Last Post for Semester - Networked Media Production Week 15

Each post throughout the semester, of course, focused primarily on what we learned in the lectures, and any information gathered from our tutorials. The first few posts introduce semantic markup language eg. html, css etc and progress into API's, mashups, Web 2.0, multiplicity and eventually into Datavisualisation. Some of the topics that I found to be most interesting was the concept of tagclouds, as well as looking into the evolution from taxonomy to folksonomy. Ontology and Predication was another topic that I found to be rather interesting. I couldn't find my views for each post, after reading a few other blogs it turns out that you have to sign up to Google Analytics and include its javascript into your websites html, which keeps track of these statistics, unfortunately I didn't find this out until now. However after reading other people's final blog posts, there are similarities between each post, how views are not significantly high. What I feel would have maybe increased incoming traffic to these blogs would be to change the blog requirements. Having a minimum of two posts per week instead of one. You have your one post that discusses what you learned in that week, and the other post can be based on whatever you like,(or morph them into one big post) this would be a good way to share feelings and thoughts and get an insight into different personalities. So when a visitor to one of these blogs reads some posts that they find amusing, they get to know the author a little better, could possibly become attached and spread the word to friends or colleagues, while at the same time consuming information learned in each week. A big trend that I found to be pretty important was the different interests of other students, how the more they elaborated on a certain post shows which topics they are more interested in, and the topics that I found to be most interesting each week, I tended to elaborate more within my blog, these trends are a great example of Networked Media. How different people express and consume information in different ways that fosters into a networked learning environment.

I nominate these three posts for assessment -

week 4

week 6

week 9

Rationale for Networked Media Production Project B

There is an icon for every sport, Tiger Woods for Golf, Michael Phelps for Swimming, Roger Federer for Tennis, Kobe Bryant for Basketball etc. this information is current, because better athletes are being produced over time for these types of sports. Boxing however has taken a slightly different path, since the early 2000's there hasn't been a rise of any boxing greats, unlike other sports. When one thinks of the sport Boxing, sure there have been plenty of boxing legends since the sport first developed, however two boxing kings come to mind, either Muhammad Ali, or Mike Tyson. These two boxers, although retired, are known as two of the greatest of all time. Now when boxing is the subject for conversation, it always seems to find its way asking the same question that's been asked for the past decade: 'Who would Win? So for this project I chose to design a datavisualisation that compares career statistics between Muhammad Ali and Mike Tyson. It consists of three pages:

First page - This page is the introductory page, it has Ali on the left and Tyson on the right, each has a record line that goes down the page, their first fight (top of the page) to their last fight(it cuts off on the first page design but would scroll down the page if it were an actual web page). It is a list of mostly Wins and Losses, there is also a legend in the middle of the page that describes certain fight results eg. Title Wins and Losses etc. Eg. by clicking on one of the results, say a W (Win), information on that fight appears under the boxer's face, in this case I have chosen to use Ali's first Title Win and Tyson's first Title Win, so information on both of those fights appear under their faces. On this page there is also the 'COMPARE' button, after choosing two results from both the lists, hitting the compare button takes you to the second page.

Second page- Hitting the COMPARE button navigates you to the second page. I have designed this page to make it look like a pre-fight pay per view style poster, that takes the results of all the fights leading up to this big fight just like in real life. Again it has ALI on the left and TYSON on the right of the page. At the top it has another legend, this is to compare the boxers, depending on which result you click on (in this case it's the two first Title Wins for both boxers) it will have further information on the two pages. On this page it shows how many times the boxer has been knocked down and knocked out, this gives the viewer a rough idea on how well each boxer can take a punch (this is only for comparison and does not effect datavisualisation which I will explain on page 3) It displays each boxers current record as well as KO's leading up to the datavisualisation fight. It also displays which 'type' of Muhammad Ali and Mike Tyson are fighting, in this design its 22 year old Ali vs 20 year old Tyson, as well as whether or not they are champions at the time. Down the bottom of this page displays two 'PRIME' graphs for each fighter. It highlights the period in their careers of when each boxer was in his prime. Based on Ali's fighting record, he was in his prime five years longer than Tyson, of course Tyson went to jail and made some poor career decisions along the way which could arguably be a contributing factor. The 'PRIME' graphs are constructed based on consecutive wins and losses between the two boxers, Tyson has more consecutive losses and No Contests than Ali towards the end of his career, these are against fighters that he would 'statistically' easily annihilate in his prime . Then there is the 'RESULT' button, which navigates you to the datavisualisation.

Third page - The datavisualisation, the big fight. At the top of this page there is a legend, this is how the datavisualisation is generated. I have used the most important aspects of Heavyweight boxing to compare and ultimately design this datavisualisation. Wins, Knockouts, Years within prime and Title Belts. I've chosen not to include the 'times knocked down and times knocked out' stats in the datavisualisation because I want the design to support the idea that a knockout or a knockdown is caused by three things: Emotion, the other boxer and fighters prime. 1.Emotion does not factor in boxing statistics, so at the end of the day, statistics are mainly there for comparison, it's different as soon as the boxers step in the ring, take the Mike Tyson vs Buster Douglas upset for example, which is considered one of the biggest upsets in boxing history. 2. The boxer who scores the knockdown or knockout gets these achievments put on their stats. It's the other boxers skill (or luck) that essentially determines a knockout, and 3. whether a boxer is in their prime. A boxer out of his prime is more likely to get knocked out, so 'years out of prime' is what effects the datavisualisation. This datavisualisation is to determine, based on 'statistics' and age, who is more likely to win. In the middle of the page is the datavisualisation of the fight. I have appropriately used boxing gloves for the visualisation, Ali in blue and Tyson in red. What the viewer takes away from this visualisation is an instant perception on which colour glove is more dominant. Based on the information in this particular design, I have chosen 22 year old WBA/WBC (two world heavyweight title belts) heavyweight champion Muhammad Ali to fight against 20 year old WBC heavyweight champion Mike Tyson. Based on the datavisualisation, 'statistically' the fight would be extremely even. They both get 10 gloves for each title belt, a glove for each knockout, and a glove for each year within their prime (this is for maturity). So at the bottom of this page I have added that if a colour glove is more dominant by 10 gloves or more, than the result of this particular fight would 'statistically' be more likely to conclude by way of knockout. Within 10 gloves, it would more likely result in a decision, this is a fun way for the viewer to get a sense of realism out of the visualisation for this particular fight result. If for example I was to choose Muhammad Ali's first Title Win and get 22 year old Muhammad Ali, and choose Mike Tyson's last fight (which was a loss), the datavisualisation would be very different. Tyson has 50 wins at this stage of his career, including his 44 knockouts, so based on these statistics Tyson would be more likely to win. However we must also consider that at this stage of his career he is 39 years of age, so based on the 'PRIME' graph, he is 9 years out of his prime, which subtracts 90 of Tyson's red gloves (-10 gloves for each year) from the visualisation, resulting in Ali's blue gloves being much more dominant. So based on the way the datavisualisation is generated, If a 22 year old Muhammad Ali and 39 year old Mike Tyson were to fight, 'statistically' Muhammad Ali would be most likely to win by way of knockout.

Because these are two of the biggest names in boxing history it wasn't too hard for me to obtain their career stats. These two links - http://boxing.about.com/od/records/a/tyson.htm and http://boxing.about.com/od/records/a/ali.htm have a great time-line for both careers. Including date of each fight, fight results, fight opponents and fight locations.

Networked Media Production Week 13

So i used this week's tutorial to research the statistics between Mike Tyson and Muhammad Ali, it is a little hard to find much information on punch percentage for each fight, especially the smaller fights, so I have decided to use the result for each fight, and analyse how many wins by KO, how many losses, how many times knocked down, how many times knocked out. Then for the big fights including each fighter's first title win, because information on those fights will be easier to get. With these fights in particular i plan on trying to find information on punches thrown in each round, how long the fight went for, and how many knockdowns and whether the fighters won their first title by way of knockout or by the decision. Using these 'big fight' statistics we can assume that the fighters were in their prime at the time, which is ultimately the period of their careers that we want to compare. I plan on designing it by having the graph of each fighter on either side of the screen, and the 'big fight' highlighted in another colour, so when you click on it you can have a look at the stats for those fights. I also plan on having a 'compare' button, which will go to another screen with a boxing ring and each fighters stats will be colour coded, maybe something like Tyson red and Ali blue, and the more of either colour that covers the ring will show who (statistically) is more likely to win.

Networked Media Production Week 12

Ok, after this weeks tutorial, i decided for my datavisualisation to scratch the idea about UFO's and focus my attention on boxing. Im STRONGLY considering using a punches thrown to punches landed ratio throughout the careers of both Mike Tyson and Muhammad Ali. Too often is the question asked "Who would win?", so for my assignment I would like to compare these statistics between two of the greatest boxers of all time. I haven't started researching yet but I don't think it will be too hard to extract these statistics because of how popular these retired boxers are, even today. So look at both of their peaks, the percentage of punches landed to how many punches thrown. I will consider the amount of wins by knockout to how many times the fighter was knocked down and out during their careers. And hopefully present it in an interesting visualisation.

Networked Media Production Week 11

This week was all about datavisualitsation- lots of different and cool ways to present data. I have a few ideas on what data I want to present, so far I am considering looking at UFO sightings from around the world. Maybe have light dots on every location of UFO sightings, or have the globe rotating but that would be a little too advanced.

In the lecture Michael was talking about how it's important to present the data in a way where the viewer can acquire useful information, to learn or come to a conclusion about the topic based on the way the data is presented. Which is why I want to sort of focus on a more analytical approach to this assignment rather than present statistics in an attractive way. Except I'm not sure how I will achieve this with UFO sightings. Maybe analyse where most UFO sightings occur to maybe come to the conclusion along these lines:

- If you directly relate UFO's to aliens, then maybe look at the location with the most UFO sightings and see why UFO sightings are occurring more in this region rather than other regions.

- Is it because of useful material that these locations offer, or is it a cultural sort of construct, where there are stronger believers in UFO's and aliens in this particular region, that these people are made to believe that anything peculiar moving in the sky is most likely a UFO.

In my opinion this is an interesting assignment, I think datavisualisation is an important and more attractive way to educate.

Networked Media Production Week 10

This week in our tutorial we had to come up with an idea of an online service that makes use of collective intelligence.

Our idea is based off the same sort of concept that amazon offers. Keeping in mind this idea is pretty futuristic.

Idea – Online, multiplicity, collective intelligence.

Extensive online 20 page personality report, based on the report it creates an application for smart phones, apple product etc that categorises all your interests, sort of like a personal tag cloud. It connects with all major stores, grocery, clothing even car dealerships (assuming that these major companies have geo-tagged products – so the application can direct to you to certain items in the store based on your personal preference). It also takes your bank balance, and weekly income into consideration. It’s function is, when entering the store, e.g. Clothing store, you can go to a rack of t-shirts, tag the shirt and it comes up with other suggestions of other clothing in the store, sort of like when you get a book from amazon, it comes up with suggestions based on your previous purchases. Based on your income, it will also suggest items that you will like as well as within your price range.

The application is free thanks to multiple sponsors who pay money to advertise their business e.g. if you usually get hungry at a certain time, it comes up with suggestions based on your regular food choices e.g. 1pm - 2 dollar slushies at Maccy D’s.

Networked Media Production Week 9

This week Michael talked about how various sources (especially on the internet) can retrieve information about specific events and viewed by users before being released by the media. Now, the internet can provide more information one would need/want to acquire rather than purchase books at a book store, read the newspaper or watch TV etc. Data created by people from around the world, collected and harnessed by various users - Wikipedia is the first place people look to gain basic knowledge on a topic they're interested in, anyone can contribute to articles, information gathered and expressed by many sources and put into the one article. Michael also talked about how people can get information about recent events from Twitter before seeing it on television.

Information can be retrieved and provided in a variety of forms. In the lecture Michael also talked about AJAX, to make web applications more dynamic. - Flickr, Google Trends, Google Maps etc. This site talks about Asynchronous JavaScript and XML and offers techniques to help improve web applications - http://java.sun.com/developer/technicalArticles/J2EE/AJAX/

Web 2.0 is also affiliated with web applications, e.g. Tag clouds - key words that link directly to other sources of information which is a technique used to primarily foster collective intelligence on the web. Tim O'Reilly defines Web 2.0 in his article - http://oreilly.com/web2/archive/what-is-web-20.html - it includes tables that compare applications from Web 1.0 to Web 2.0, simple directories on the internet (taxonomy) evolving into tags (folksonomy) being one of the comparisons.

And that's my blog for this week.