Tag Archives: Develpment

Big Data for Development: Challenges and Opportunities

The UN Global Pulse report on Big Data for Development ought to be required reading for anyone interested in humanitarian applications of Big Data. The purpose of this post is not to summarize this excellent 50-page document but to relay the most important insights contained therein. In addition, I question the motivation behind the unbalanced commentary on Haiti, which is my only major criticism of this otherwise authoritative report.

Real-time “does not always mean occurring immediately. Rather, “real-time” can be understood as information which is produced and made available in a relatively short and relevant period of time, and information which is made available within a timeframe that allows action to be taken in response i.e. creating a feedback loop. Importantly, it is the intrinsic time dimensionality of the data, and that of the feedback loop that jointly define its characteristic as real-time. (One could also add that the real-time nature of the data is ultimately contingent on the analysis being conducted in real-time, and by extension, where action is required, used in real-time).”

Data privacy “is the most sensitive issue, with conceptual, legal, and technological implications.” To be sure, “because privacy is a pillar of democracy, we must remain alert to the possibility that it might be compromised by the rise of new technologies, and put in place all necessary safeguards.” Privacy is defined by the International Telecommunications Union as theright of individuals to control or influence what information related to them may be disclosed.” Moving forward, “these concerns must nurture and shape on-going debates around data privacy in the digital age in a constructive manner in order to devise strong principles and strict rules—backed by adequate tools and systems—to ensure “privacy-preserving analysis.”

Non-representative data is often dismissed outright since findings based on such data cannot be generalized beyond that sample. “But while findings based on non-representative datasets need to be treated with caution, they are not valueless […].” Indeed, while the “sampling selection bias can clearly be a challenge, especially in regions or communities where technological penetration is low […],  this does not mean that the data has no value. For one, data from “non-representative” samples (such as mobile phone users) provide representative information about the sample itself—and do so in close to real time and on a potentially large and growing scale, such that the challenge will become less and less salient as technology spreads across and within developing countries.”

Perceptions rather than reality is what social media captures. Moreover, these perceptions can also be wrong. But only those individuals “who wrongfully assume that the data is an accurate picture of reality can be deceived. Furthermore, there are instances where wrong perceptions are precisely what is desirable to monitor because they might determine collective behaviors in ways that can have catastrophic effects.” In other words, “perceptions can also shape reality. Detecting and understanding perceptions quickly can help change outcomes.”

False data and hoaxes are part and parcel of user-generated content. While the challenges around reliability and verifiability are real, Some media organizations, such as the BBC, stand by the utility of citizen reporting of current events: “there are many brave people out there, and some of them are prolific bloggers and Tweeters. We should not ignore the real ones because we were fooled by a fake one.” And have thus devised internal strategies to confirm the veracity of the information they receive and chose to report, offering an example of what can be done to mitigate the challenge of false information.” See for example my 20-page study on how to verify crowdsourced social media data, a field I refer to as information forensics. In any event, “whether false negatives are more or less problematic than false positives depends on what is being monitored, and why it is being monitored.”

“The United States Geological Survey (USGS) has developed a system that monitors Twitter for significant spikes in the volume of messages about earthquakes,” and as it turns out, 90% of user-generated reports that trigger an alert have turned out to be valid. “Similarly, a recent retrospective analysis of the 2010 cholera outbreak in Haiti conducted by researchers at Harvard Medical School and Children’s Hospital Boston demonstrated that mining Twitter and online news reports could have provided health officials a highly accurate indication of the actual spread of the disease with two weeks lead time.”

This leads to the other Haiti example raised in the report, namely the finding that SMS data was correlated with building damage. Please see my previous blog posts here and here for context. What the authors seem to overlook is that Benetech apparently did not submit their counter-findings for independent peer-review whereas the team at the European Commission’s Joint Research Center did—and the latter passed the peer-review process. Peer-review is how rigorous scientific work is validated. The fact that Benetech never submitted their blog post for peer-review is actually quite telling.

In sum, while this Big Data report is otherwise strong and balanced, I am really surprised that they cite a blog post as “evidence” while completely ignoring the JRC’s peer-reviewed scientific paper published in the Journal of the European Geosciences Union. Until counter-findings are submitted for peer review, the JRC’s results stand: unverified, non-representative crowd-sourced text messages from the disaster affected population in Port-au-Prince that were in turn translated from Haitian Creole to English via a novel crowdsourced volunteer effort and subsequently geo-referenced by hundreds of volunteers  which did not undergo any quality control, produced a statistically significant, positive correlation with building damage.

In conclusion, “any challenge with utilizing Big Data sources of information cannot be assessed divorced from the intended use of the information. These new, digital data sources may not be the best suited to conduct airtight scientific analysis, but they have a huge potential for a whole range of other applications that can greatly affect development outcomes.”

One such application is disaster response. Earlier this year, FEMA Administrator Craig Fugate, gave a superb presentation on “Real Time Awareness” in which he relayed an example of how he and his team used Big Data (twitter) during a series of devastating tornadoes in 2011:

“Mr. Fugate proposed dispatching relief supplies to the long list of locations immediately and received pushback from his team who were concerned that they did not yet have an accurate estimate of the level of damage. His challenge was to get the staff to understand that the priority should be one of changing outcomes, and thus even if half of the supplies dispatched were never used and sent back later, there would be no chance of reaching communities in need if they were in fact suffering tornado damage already, without getting trucks out immediately. He explained, “if you’re waiting to react to the aftermath of an event until you have a formal assessment, you’re going to lose 12-to-24 hours…Perhaps we shouldn’t be waiting for that. Perhaps we should make the assumption that if something bad happens, it’s bad. Speed in response is the most perishable commodity you have…We looked at social media as the public telling us enough information to suggest this was worse than we thought and to make decisions to spend [taxpayer] money to get moving without waiting for formal request, without waiting for assessments, without waiting to know how bad because we needed to change that outcome.”

“Fugate also emphasized that using social media as an information source isn’t a precise science and the response isn’t going to be precise either. “Disasters are like horseshoes, hand grenades and thermal nuclear devices, you just need to be close— preferably more than less.”