02
Jan 17

Building an initiative about earthquakes

By: K.Tatakis

Two friends in Italy decided to act, as a devastating earthquake happened last August. Their initial idea was to create a group on social media but then also a website was born Terremoto Centro Italia.info. The initiative became famous as an open source platform where everyone helping or needing something in or about the devastated zones could share information and thus organize more efficiently her or his own wishes.

Matteo Tempestini and Matteo Fortini were the two friends that originally had this idea and dared the next step.

“This idea was born on August 24 (2016), when we had a very big earthquake in central Italy. We decided to create this project in order to have more information about this event.We are friends with Matteo Fortini and we always keep in touch. In the morning of August 24, we discussed in a chat and we decided to open a group on Facebook immediately and then, the day after, we created the website” Tempestini said in an interview I made with him on November 20, 2016.

“The Facebook group became popular immediately as Facebook is full of people. What we initially have done initially on Facebook, was to call people to get involved and explain what we are trying to do. We wanted to create tools to inform someone about the earthquake. It is not that simple to explain. We have a lot of associations and communities that keep in touch with us, people that are in field and explaining other people how they can use the platform we had created. This is very important. Thus we are involving people that are really in the epicenter of the earthquake. That is the most difficult thing to do but is what to be done in order to have real, first hand information about the earthquake” Tempestini explained during the interview.

“Since the beginning we had the support of organizations like Action Aid Italia that believed in this project and gave human resources to support the idea, but also open street map activists that gave tools for the emergency, like maps for people in the field. When a disaster happens in Italy people create spontaneous associations to rebuild their cities” Tempestini adds.

They even offered helped to build a similar initiative in New Zealand after an earthquake happened there recently. We don’t want to impose anything. This is a solution for aggregating information we have in social media channels and it is all open source and re-usable” Tempestini argues.

Connecting needs to offers faster is crucial

What a country can do in order to be more effective in emergencies?

“I think that Italy is very well organized to manage crises in field but in my mind there is some miscommunication sometimes, so we can now we can increase communication through Terremoto Centro Italia . If you want to improve this aspect you should create what I call the Internet of people, instead of the Internet of things. Before the crisis you have to create a network of people, the right channel to communicate something. This is very important. If the people are educated to communicate what’s happened in their community, we can really use the crowd-sourcing afterwards. If you combine the real needs in crisis and the offers existing, then you can solve the problems much faster” Tempestini declares passionately.

Matteo Fortini starts his own narration about the initiative: “It was in the morning of August the 24th when we were watching news about the earthquake. I live in a place which was hit by an earthquake in 2012 so I lived the fact that we need quick information about what was happening and what to do and we talk about that with Tempestini. Both of us were on Twitter and Facebook, on social media. We were seeing that on Twitter, I found that Twitter was a very powerful source of information , there were different sources but not talking at the same time. There was not a single source of information to find together everything related to the earthquake, on social media. Tempestini opened very quickly a Facebook group to try to share information in a more organized way and after that we followed with Twitter,Telegram and everything else”.

In just three months since its opening Terremoto Centro Italia.info became an established initiative in Italy.

“My closest friends were more on-line than off-line. They jumped on the project and started helping in the ways they were able to. One of the strengths of this project is that everyone contributes on what she or he can do best. There are people taking care of social media, others writing code and whatever else. I had friends that helped me a lot, not virtual, but real friends that I connect through social media that helped me a lot” Fortini explains in an interview I made with him on November 20, 2016.

Even in countries which developed sophisticated systems for emergencies, a multiplatform that grabs and organizes information can give great assist:

“We have in Italy a Civil Protection (Protezione Civile) which is very well organized, but they need to take very clear steps in order to help. When the earthquake happened media like TV crews were going to interview persons under rumble and they were telling people to call emergency numbers, but the telephone could not run as efficiently in the very first moments, as telephone lines were extremely crowded.

We thought in that moment Social media as a way to share information and to share issues that we were finding. Redundancy (duplicating things in order to increase reliability) is the best in these cases. We don’t want to overtake any other channel of communication but we want to try together and to get a redundancy about the different sources of information in order to get the information to the right place as quickly as we can” Fortini said.

How they got connected to local people? Fortini gives an example:

“We met some people from the affected area, very young guys which formed the association Chiedi alla Polvere / Ask the Dust, very young people with interest of staying there and helping people in that place which I think is very meaningful”.

“Around 20 persons help as volunteers every day but altogether approximately 50 people help when they are asked” replied Fortini when asked on November about the number of persons involved.

NOTE: Product or corporate names may be registered trademarks or trademarks and are used only for identification and explanation without any intention to infringe


17
Dec 16

Hashtags, algorithms, ethical rules and regulation

by K. Tatakis

Competition sometimes is over hashtags in social media. Which hashtag will attract more users for a determined event?

Thus the more important the event is the most gets fragmented between hashtags in the social media world. An example is the recent (November 2016) earthquake in New Zealand when at least 4 hashtags like #nzearthquake , #eqnz , #nzquake , #earthquakenz appeared on Twitter next to more traditional ones’ as #earthquake.

Discussion other how algorithms influence what you’ ll see on your screen (and eventually your favorite search engine’s first pages (Meikle, 2016, p.78) is a hot and trendy topic with a variety of opinions about it.

Hashtags and algorithms, Design by K. Tatakis, December 2016

Continuing my review on Meikle’s recent book (Meikle 2016), I will stay now on users’ behaviours and users’ tiredness of exploring for long the same topic.

Meikle suggests that the continuous news flow over a certain subject, sooner or later makes the user unable or unwilling to follow developments (Meikle, 2016, p.75). There are of course are e-mail notifications, rss feeds and other tools to help people dealing with augmented flows but to my opinion users are feeling a psychological fatigue and a sense of burn-out, when constantly following the same subject. Thus it is more up to researchers to follow at long term a story than to everyday users which tend to change preferences when tired or when there is no immediate resolution over a problem.

Ethical rules: Some obey, others not

Meikle successfully questions over the significance and the importance of ethical rules on the web. Some obey on ethical principles, but often the same rules are heavily violated by individual users or even by organizations (Meikle, 2016, p.79). The advance of Syrian refugees in the heart of Europe was an example of this. Kids’ privacy was disregarded in some cases on visual documents about it…In a traditional news environment there would have been probably a debate over the need of publishing such pictures and the possible intervention of press syndicates in such a debate. Once now this is done individually by citizens – journalists and others, we are entering a new era regarding ethical rules and this landscape is is often full of new controversies.

Such a conclusion is close to the question pointed out Read, Taithe and Mac Ginty which in a recent article pointed out that “it becomes potentially problematic if technologies become naturalised and mainstreamed to the extent that they are not subject to fundamental questioning, or they exclude methodologies” (Read, Taithe, Mac Ginty, 2016, p.1325).

As traditional media get weaker and numbers of professional journalists working on night shifts reduced, the demand for 24/7 news is not always responded from such media as rapidly as a user would like. That is something that articles about last August earthquake in Italy reveal (Neri, 2016), (Minzi 2016).

The other side of the coin in this era is what Meikle calls “Being first counts more than being right” (Meikle, 2016, p.83).

Meikle’s book gives us some fresh views over an ongoing phenomenon which is social media influence on societies and every day’s life, an influence which is more significant and omnipresent than it ever was. “Communication, sharing and visibility” (Meikle, 2016, p.iii) are now more global than ever with the pros and the cons of such an expansion. “Raising public awareness” as Fuchs (2014, p.261)suggests about data handling and privacy problems could trigger a fruitful discussion over it.

A development of social media and new media for the social good will ask for new regulating schemes sooner or later, as technology only now start to mature. Will such regulation agent be social approval, self regulation, a public & experts commission, or eventually a new technology like

blockchain time will tell cause it’s only now the majority of people realizes how much the world changed over the last 20 years.

References

Fuchs, Christian (2014).Social Media: A critical introduction, London-Thousand Oaks: SAGE

Meikle, Graham (2016). Social Media: Communication, Sharing and Visibility, New York – Oxon, Routledge.

Minzi, Simone (2016, August 24). Terremoto 24 Agosto 2016: la notte in cui Twitter superò la stampa. Retrieved in November 1, 2016 from http://www.araundu.it/blog/2016/08/24/terremoto-24-agosto-2016-la-notte-in-cui-twitter-supero-la-stampa/

Neri, Gianluca (2016, August 24). Il giorno in cui in Italia morì  la stampa. Retrieved in November 1, 2016 from http://www.macchianera.net/2016/08/24/il-giorno-in-cui-in-italia-mori-la-stampa/

Read, Róisin, Taithe, Bertrand and Mac Ginty, Roger (2016). Data hubris? Humanitarian information systems and the mirage of technology, Third World Quarterly Third World Quarterly, 37(8), 1314-1331. DOI: 10.1080/01436597.2015.1136208

NOTE: Product or corporate names may be registered trademarks or trademarks and are used only for identification and explanation without any intention to infringe


15
Dec 16

Emotions, big data archives and new taxonomies

by: K. Tatakis

Emotions not easily traced on Big data indexes, questions over items already missing from our digital archives and new taxonomies voluntarily or involuntarily happening in our digital world is what I will try to explore on this post. Reviewing parts of Graham Meikle’s book on “Social Media: Communication, Sharing and Visibility” (Meikle 2016), I take as an example and eventual case-study every day incidents from social environments which become more and more digital nowadays.

New alternatives, both positive and negative are being opened because of the new apps we now possess in order to solve everyday problems. Smartphones and tablets contributed a lot to the growth of time and scale we stay digitally connected and to the accession of more generations to what is digital communication.

Meikle questions how much previous journalistic patterns like the “inverted pyramid’ are used in this new environment. (Meikle, 2016,p.70). News evolution is in some cases so rapid that describing “when” is not necessarily primordial at least in social media chats, as everything happens in what I would call a prolonged now.

Meikle calls “timeliness” such a trend(Meikle, 2016, p.74).The news are often consumed at almost the same time they are produced. Missing the live development of an event is like missing the opportunity to participate in such a communication debate. It is not any more that meaningful to post something days after, a posteriori. The attention is already transferred somewhere else.

Meikle stays also on another element of the inverted pyramid, the space that “why” can occupy in such a thunderer news coverage.(Meikle, 2016, p.75).

Emotions, Big Data and Taxonomies, Design by: K.Tatakis, December 2016

Why” is not so much covered in a medium like Twitter because of space limitations, but people instead there and elsewhere prefer to describe people or events with adverbs which most commonly have a sense of exaggeration. Chatting through images, the advance of visual communication also sometimes puts “why” aside. Only critical citizens can elaborate meanings, but others just follow what’s buzzy is.

Furthermore it is very hard to be sure about “why” when trying to publish as early as possible. “Details, stories and entire controversies appear as though from nowhere and are replaced without resolution”(Bourdieu 1998 in Meikle, 2016, p.75).

Summaries, influences and buzz without resolution

No item is necessarily more important than others” Meikle notes (Meikle, 2016, p. 70). But the number of clicks, re-tweets or likes creates at the end of the day a new taxonomy of importance of this item compared to other similar items which can influence most people. “The user determines the connections as they make them, through these processes of navigation and search” as the same author suggests (Meikle, 2016, p. 70). The platform managers can also guide the navigation through suggestions, directions (interesting accounts etc. which appear when entering a platform’s “home” page). Even though you can describe that process more help or advice than manipulation, this taxonomy influences choices in many cases.

The user needs a kind of briefing, because the time she/he has to consume news or to explore what’s hapenning is limited. That’s why in today’s media landscape ads about articles or videos and apps like news managers are emerging. News managers offer what is important and thus what’s not. Many mobile phone companies turned on developing news managers, snipping the best articles or videos to watch.

Today we are used to look more infographics which is also can be a review or analysis of fragmented data in a new synthesis. Meikle notices the augmented role of such data in this new era (Meikle,2016, p.71).

The content gets connected with the success of the platform and the ability to grow its users’ base , the eventual stock price (if in a stock exchange) or the ability to attract new investors. If the platform (or the app) fails and goes down, the content disappears, sometimes regardless the value of it.

Of course fragments of these items remain in places like Internet Archive and it’s initiative The WayBack Machine which made a tremendous work of rescue. But in a search about South European digital newspapers in the nineties’ you can see that often just samples and portions (not the whole content of these media) where rescued. A part of our digital past has already vanished in some cases…

New initiatives on Big Data

Will in the future big data collectors maintain every bit of data (or information) produced? The more the base of news producers’ expands, the most difficult will be to safeguard such bases for any reason. Thus questions of choice will rise again. Prices of data storage devices are going down, but on the meanwhile digital data produced are multiplied at a blistering speed.

In Europe new initiatives are taking place in such a context, like Big Data Europe in order to connect new technology opportunities with communities and private companies. Bonding experts and citizens (and individuals’ needs) will be critical to the success of such an initiative. My opinion is that EU should listen more to individuals’ demands (not necessarily only through their representatives, but at a personal level) in order to remain popular.

Initiatives like The Linux Foundation’s summit Apache Big Data Europe” in Spain, #ApacheBigData also took place recently. But the big challenge remains. How to categorize things not expressed either visually or verbally?..

As I was typing this thought I found out that new projects come in life in this domain. Like the EU funded Mixed Emotions, a project managed by Dr. Paul Buitelaar and coordinated by the NUIGalway university. Scientists have already acknowledged the need to combine emotions and big data in order to create better understanding and I think that gives signs of optimism about the future.

References

Bourdieu, Paul (1998) On Television, New York: The New Press.

Meikle, Graham (2016). Social Media: Communication, Sharing and Visibility, New York – Oxon, Routledge.

NUIG – Dr. Paul Buitelaar (project manager)(2016). Mixed Emotions http://mixedemotions-project.eu/

NOTE: Product or corporate names may be registered trademarks or trademarks and are used only for identification and explanation without any intention to infringe


14
Dec 16

Big Data in healthcare: Dr Jekyll or Mr Hyde? Part II

Part II: the bad Mr Hyde?

By Athanasia K.

In a previous post, I have tried to show that Big Data applications could offer innovative and effective solutions towards better healthcare services.

Despite the very many promises however, Big Data applications in healthcare are not a panacea against all evils, but could also result in negative impacts with challenging aspects. And these challenges are still out there, unresolved for years now, despite the exponential technological development of the field.

In developed countries, one of the biggest concern seems to be the protection of personal data, which is even more sensitive when this is medical data. Kaplan very rightly notes that “data can be sold and replicated anywhere and, once sold, may be used for good or ill”. Furthermore, as Lunshof et al have showed, with the current IT technology we have, privacy and confidentiality can no longer be guaranteed. On the contrary, when we are dealing with the analysis of genetic samples, the re-identification of data samples back to their donor is more than possible, as Malin et al showed some years ago. But even if indeed there is a way to security and totally anonymise the samples in a genetic database, this can limit the usefulness of the data, as showed by Budimir et al.

One more point of concern on the impact of Big data in healthcare is that not all data are reliable. In fact, “people change their behavior and withhold information in order to protect their health information privacy” and  “according to a 1999 survey, nearly one in six patients withheld information, provided inaccurate information, doctor-hopped, paid out of pocket instead of using insurance, or even avoided care” as Kaplan notes. This has lead experts to fear a GIGO effect (e.g., garbage in–garbage out), and to a questioning of the reliability of this methodology to vulnerable groups and poorer regions, as also analysed previously by Shahin. However other scholars such as Alemayehu argue that “although much of real world data is sparse and a lot of the data is ‘‘dirty’’, with proper analytical, computational and data management tools, it is still useful and can support health policy decision-making”.

Adding to this conundrum of confidentiality vs usefulness, the lack of transparency in the acquisition and ownership of the data also adds more question marks in the field. It is common practice that Big data vendor companies do not disclose their contracts on the acquisition these data. As Kaplan notes, the legal framework in the United States and abroad ”does not address health data ownership clearly; it is not clear who the owner should be … Furthermore, it is also not clear where those who sell data analytics services obtain the data, or how they might use them.” Furthermore, as Kaplan continues, “vendors often consider their contracts intellectual property and do not reveal these and other contract provisions”.

But who benefits from this?

One could very logically assume that the companies involved in Big data do gain some sort of profit from this business. But what about the rest? As Kaplan notes, “the cost [of data gathering] is passed on to patients and payers, whether private of confidential. These individuals gain little benefit from the aggregation and sale of data about them, and they may even be harmed by it”. Indeed, Kaplan continues, “patients can be harmed when data about them are violated: to deny employment, credit, insurance”.

This unbalance of the distribution of benefits is more evident when we look in developing countries. As Rudan et al note, nearly all biobanks (at least back in 2011) “have been developed to address the health problems relevant to the minority of people living in wealthy countries”.  This has caused reluctance in developing countries to share their national data or permit foreign researchers to access them, in fear of exploitation. An example to illustrate this better is the one cited by Staunton and Moodley, where in “2007, Indonesia refused to share its H5N1 samples without a legally binding agreement which addressed among others, benefit arrangements and intellectual property rights”.

Apart from the benefits’ unbalance, one more real concern regarding data collection in healthcare is about the possible stigmatization of the patients in case the confidentiality of data is breached. This has been reflected even in court cases, where, as cited by Staunton and Moodley, in April 2010 the Arizona State University paid 700,000$ to the Havasupai Indian tribe as a settlement against claims of an improper use of blood samples which stigmatised the tribe. This fear of stigmatisation is also reported on African studies, where research participants fear about discrimination and possible stigmatisation of themselves and their family (see again the Staunton and Moodley paper). This aspect is more difficult to tackle since cultural differences make the analysis more difficult. As Kaplan notes, what is considered as very private, embarrassing, stigmatising, or posing grounds for discrimination varies among individuals and groups, and also differs between cultural backgrounds, places or time periods”.

But is it all that black and white, Dr Jekyll vs Mr Hyde situation when we speak about Big data for healthcare? In a forthcoming post, I’ll try to maybe find a third way of looking at this.


14
Dec 16

Big Data in healthcare: Dr Jekyll or Mr Hyde? Part I

By Athanasia K.

Big Data applications in healthcare is probably the field with the most heated discussions about the controversial impacts of this new technology. In fact, I could bet that there are not so many other discourses in this field where there is such clear contrast of benefits vs harm, individual vs common good, public vs private, data identification vs identity or last but not least, a contrast between the virtual vs the real, as Kaplan also observes. It’s like an old spaghetti western, where after a closer look in the plot we realise that what is a “good” and justified against a “bad” and condemned behaviour, really depends on the observer. I’ll start with the positive part:

Part I: the good Dr. Jekyll

According to Ya-Ri Lee et al “the field that shows the most promise among the application areas of Big Data is the medical sector”.

As Alemayehu lists in a recent paper, in the context of healthcare Big Data includes “not only electronic health records, claims data but also data captured through every conceivable medium, including Social Media, Internet search, wearable devices, video streams, and personal genomic services; it may also include data collected from randomized controlled clinical trials (particularly when dealing with high dimensional data, including genomic, laboratory, or imaging data)”. And all this vast information could be exploited in different applications.

In epidemiology, Big Data analysis’ applications can indeed offer innovative approaches in communicable diseases’ outbreak investigations, adding useful tools for more effective and cost-efficient ways to prevent and manage outbreaks. One such example is a study by the Karolinska Institute and Columbia University in response to the cholera outbreak in Haiti, where researchers have used data from mobile phone providers in order to have a better overview of population movements, and thus plan a better and more efficient action plan for managing the outbreak.

The positive impact of what Big Data has to offer is probably even more visible in the field of human genetics which traditionally had a rather slow progress due to the nature of the experiments needed to prove the field’s theoretical models (most of the experiments could not be performed due to ethical concerns). However, following the sequencing of the human genome at the beginning of our century, a brave new world has opened for human geneticists since a vast volume of raw data waiting to be analysed. Terms like “computational biology and medicine” enter the medical students’ curricula, and at least basic knowledge of database and system analytics is now a must in the modern bioscience researcher’s armory.

The genome-wide data analysis could indeed identify the causes of rare or other serious hereditary diseases, which would otherwise be difficult to identify and investigate because of their rarity. For example, the analysis of the Icelandic genetic database led to the identification of genes linked to human diseases, as cited by Kaplan.

Moreover, as Alemayehu notes, Big Data and the use of biobanks are very useful in drug development and they open revolutionary possibilities in the development of more efficient and safer drugs, in the direction of a completely personalised medicine and patient care. Furthermore, the use of smart mobile phone applications (like e.g. apps which measure the blood pressure via a smartphone screen) provide new field of direct-time monitoring of patients, as well as healthy persons, which provide again a unprecedented level of statistical information to researchers.

Apart from the science-related opportunities however, Big Data applications in healthcare could also lead to the reduction of costs. As Kaplan notes: multiple healthcare professionals, payers, researchers, and commercial enterprises can access data and reduce costs by eliminating duplication of services and conducting research on effective care.  In other words, Big Data is good for the business too, since healthcare organisations may benefit financially by selling medical records of their patients, at least in the US context described by Kaplan.

By browsing on tech-related articles, blogs and webpages one could find even more current, or futuristic applications of Big Data which will make our lives easier, safer and healthier.

But what’s the price for this? I’ll try to analyse some of the negative aspects of Big data applications in healthcare in my next post.


12
Dec 16

Big Data: from an omnipresent servant to an omnipotent master?

By Athanasia K.

Not so far ago, just in 1995, less than 1% of the Earth’s population were using internet, whereas today more than 40% has access to internet according to internet live stats. This number is constantly increasing and the broader and broader use of mobile phones, social networks, RFID tags, geolocation systems or interconnected personal or public monitoring systems (see also the internet of things for a glimpse into the future) all add to the omnipresence of information and communications’ technology (ICT) in our lives.

This exponentially fast rate of new ICT developments brings the average citizen also exponentially faster in front of new realities. New “tech” services, products words and jargon enter our lives on a daily basis and quickly become part of our reality, as if we were always twittering our news, using satellites to find our way to the supermarket, or instantly send ing pictures of our new-born cat to all known and unknown online “friends” of ours.

And if we consider that all these IT activities leave digital traces, the volume of the IT data and traces which are produced globally on a daily basis are almost impossible to conceive for our human brain. These pieces of data as single points of information might be insignificant. However, as compiled sets of data are more and more valuable for exploitation. As Boyd and Crawford note, the value comes from the patterns that can be derived from making connections between pieces of data, about an individual in relation to others, about groups of people, or simply about the structure of information itself.

This is where another jargon phrase is entering our lives: “Big Data”. This is a term which is not clearly defined, but is used to broadly describe a result of this exponential/massive increase of data gathering, their storage and their analysis capabilities as Hilbert notes in his paper.

ICT gurus and bloggers see Big Data as the new promise land which will bring innovative solutions to almost every problem in our human societies. From our personal daily routines, to innovative solutions for decision making in national and international development policies, there is something for all in the Big Data universe which promises to make things easier, cheaper, more efficient and reliable. Furthermore, milestone IT companies are aggressively promoting the use of Big Data as a tool for the benefit of our society, and which would in parallel also open new markets for the company, as the perfect win-win situation.

But is this a true wonderland?

One important concern is that Big Data is not necessarily open data. On the contrary, more and more companies are doing business in buying and selling databases of personal information, including consumer preferences and behaviour in social networks. A practice which poses considerable concerns on transparency of the transactions, respect of privacy of the data subjects, as well as about the ownership of personal data and their exploitation. This is more valid in particular for the health sector, where in black markets  medical record information is reported to be considered as more valuable information for identity theft than stolen credit card numbers are.

Furthermore, Big Data is not necessarily better data and there is a high risk of what is called “GIGO” in data analysis, that is: “garbage in-garbage out”. As Stefaan Verhulst of Markle Foundation reportedly said: “perhapsless is more” in many instances, because more data collection doesn’t mean more knowledge. It actually means much more confusion, false positives and so on. The challenge is for data holders to become more constrained in what they collect.

Big Data also is not necessarily more cost-efficient either, not at least for the less developed countries who cannot afford the necessary infrastructure, neither have adequately trained data specialists and analysts to support Big Data activities, as Hilbert notes. And here it comes the question of who really benefits from this new exciting technological opportunity?

Scholars such as Hilbert have raised concerns about a risk of widening the gap in the digital division between developed and developing countries. In addition, Rudan et al have argued for the need to develop infrastructure and personnel capacity in poorer countries and several promising activities are moving into that direction. However, the reality so far is that the challenges for a fair application of the Big Data revolution are far from resolved, as also outlined earlier in this blog by Shahin. This is in particular true for the health sector, on which I will focus in my forthcoming post.


12
Nov 16

Wearables, big data and traffic regulation

Wearables and traffic. Photo and drawing by K.Tatakis, November 2016

By: K. Tatakis

Big data could help citizens in numerous ways and it’s up to our phantasy to invent new uses. In this short post I will work on the subject of wearables, big data and traffic regulation, something very useful globally as urbanization advances rapidly at a global scale. Wearables with GPS geo-localization that will count the number of citizens waiting in a metro station platform could be meaningful to reduce human stress and delays. Such delays are the reason for many missing meetings as employees (and even employers) arrive often late at work due to such disturbances. Big data could give traffic controllers the opportunity to augment the frequency of metros’ in a particular metro rail line until demand from passengers to enter wagons drops. But it is not only trains and subways where such an idea could be used.

It could be used to count passengers in a particular airport or a particular airport desk, to count drivers willing to take a turn towards a big highway and so on (especially when connected with route plans that people have picked in their digital traffic advisor). That will resolve many of today’s problems citizens daily have in large urban areas all over the world, from Shanghai to New York.

A particular scan of data could even calculate the number of social media messages about high traffic in a particular area and thus to help who organizes the traffic to send more buses, taxis or trains to relieve a problem. In such a case an alert level could be set, when for example messages about traffic are more than 50 in a small neighborhood.

In a less developed country where people don’t have enough money to buy these new gadgets, SMS could be traced in order to inform if particular products are available in a small local market of a poor African country. Linnet Taylor and Ralph Schroeder open up very interesting questions about big data in one of their recent articles. They inform about the MIT’s “Billion Prices Project (Cavallo 2013 in Taylor and Schroeder 2015, p.504) which is a similar idea but at a much larger scale.

Will citizens be more willing to provide such data if they can immediately use the outputs of such a survey? I think yes. Where positive results of an action are immediate and more tangible, people will feed such initiatives with more data for their own good.

Many companies that provide GPS orientation programs provide updated information about traffic in big cities. What if all these could be transformed in an all – included program which will combine road, train and air traffic and that will really inform about traffic jams in order to avoid delays. Buying tickets and the opportunity to see tickets availability would make such a program far more desirable.

A big fear of today’s citizen in a big metropolitan area is sudden traffic chaos. I think that people are ready to accept less privacy when outcomes really help them immediately towards a better life quality.  Αfter all improving our lives is  a form of social change.

 

 

blog-pi-header1

Social media, big data and development (Drawing by K. Tatakis, 2016)

A critical question is where data scan and elaboration will be done (Taylor & Schroeder, 2015, p.504). Let’s suppose that a heavy traffic jam hits Mombasa. Are local authorities ready to use such technologies or this will add new costs to the local economy, as technologies and scientists will have to be imported? The two scientists also inform about the dangers of a “top-down approach” ((Taylor & Schroeder, 2015, p.504), where questions (of data surveys) and needs are simply guided by officials both in national and international organizations and are not necessarily what people ask about.

Talking about the digital ethics and the digital concerns over big data was something very trendy during the last ten years. Can we talk about politicization of data in such a field, in a way similar to the one Taylor & Schroeder discuss about over other development issues (Taylor & Schroeder, 2015, p.506)?  My answer is yes, as decisions that will follow the use of these data (the decisions of traffic controllers for example will affect the lives of thousands of people). After all it is a political judgement what you consider high traffic in your polis (city) and the decision you make to inform others on social media. Speculation over the use of traffic control data could affect the elections in a large metropolitan area, if a candidate will blame the current mayor that she/he is not doing enough to press for more metro’s wagons in a particular line. But it will also affect private companies’ profits, positively or negatively.

Reviewing Taylor’s and Schroeder’s article, I can notice that one of the most important questions they pose is “what ‘development’ means to data scientists” (Taylor & Schroeder, 2015, p.508). How were they educated on development (both in class and from the environment they have lived) and how that will influence their choices?

They also pay attention to the fact that not all scientists gain allowance to use big data. They need prior reputation and good connections in order to gain such a permission. Thus only a small circle of scientists could end up on using big data (since big data is not always open data) and the rest will be constrained to do what is a small scale data research or a qualitative research(Taylor & Schroeder, 2015, p.510). The two scientists stay on “power and knowledge asymmetries” (Taylor & Schroeder, 2015, p.516) and insist on education of people about their rights (Taylor & Schroeder, 2015, p.516). When entering what I would call this new marvelous world, the world of big data, you have to take account of such complexities.

 

References

Taylor, Linnet & Schroeder, Ralph (2015). Is bigger better? The emergence of big data as a tool for international development policy, GeoJournal (2015) 80:503-518

 

 


01
Nov 16

Big data in an African context

Shahin Madjidian

Introduction

Many development goals, policies and programs are based on numbers and statistics. How accurate are these numbers on the African continent and can big data help in improving the accuracy?

African statistics today

In his book, Jerven offers a devastating critique over the state of statistics on the African continent. He notes that the numbers being produced and published are neither reliable nor valid, often being based on estimates, guesswork and/or assumptions. Many times, these assumptions are in turn based on older data sometimes dating back decades. These old baseline numbers have very little relevance with how things look today.

Several consequences can be identified here. Firstly, different actors may look at the same old data, but through different angles, and thus produce very different numbers (in Jerven’s case it is mostly about GDP/capita). Secondly, these poor numbers feed into a larger picture of how African countries are depicted, which problems they have and where these can be found, and any possible solutions to remedy them, to develop the nations.

Jerven laments the poor state of the countries’ statistical offices and argues that they are basically there to serve actors from the international aid, donor and development communities (Jerven 2013:105). “International institutions are the main providers and disseminators”, as he notes (Jerven 2013:8f).

Jerven calls for new baseline estimates, from which fresh statistics can be extrapolated and drawn from. However, he stresses that “these must be based on local applicability, not solely on theoretical or political preference” (Jerven 2013:xiii) and also highlights the importance and necessity of local knowledge and input. Data and statistics ought to serve the needs of the people on the ground, not reaching targets for some faraway aid organization.

Big data replacing statistics?

Can big data replace the poor state of statistics on the African continent and help improve public policy and development goals? First, let us quickly go through what big data is and how it works, before answering the question.

In their book, Mayer-Schönberger & Cukier provide us with a clear overview of big data and what it is. They note that “at its core, big data is about predictions” (Mayer-Schönberger & Cukier 2013:11), about inferring probabilities. Furthermore, big data is about finding the general direction, about a trade-off between being accurate at the micro level versus gaining insights at the macro level (Mayer-Schönberger & Cukier 2013:12f). So far, big data seems like a useful tool to use. In fact, big data can be viewed as pure statistics. But which data can be and is currently collected in big data sets?

A lot of the data comes from using various communication tools, such as cell phones and computers, while simultaneously being connected to the Internet. Taylor & Schroeder warn us when they point out that far from everyone use cell phones or is connected to the Internet in developing countries. This results in user bias and a situation where vulnerable or ‘hidden’ populations, such as children, the elderly and the poorest in society are left out in the data collection (Taylor & Schroeder 2015:510f). They argue that “mobile phone use is highly differentiated by gender and income level” in India (Taylor & Schroeder 2016:506), and a qualified guess is that many African countries exhibit the same patterns.

Meier concurs, saying that “not everyone is on social media. In fact, social media users tend to represent a very distinct demographic, one that is younger, more urban, and more affluent than the norm” (Meier 2015:37). So perhaps inferring national probabilities from a rather narrow subset of the population is a fairly poor idea, which will not give a rewarding big picture, as is one of big data’s positive sides.

Quality of analysis

If the previous section discussed the quality of data, this will delve deeper into the quality of analysis regarding big data. In the previous post I briefly mentioned how big data actors mostly are big corporations and governments. What they have in common is that the majority of them are based in the global North, far away from the realities of Africa.

Jerven writes: “In order to employ the evidence usefully, one must know the conditions under which the data were produced. This is readily recognized in qualitative analysis, but somehow these principles have not been applied to quantitative evidence” (Jerven 2013:7).

Read, Taithe & MacGinty are even more pessimistic and question the quality, reliability and validity of data when “field level information may be sent to headquarters in a different country, collated with other data and then sent back to the country of operation” (Read, Taithe & MacGinty 2016:7). They continue saying that there is a risk where people analyzing the data are cut-off from local knowledge and context, only looking at numbers (Read, Taithe & MacGinty 2016:12).

Mayer-Schönberger & Cukier in turn touch upon the very real possibility of a situation where “data-driven decisions are poised to augment or overrule human judgment” (Mayer-Schönberger & Cukier 2013:141). Let us hand over everything to the machines!

Big data the statistical saviour?

Based on the literature reviewed here, this question can only be answered with a resounding no. Jerven complained about the dominance of outsiders when producing statistics and I cannot see how things would be any different if big data actors were to run the show instead of today’s powerhouses within the statistical field. The same objections, such as democratic deficit and out-of-touch with local circumstances, can be raised and more, such as the gender and income gap among users, may even be added.

Big data proponents argue that big data “offers new and higher knowledge ‘with the aura of truth, objectivity, and accuracy’” (Read, Taithe & MacGinty 2016:10). But statistics, be it presented as big data or traditional surveys carried out on the ground, is always subjected to human bias. This is actually something that Meier, himself a big proponent of big data, confirms when he says that everything is biased (Meier 2015:39).

REFERENCES

Jerven, M. 2013: Poor Numbers: How We Are Misled By African Development Statistics and What To Do About it. Ithaca, NY: Cornell University Press.

Mayer-Schönberger, V., Cukier, K. 2013: Big Data: A Revolution That Will Transform How We Live, Work, and Think. London: John Murray Publishers.

Meier, P. 2015: Digital Humanitarians: How BIG DATA Is Changing the Face of Humanitarian Response. Boca Raton, FL: CRC Press.

Read, R., Taithe, B., MacGinty, R. 2016: Data hubris? Humanitarian information systems and the mirage of technology, Third World Quarterly, forthcoming.

Taylor L, Schroeder R. 2015: Is bigger better? The emergence of big data as tool for international development policy. GeoJournal 80: 503-528.


01
Nov 16

Big data, privacy and ownership

Shahin Madjidian

Introduction

Studying big data critically leads to several interesting topics which can be examined and developed. In a string of four blog posts this is exactly what I will do. The first post is perhaps the heaviest as it deals with ownership issues, democracy and whether or not big data can be seen as something revolutionary that will lead to social change.

I will start with a short analysis of the individual’s right to his/her data and then move on to the macro level – who owns the data, who can store it, analyze it or draw conclusions from it, and later act on them?

Privacy

Today, most people in the world leave digital fingerprints as we go on about our businesses, whether we like it or not. This data gets stored and many times analyzed and acted upon by the actors who picked up the data in the first place. The data can be anonymized and used in a big data set where it is very difficult, or even outright impossible, to identify individuals, or it can be used to improve targeted commercial ads suited for a unique individual.

Privacy issues have been raised, especially as individuals have very few possibilities to reject the data collection. Spratt & Baker argue that algorithm transparency is important, as well as the fact that people should be allowed to know which data is stored on them and where. They suggest that “all individuals have the right to control their own personal data, and can choose to sell as much or as little of this as they like” (Spratt & Baker 2016:30). While this sounds laudable, there are several problems which are ignored. How will an individual get access to this data and where can s/he store it? What about situations when the individual requires money or are in other desperate situations and decide to sell data, doing a trade-off between short-term gain in favour of perhaps long-term exposure? Which population sectors in which countries may be most prone to do this?

In reality, as Spratt & Baker note, consumers may object to their personal data being bought and sold, but in reality have very little control over it once it has been collected (Spratt & Baker 2016:12). This leads us to the next section, namely who these holders of data are.

Ownership and “usership”

The main holders and owners of data today are big corporations, especially those in the social media and communication sectors, and government. Often, data collected from actions and events are used to create new forms of value in innovative ways, as “the system takes information generated for one purpose and re-uses it for another” (Mayer & Schönberger 2013:97, 103). The trick is in finding secondary usage of the data and as a result, hidden correlations which may turn out to be highly valuable. Related to the privacy issue, it is very difficult to prohibit something that has not yet happened, to prohibit uses of data which the data owners have not previously thought about.

Read et al. argue that “if the power of initiative, design, funding and analysis still resides with the tech-savvy individuals and organisations based in the global North, then it is difficult to concur with the view that technology is empowering or liberating” (Read, Taithe & MacGinty 2016:12).  This notion is amplified in the global South considering “the growth of private-sector involvement in public infrastructure projects across the globe” (Lovink & Zehle 2005:10), with infrastructure here broadly meaning Internet and cellphone development.

A few huge corporations have taken the lead in the use of big data and to remedy this, Spratt & Baker propose state support to startup companies within the field in order to learn and become more competitive (Spratt & Baker 2016:26).

Are there any possibilities of individuals becoming owners, analyzers and users of big data? Meier certainly believes so, and I will return to his book “Digital humanitarians” in a later post. For now, I will use his own words against him, as he writes that big data can easily turn into information overload and that the data coming in during one of his humanitarian efforts was simply too much for him and his hundreds of volunteer to handle (Meier 2015:4, 50, 52).

It is not only the vast amount of data that makes it difficult or impossible for individuals to use, but also its messiness and complexity. The data comes from different sources, in a wide variety of shape and form, many times unclear and fragmented. The technologies required means that big data use today is limited to a few actors. Individuals, or groups of individuals, are usually not among the lucky ones.

Mayer & Schönberger have a somewhat romantic view of the future development, believing that just like everyone with cell phones has the potential of being a “journalist” in the broad sense, everyone may be able to extract and analyze big data as “tools get better and easier to use” (Mayer & Schönberger 2013:134). It may not be necessary to be a statistician, engineer or software developer working for a government agency or Facebook.

Conclusion

While development may allow more people to become big data users, today’s actors will have a huge head start. Furthermore, Mayer & Schönberger predict that data owners will increasingly be in the most lucrative position in the future (Mayer & Schönberger 2013:134) and as long as the privacy laws are not changed, the data owners will be social media and communication corporations, not individual citizens.

I agree with the somewhat glum view of “although cloaked in an the language of empowerment, data technology may be based on an ersatz participative logic in which local communities feed data into the machine /…/ but have little leverage on the design or deployment of the technology” (Read, Taithe & MacGinty 2016:11).

In many ways, big data is revolutionary and holds great possibilities for humankind, but used within today’s societal and economic logic, it is but a furthering and strengthening of the status quo, with little to none possibility of empowering individuals or inciting social change.

REFERENCES

Lovink, G. & Zehle, S. (eds.) 2005: The Incommunicado Reader. Amsterdam: Institute of Network Cultures

Mayer-Schönberger, V., Cukier, K. 2013: Big Data: A Revolution That Will Transform How We Live, Work, and Think. London: John Murray Publishers.

Meier, P. 2015: Digital Humanitarians: How BIG DATA Is Changing the Face of Humanitarian Response. Boca Raton, FL: CRC Press.

Read, R., Taithe, B., MacGinty, R. 2016: Data hubris? Humanitarian information systems and the mirage of technology, Third World Quarterly, forthcoming

Spratt, S, & Baker, J. 2016: Big Data and International Development: Impacts, Scenarios and Policy Options. Brighton: IDS.


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