurately.

SMS for life has managed to reduce the number of rural health facilities in Tanzania without medical stock from 78 percent to 26 percent.

World Bank blogger Alla Morrison has likened the transformative potential of big data to the transformative effect that electricity had on industry in the 19th century. She argues that big data is a game changer for business, and notes the unprecedented productivity gains in the second industrial revolution due to businesses in all sectors taking advantage of the new electrical resource.

Likewise, as humanity forges ahead, it is important that organizations, governments and individuals take advantage of big data to address the seemingly intractable challenge of poverty.

June Samo

Sources: Enterra Solutions 1, Enterra Solutions 2, FT Magazine, IEEE, SAS, Smart Data Collective, UN GLOBAL PULSE, World Bank 1, World Bank 2
Photo: Flickr