Assessment 2 1092 - Distinction PDF

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ASSESSMENT 2 ARTS1092

Render 1 https://www.timetoast.com/timelines/2314508 Using personal data requested from my Instagram, I was able to select an aspect of the data set to render 3 different visuals of the same data. The singular aspect I chose was my Instagram photo data, with this information, my first render is a timeline. The timeline created ranges from Feb 2018- July 2020 showing all photos posted on my feed. The photo data was given in folders labelled e.g. “202007” illustrating the year (2020) and month (07) along with a collection of photos posted in that month in random order. By creating a timeline, I was able to illustrate the interactions with my audience overtime as referenced in the folders. The more photos within the folder dedicated to each month and year, correlated with the number of events that happened within the month. I wanted to represent the surface of data within this rendering, the timeline merely showed my posts throughout time however what it didn’t recognise was the caption and specific dates (in folders). When creating the render I was forced to look at an external source for specific dates due to the software not being able to produce the timeline without dates moreover, the system automatically set out its own dates instead of years as the base of the timeline. With this, the timeline to left out any caption and only labelled each photo according to the title of the folder and used days (1-31) instead of years making it indistinguishable unless the viewer looks at the year at the top. The data initially given included archived posts stretching from 2015, utilising this knowledge i purposely made the timeline only using the posts that were up on my Instagram feed. Inspired by ‘What is visualisation’ (Lev Manovich) I sought out to only show what I wanted the audience to see, excluding “99% of data” i.e. archived photos. Elucidating the sacrifice of 99% of the data to display the 1%, a mirror to Lev Manovich’s’ second principle, ‘in essence, favours certain properties of information and the unfavoured are displayed through other visual means’ (personal week 4 analysis). The other ‘visual means’ is seen in the 3rd rendering, by favouring the photos that were displayed on my feed I was able to showcase this idea. Timelines are an important medium to illustrate important dates/ key events, through using this medium of visualisation, like render 2 and 3, the targeted audience young teens and adults are provoked to conceive that there is more than to what meets the eye. Also based on ideas from the anatomy of the AI system, it combines the aspect consumer consumption and the normalisation of labour but in the sense of comparison and editing. Where young teens would compare themselves to strangers not aware that feeds (combination of all Instagram posts) are manipulated to give the idea of perfection illustrating the idea of

consumption combined with the labour, where individuals spend their time editing the ideal post. By intentionally leaving gaps in my timeline and not incorporating archived data I situated my first render to represent the lack of transparency in data.

Render 2

Render 2 is a colour visualisation of the first photo of my Instagram feed. Created with photoshop, I used the eyedropper tool to pick out the most predominant colour in each photo and filled a 3 x 11 square according to the colour and order placed on each photo. Colour plays an important role in the discrimination of the world around us, it can help us make decisions, cause reactions and control our likes and dislikes. Taken inspiration from Star and Bowker’s’ ‘Sorting things out’, Postcards from google earth (Clement Valla,2010) and Lev Manovichs’ ‘What is visualisation’, I tended to focus on personal classification and distinction on this render.

The render is rather simplistic, lacks structure and doesn’t show much insight on the data at first glance. Its limitations are bound to colour and only illustrate a small sample space of the data. This was due to the data’s inability to distinguish which photo data is actually shown to the public. I designed render 2 to display similar elements to the timeline, to render the 1% [reference to ‘what is visualisation], hence it was imperative to single out which photos were on display when audiences view my Instagram. A major part of Instagram growth is the feed, by creating a colour palette of the photos I was able to analyse how the colours fit together. Render 2 incorporates a variety of colours and shades, each with a set out classification system. The system labels each square with a number and repeats the same number on the same shades e.g. number 1 is blue and all shades that fall under that category are also given number 1. As each shade on the grid was labelled, all information on the was represented by a key and results were counted under a title from most to least with the amount. The categorisation of shades under a singular colour makes it easier for audiences to comprehend, an acknowledgement to ‘postcards from google earth’ and ‘what is visualisation’, through this I was able to demonstrate the “fluidity” (Valla, 2010) of classification systems in labelling visual data. In reference to “No one classification organizes reality for everyone” (Star & Bowker, 1999), I recognise that the grouping of shades and colours can be argued to be placed under different labels. As a result of individual colour discrimination what I may perceive as a shade of purple others may perceive as a shade of blue delineating a barrier of perception. When individuals view Instagram pages one of the key elements in pressing the follow button has a lot to do with the colour palette of a persons feed. Through render 2, it became clear that certain colours, shades and trends attracted audiences to follow and the subjectivity of colour perception highlighted personal preference as I, myself took a liking to one section of the render. As a result of data visualisation, I was able to construct my own colour palette to view what others perceive, illustrating a system classification and distinction based on preference.

Render 3

Throughout all 3 renderings I heavily relied on the concept of infovis, “mapping between discrete data and a visual representation” (Lev Manovich, 2011) as my data embodied this concept due to the nature of the chosen set (photos posted). Render 3 displays the 99% of “We throwaway 99 percent of what is specific about each object to represent only 1 per cent – in the hope of revealing patterns across this 1 per cent of objects’ characteristics” (pg. 4, Lev Manovich, 2011). The data requested from Instagram provided the information of all personal photos from 20152020, but what it didn’t take into account were archived photos, photos that were not public anymore. In render 1 and 2 I emphasised the concept of the 1% to highlight what I envisioned for render 3 — the combination of the neglected archived data. Using a graphing software called ‘visme’ I

was able to articulate archived data into “other less important properties of the objects are represented through different visual dimensions” (Lev Manovich, 2011) through a bar graph, line graph and scatter plot. I chose to represent 3 types of graphs in my render to express the multifaceted of ways information is able to form, highlighting the dependency for a quick and easy way to view data and provokes thought into what type of information is suited for each graph. The bar graph is the easiest to read, the bars allow readers to quickly correlate numbers to the coloured years, on the other hand the line graph requires more analysis as the years and numbers align and overlap each other, finally the scatter plot instigates the most thought — the dots don’t align and our eyes often get lost finding imaginary patterns. Though the graphs comparatively are the same with the exception of the type of graph, it illustrates an important sense of perception and interpretation of data. I was able to draw on this perception and interpretation of data based on the idea of “infovis”, articulating the 99% left over from render 1 and 2 in render 3. As previously stated that render 3 is heavily based on “infovis”, I also drew inspiration from “practical politics” (star and Bowker, 1999) ; “the practical politics of these decisions are often forgotten, literally buried in archives [Instagram photo archives]”. This concept enabled me to situate my creative decisions “along the way to decide what would be visible or invisible within the system [render 1, 2 and 3].” Instagrams’ collection of [requested] personal data presented me with overwhelming amounts of information, consequently, i only picked a piece of that information and broke it down displaying the 99% and 1% of that chosen data completely neglecting other personal data. In hindsight the aspect I chose was only 1% where the rest of the 99% were the data that weren’t chosen i.e. likes data, direct messages, comments, etc., but the process allowed me to easier explore other means of portray information with creative license. All 3 renders were all intended to implicitly flow together, not as a ‘seamless illusion’ (Clement valla, 2010) but as a demographic of photos elucidating the nature of individual preference, inaccuracy and extrapolation of visual data.

Ref er encel i s t Bowk er ,G. C.andSusanLei ghSt ar( 2000) .Sor t i ngt hi ngsout :cl ass i ficat i onandi t s consequences.Cambr i dge,Massachuset t s:Mi tPr es s.“ Noonecl ass i ficat i on or gani z esr eal i t yf orev er y one”“ t hepr act i calpol i t i csoft hesedeci si onsar eof t en f or got t en,l i t er al l ybur i edi nar chi v es ” .al ongt hewayt odeci dewhatwoul dbevi si bl e ori nvi si bl ewi t hi nt hes y st em" . Cr awf or d,K.andJol er ,V. =( 2018) .Anat omyofanAISy s t em.[ onl i ne]Anat omyofan AISy st em.Av ai l abl eat :ht t ps : / / anat omy of . ai /[ Accessed2Aug.2020] . Manovi ch,L.( 2011) .Whati svi sual i sat i on?Vi sual St udi es,26( 1) ,pp. 36–49. “ mappi ngbet weendi scr et edat aandavi sual r epr esent at i on” “ Wet hr owaway99 per centofwhati sspeci ficabouteachobj ectt or epr esentonl y1percent–i nt he hopeofr ev eal i ngpat t er nsacr osst hi s1percentofobj ect s ’ char act er i st i cs ”“ ot her l essi mpor t antpr oper t i esoft heobj ect sar er epr esent edt hr oughdi ffer entvi sual di mensi ons. ” Val l a,C.( 2010) .Pos t car dsf r om Googl eEar t h|Post car dsf r om Googl eEar t h. [ onl i ne]www. post car dsf r omgoogl eear t h. com.Av ai l abl eat :ht t p: / / www. post car ds f r omgoogl eear t h. com/[ Accessed12Aug.2020] ." flui di t y” " seaml essi l l usi on" . www. col or mat t er s . com.( n. d. ) .Col orMat t er swel comesy out ot hewor l dofcol or : Symbol i sm,desi gn,v i si on,sci ence,mar k et i ngandmor e![ onl i ne]Avai l abl eat : ht t ps: / / www. col or mat t er s . com/ #: ~: t ex t =Col or %20pl ay s%20a%20vi t al l y%20i mpor t ant [ Accessed14Aug.2020] ....