In early November 2013, Typhoon Haiyan tore through the Philippines leaving in its wake a trail of destruction and tragedy. 13 million people were adversely affected by the Typhoon, including 4.9 million children of whom 1.5 million were under the age of 5 are deemed to be at risk of Global Acute Malnutrition. The pain and suffering that a disaster such as this precipitates is clearly impossible to quantify in terms of data, but data’s use can be an essential tool for the emergency services when they need to identify when and where people are in danger and what resources they need to save them.
Big data when used in conjunction with humanitarianism is often harnessed in the form of crowdsourcing, a phenomenon that has been with us since 2007. Around that time, Kenyan, non-for profit, Ushahidi began mapping user-generated accounts of brutality after the elections in Kenya in an effort to spur donations to the region. By plotting the events it created a public record of the event and spawned a number of similar sites that have helped humanitarian projects go from strength to strength.
The process of crowdsourcing is developed by categorising and verifying reports transmitted by witnesses of events, normally through email, text messages and social media, with Twitter being of particular importance. Crowdsourcing and the incorporation of Big Data has been imperative in assisting the services efforts to stem the tide against a number of natural disasters including, Typhoon Haiyan and the Haiti earthquake.
Big Data is also being picked up by a number of international relief institutions, including the Disaster Relief International (DRI), a major supplier of humanitarian aid, which has used Big Data analysis to improve response efforts in the Philippines by tracking assets and personnel in real-time and determining where is help is most urgent. Their willingness to get on board with Big Data gave them the Peter F. Drucker award for Non-Profit Innovation.
Typhoon Haiyan was not Big Data’s first major, widespread endeavour into humanitarianism. Instead, we first saw its potential being put to use in the Haiti earthquake. Its success was born out of partnership between the public and private sector and allowed data scientists from the Karolinska Institute to use data from Digicall, Haiti’s largest mobile phone operator, to compare people’s movements before and after the earthquake so that they can decipher where the ‘hot-spots’ so that they can get medical supplies over to them as soon as possible.
Big Data’s use is not just confined to disaster relief and can also play an important role in helping policy makers and researchers. The United Nations Population Fund teamed up with SAP AG in 2011 to create two dashboards with the aim of engaging people in the societal and demographic trends that are shaping the world we live in through to 2100.
By using data, the dashboards gave us an in-depth look into the way in which the globe is likely to develop, all the way up to 2100. This project was in line with their 7 Billion Actions campaign, which looked to raise global awareness around the opportunities and challenges associated with a population of over 7 billion. The fact that technology and data plays such an important role here shows that data has the power to transform hard, often difficult to read information, into clear and consummate info-graphics.
Far from being just a reactive tool, Big Data has the capacity to pre-empt crises, or at least respond to them in quicker fashion. Take the Cholera outbreak in Haiti that has been a pressing issue for over four years, a study in 2012 showed that Twitter was yielding data that would have made for quicker detection of the outbreak when compared to more traditional methods.
They found that, as the number of incidents increased and decreased, so did the amount of tweets and informal media reports. A truly remarkable finding, and one that shows the power that data has at its disposal. If these trends had been visualised more readily, the disease could have been stifled in its early stages, and in turn saved a number of lives. The report stipulates, “This information in the right hands could have saved lives”
Disease tracking, as seen with the Cholera outbreak in Haiti, is perhaps the most meaningful contribution that Big Data can make. In Kenya, a number with high mobile phone penetration, data has been combined with regional malaria prevalence information to figure out how population movements influence the spread of disease. The information they garnered allowed them to suss out the probability of a resident being infected with malaria and the chance that a visitor to a stated area would be infected on any given day.
These developments are incredibly impressive and when you consider that mobile phone penetration in Africa has hit 80% and is still on the up, some 4.2 % annually, the opportunities for mobile data aggregation is a significant one and one that has the potential to foresee disease epidemics.
If take some time to look at Big Data and Humanitarianism, then a concept you will see commonly is ‘Data Philanthropy’. This concept involves companies sharing their proprietary datasets for social good. As stated numerously throughout this article, big data is at the centre of aid reliefs nowadays, but issues continue to persist in the form of the processing phase, which can still be rather time consuming and often requires individuals to monitor content in real time. This has led to the proposition of media companies getting on board whose insights will allow for cutting edge media monitoring that can be done faster and more efficiently.
Big Data and Humanitarianism are two areas that have the ability to be a match made in heaven and go some way to helping the emergency services quell some of the globes most pressing and urgent humanitarian crises. Similar to its implementation in the LAPD, it demonstrates how far Big Data can go outside of the business landscape and the extent to which it can assist the individuals working at the ground level. What is clear is that through Big Data we have a more accurate view of the earth and where it is likely to be in the next 100 years Through these projections, organisations such as the UN can make accurate assertions as to what sections of society need the most help to develop as fruitfully as possible. This has been reflected in the UN’s 7 billion Act, where they marked 7 key issues that they feel are the most imperative for the globes growing population.
The use of Big Data and crowdsourcing is clearly not without its limitations. You only need to look back a few months to the Google Flu Tracker failure to see that there is still significant progress to be made, and that relying on data is not always best practice. The Google Flu Tracker overestimated the size of the influenza pandemic by 50% and miscalculating the severity of last year’s flu, predicting double the amount of flu-related doctor visits. So clearly we must tread with caution, but Big Data can be an essential tool and this has been proven time after time over the last 5 years or so.