The Human Face of Big Data

I’m imminently about to start my PhD in Web Science, beginning with the MSc. I’m a big ball of nervousness, excitement and the feeling of actually having the energy to do research that I’ll be interested in. One of the bi-products of this however, is that far more people are asking me about my course.

Generally I give them a brief description of how the web has effected things, and will continue to effect things. Often, this confuses people.

I used to get this a lot with my degree in Geography too. People say things like ‘All I remember about geography is Oxbow lakes LOL, what do you actually study? Colouring?’.

This really grated me for a long time, because geography is the study of how everything relates to one another. By the time I had finished my degree, when someone asked what I studied in particular I simply used to repress an eye-twitch and shout ‘Everything! Geography is everything!’ and then skulk off to get a drink.

Consequently I’ve begun to explain things regarding my new MSc and PhD in a way you would justify learning a maths equation – by giving it a context in which it can be applied.

So here are some excellent examples of crossovers between Geography and Web science that I’m interested in researching further. The first one is human-based, the second one physical-based, and the third is a little bit about my other posts:

‘The Human face of Big Data’ is a term used to describe and explain the human nature behind data. It addresses the growing desire to understand what data can tell us about ourselves, and why we use it in the way that we do. It is commonly used in the study of Social media such as Facebook and Twitter, and how human usage can reveal insights in to real-life trends that make up who we collectively are: our cultures, our mindsets, who we favour or disfavour, and much more.

For instance, analysing billions of Tweets helped two researchers record new insights about public health issues and the way disease is spread. Further to this, they were able to tailor specific health warnings, targeting selected sites that the data indicated to them were the sites of choice most individuals would encounter. As a result of the increased connectivity of our culture, metrics and city structures, people were able to spread information regarding health warnings far faster via social media than they would have relying on previous methods (i.e. by watching the news, or by word of mouth).

Additionally the same analysis and smart application of data can be done with physical hazards as well: change out ‘disease’ for a ‘natural hazard’, for example an earthquake, and the same kind of responsiveness of social media can be recorded to similar effect.

The interesting development with using social media as data streams is that one can ask how and why a population may be affected by these notifications. How can the data be used in prediction systems and 3D modelling of communities. It also lends to the development of maps, virtual realities and other visualisations to adapt to different mediums and scholarly discourses.

Producing the most detailed and applicable results is another substantial task, as one is effectively waiting for a natural disaster to occur in order to collect the data. Once this happens the applications it may be used for are endless, and there are certainly other big data sets other than social media that may help to answer, or build on, the same hypotheses.

If you have read any of my other posts you will realise that there is also a pretty cool crossover between geography and the gaming industry. Again, big data is used in game development in order to analyse, assess and tailor games to target audiences.

However, the gaming industry is one of the leading developers of Geographical Virtual Environments (geoVE’s), usage of real geographic data in game play, new methodologies of recreating virtual city-scapes, combined with ever improving graphics.

Great examples of this can be seen nearly continuously over time as games evolve with softwares. Assassins Creed, Skyrim, Civilization, and Grand Theft Auto to name a few are leading not only in real reflective city-scapes, but also in the virtualization of mapping and other geographic data.

The modification of geoVE’s and VR’s (virtual realities) will be a key-stone in the future prediction of disaster management systems. They aim to do this via transforming the visualisation of a predicted event and tailoring responses to it.

By hosting geoVE’s using gaming platforms and online big data storage, a more accurate and intricate representation of city metrics, human nature and city infrastructure can be mapped out in a way never before achieved.

Certainly, having more information from multiple sources which are able to update instantaneously would be a viable and productive goal in real-time disaster management.

Hopefully this has given you more of an idea of the areas I will be studying, and some of the content that is to come on this blog. Keep Reading!

Featured image Copyright of ‘The Human Face of Big Data’ Rick Smolan 2012-13.

 

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Counter-Strike and DOTA2

Have you ever wondered why professional gamers (Pro-gamers) are dominated by young males? Have you ever considered why they have little age difference, or difference in nationality?

This post compares pro-gamers using the examples of DOTA2 and Counter-Strike, and assesses in what ways they are similar, and how this may be explained using human geographical statistics. So let’s begin…

Counter-Strike is a first person shooter game first released in the year 2000, and was rapidly developed into a series of games with versions ranging from 1.0 to 1.6. Tournaments of Counter-Strike have been running for nearly 13 years, and offer competitive gamers the opportunity to win large sums of money. According to http://www.ongamers.com, the list of top ten pro-gamers of all time by prize money won are as follows:

  • 01. $858,872 – ‘f0rest‘ – Patrik Lindberg (Sweden), 26
  • 02. $798,506 – ‘cArn‘ – Patrik Sättermon (Sweden), 29
  • 03. $777,772 – ‘dsn‘ – Harley Orvall (Sweden), 28
  • 04. $631,251 – ‘NEO‘ – Filip Kubski (Poland), 27
  • 05. $626,251 – ‘TaZ‘ – Wiktor Wojtas (Poland), 28
  • 06. $609,106 – ‘RobbaN‘ – Robert Dahlström (Sweden), 29
  • 07. $597,092 – ‘Loord‘ – Mariusz Cybulski (Poland), 27
  • 08. $593,528 – ‘kuben‘ – Jakub Gurczyński (Poland), 26
  • 09. $531,921 – ‘zonic‘ – Danny Sørensen (Denmark), 28
  • 10. $508,571 – ‘walle‘ – Dennis Wallenberg (Sweden), 27

There are three immediate points we can recognise: all of these pro-gamers are male, all originate from Europe, and all are aged mid to late 20’s.

Similarly to Counter-Strike,  DOTA 2 (Defence of the Ancients) has had increasing availability of prize money and was also produced by Valve Corporation. The game is an online multiplayer battle arena, which was released in 2013 as the sequel to the original DOTA. According to http://www.gosugamers.com, the top ten pro-gamers of all time by prize money won are as follows:

  • 01. $280,600 – ‘Dendi’ – Danil Ishutin (Ukraine), 23
  • 02. $278,100 – ‘Puppey’ – Clement Ivanov (Estonia), 23
  • 03. $277,300 – ‘XBOCT’ – Alexandr Dashkevich (Ukraine), 25
  • 04. $266,490 – ‘LightOfHeaveN’ – Dmitriy Kupriyanov (Russia), 25
  • 05. $240,000 – ‘Ferrari’ – Luo Feichi (China), 23
  • 06. $231,500 – ‘Zhao’ – Chen Yao (China), 23
  • 07. $229,000 – ‘Faith’ – Zeng Hongda (China), 21
  • 08. $221,700 – ‘YYF’ – Jiang Cen (China), 26
  • 09. $216,000 – ‘ChuaN’ – Wong Hock Chuan (Malaysia), 21
  • 10. $204,000 – ‘ArtStyle’ – Ivan Antonov (Ukraine), 24

Once again all of the top 10 are male, but are on average younger than the top ten Counter-Strike players, aged early to mid 20’s. Additionally there are players from Russia, China and Malaysia, which challenge dominent European title holders.

Despite the addition of Chinese, Malaysian and Russian pro-gamers and a shift in age range, little is different between both games top 10. What makes these pro-gamers the best at their respective game then?

Do different nationalities think differently or have different traits? Why do only male players dominate the top 10 in each game? Why are they so closely similar ages?

The first part of these questions can be answered by the availability of the web, and percentage of population that use the internet in the countries of origin of the pro-gamers. The table below illustrates global statistics of internet usage and population statistics (2013).

internetstats

The table above shows that the number of individuals of the population who have access to the internet was 566,261,317 in 2013. This means that nearly 69% of the population has access to the internet.

It is reasonable to conclude then that the ease of access to the internet is not the singular defining characteristic of pro-gamers, as both North America and Australasia record similar and higher percentages of population usage.

Another theory that had weight throughout the early 21st century was that male pro-gamers became dominant due to the effects of gaming addictions. This would manifest itself in a way that allowed the individual to concentrate solely on the game they were playing for long periods of time.

It also speculated that males were able to concentrate on singular aspects for a longer duration than women, who were argued to be better at multi-tasking. Thus, a reasonable assumption for how male players thought differently, and were able to excel in that particular game.

In 2012 this theory was contested by a research paper by Han et al (2012) who used MRI scanning to monitor brain activity between Pro-gamers and persons with online gaming addictions (POGA’s). In this they discovered that there were considerable differences between the two groups: concluding that a gaming addiction did not differ wildly from any other type of addiction, whereas pro-gamers showed heightened levels of problem-solving regardless of gender.

Alongside this there are countless other studies, especially in psychology, that produce evidence for the balance of genders in problem solving. To put it simply, males are not scientifically proven to be better at games.

One of the bigger issues that may account for the gender divide in pro-gaming is the the difference in embodied work. Women are far less likely to be recognised in a competitive gaming environment, especially in games that are heavily dominated by male gamers, fans, marketing, and judging panels.

Certainly recent ethnographies of the female role in competitive gaming has highlighted the different expectations of female gamers based on Bryce and Rutter’s (2005) call to change the perceived roles within the community.

Taylor et al. (2009) summarise that the dominance of young male gamers is not a result of a specific set of traits held only by certain people, but as the perceptions and preconceptions maintained within a community that most commonly reads female participation in sexualised terms.

Heightened marginalisation of females in pro-gaming tends to focus around games with higher violence and objectification. As such, games like Counter-Strike and DOTA2 not only disparage women, but have a self-sustaining community whose perceptions are difficult to change.

Do you think this type of communal thinking can be changed surrounding DOTA2 and Counter-Strike? How do think it could be done? Why do you think marginalisation exists in the first place?