visualizing social media eating habits

VISUALIZING SOCIAL MEDIA EATING HABITS

Fabián Eduardo Ríos Arias

VISUALIZING SOCIAL MEDIA EATING HABITS - 2. December 2018

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Submitted to the Program in Digital Media, Faculty of Communication andEnvironment, in partial fulfilment of the requirements for the degree of Master ofArts in Digital Media at the Rhine-Waal University of Applied Sciences.

December 2018

Fabián Eduardo Ríos Arias Program in Digital media.

Prof. Michael Pichler Thesis Supervisor

Herr Prof. Dr.-Ing. Ido Iurgel Second Thesis Supervisor


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STRATUM: “a level or class to which people are assigned according totheir social status, education, or income.”

1. Table of Contents

Table of ContentsIntroductionKeywordsA brief history to media analytics studiesSocial media and Inequality

GentrificationPrevious research and projects

Analysing Lev Manovich InequeligramAnalysing Lev Manovich SelfiecityAnalysing the Visual earth project from the Cultural Analytics Lab

Foodiestratum visualising Social media habitsProposalWhat is the food sharing trend?Camera Eats firstSelecting the right tags:Conceptual Frame and Literature theory

On PhotographySoftwareBig Mac Index

VariantsTechnical overview

Data gathering and parsingBerlin and the privacy act

Data analysis and visualisationText based analysis

Technology stackCode review and Documentation

Final ConclusionsFuture researchA Personal referenceBibliography

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2. Introduction

Exploring the camera eat first behaviour in five main cities across the world.

Foodiestratum investigates the typical foodie pic using theoretic, artistic andquantitative methods, the project search similarities and distributions of this kindof photographies by associating rich media visualisations (Image-plots) build byassembling 1000 photos for each city to reveal interesting patterns.

This theoretical essay will discuss this particular behaviour, the influence it exertsin mobile hardware and software industry and summarise and analyse previouscreated project under a similar scope also it’ll clarify the methods and the datasetand come to and end of conclusions and possible future research to be done.

Photography theory inherit the argumentative framework from the 19th and 20thcentury Image exploration dividing the work in fragments and art movements (ex:Surrealism, modernism, etc.) 21st century social media photography includedifferent photo-cultures inside standalone mediums like twitter, facebook,instagram, etc. and this media doesn’t respond to the same uniqueness andspecifications but to the contrary it puts different image and sociologicalbehaviours and trends into play at the same time. The amount of data share for thisplatform is quantify by Billons a day instead of hundreds. Of course the printedmedia and the art categorisations of this almost handpicked curated artists fromthe previous centuries is still alive and has a privileged positions in our society.With digital based media spaces, we have the unique ability to trace back thisbehaviours and analyse in a quantitative way all the actions previously (or Realtime) perform, to be able to research human behaviour across different platformsfor example: Twitter conversations, mobility around the cities, Instagram pics orpurchasing patterns.

This paper aims to be an exploration into the social media photo-cultures byanalysing the camera eats first foodie behaviour, the pertinence previous andfuture studies can have on it and which sociological, economical and spatialanalogies can we determinate around the extracted information formulatingquestions like, Can we perceive a socio-economical or spacial stratificationthrough a social media photo culture?, can we speak about a globalised foodsharing culture?, can we sliced a cultural diet through the photo-culture? In whatdo every city chose to spent? How much it cost the kind of food every culture preferto have and how much this price influence the dietary preferences? Is the cameraeat first behaviour divided into specific areas inside the cities?

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https://www2.deloitte.com/uk/en/pages/press-releases/articles/3-point-5-million-photos-shared-every-minute.html


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The theoretical essay also reviews different previous papers and research projectsthat have approach this problems using quantitive methodologies and strategies;presenting data visualisations of different layers of information. By doing so tryingto highlight what becomes a common ground and which parts introduce uniqueperspectives to the conceptual scope.

The end products generated is a data visualisation series of images or Image-plotsanalysing #foodie pic patterns crossed with socio-economical data on majorsocial media sharing cities around the globe, a website containing a moreapproachable simplified and useful information about the project, and thistheoretical paper.

Final conclusions and different outputs of this project intent to be a reflection intoContemporary cultural and social distribution immerse into the visual social mediacultural pattern (“#foodies”). I hope that in time, the paper and the concludingproducts will enabled interested scholars to gain insight and increase the overallawareness of how different social, economical, base location and user - medialayers transpose over this photo-culture data and affect the output which cannotbe read as a single entity on this media studies.

3. Keywords

Foodie, camera eats first, social media, Instagram, Twitter, urban analytics, urbanscience, urban studies, quantitative studies


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4. A brief history to media analytics studies

Big data has transform the way society takes decisions, most of today techcompanies consider themselves data driven companies and take every decisionfrom finance to design and software development based on centralised based dataanalytics. how did it became so important?

Walter Benjamin is largely appreciated for thinking about the concept of what awork of art is. When the possibility of media reproduction came in place, he startedwondering what even an artist is and what is the concept of authorship. With theintervention of different media and technologies came the power to transformconcepts that we took from granted. Michel Foucault, during the XX centuryanalysed the social structures art was embedded and also try to provide an answerto the transformations which were happening during his century.

Marshall McLuhan wrote during the 60´s the “message of any medium ortechnology is the change of scale or pace or pattern that it introduces into humanaffairs” at the beginning of the XXI century Lev Manovich took their words andstarted to talk about the “language” of the new media and in more recent yearsevolved gradually to a theory about software to be in the heart of our societies andinformation to be the primary raw material to which we enable our cultural andeconomical dialogues in todays contemporary society. So the recurrent paradigmsfrom different societies during the industrial revolution were to be replaced for dataand data processing machines (computers, and software).

The work of Friedrich Kittler was happening during the same time, and wasimportant to be able to also start understanding the media outside of humancapacities. He put another perspective on the media theory by arguing thattechnology has his own autonomy and not necessarily serve a purpose merely asextensions of our own senses and physical reach, “Media are not pseudopods forextending the human body. They follow the logic of escalation that leaves us andwritten history behind it.” their views only got revindicated in the 80´s when thetechnological machines were advances to more easily imagine them growingseparately from our human perspective.

Somewhere between the 60´s and 70´s Most theoreticians agreed that thetransition to an information society started to happen. Even before the computerrevolution there were several economist and sociologist referencing the changesof the era and how that affect the peoples every day life. McLuhan dedicates the

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last section of Understanding Media to data processing and talks about it as achannel and distribution, but besides that computation and even further softwareitself doesn’t play a big role in his theories. It became more clear that when westarted going underway to reach more and more storage and analytics possibilitiesusing computers, the transformation from an industrial world to an informationprocessing world, was happening.

“In 1977 Alan key and Adele Goldberg imagined that a computer shouldbecome a ‘metamedium’ that would contain ’a wide range of already-existingand not-yet-invented media.” Exactly as they predicted, computer have beenused to invent a number of new types of media that are not simulation of priorphysical media."

Today what was once an academic hypothesis can clearly be seen in all corners ofour lives, all white collar professions have been transform to analyse and representdata, our mediation with external outputs as citizens of developed or developingcountries is bind to our personal information appliances like our smartphone orcomputer. To order pizza a taxi or plan a vacation we rely on services that compareprices or calculate the amount of time a vehicle will take to arrive to ourdestination, etc…

NASDAQ the epicentre of our economy which contains the words “electronic” and“computational” in his description since 1970´s, is an everyday representation ofstock Exchange data. Processing information and quantifying internationalcompanies and countries economies playing by digitalised theories, when 2008US. Crisis when down all eyes roll down for answers and many were deceived tryingto explain what was happening conveying information from the digitalised world toeveryday worker type people around the world.

Trying to make sense of all of this information has been the new problem of thisdecade with Artificial intelligence, Machine learning both branches of Kittlerperspective on machine independence, data mining and every time biggerprocessing capacity to ourselves has shift our world from a sociologicalperspective, we understand from the technological media history that we are tiedwith the tools we use to live and in this particular era our data organisationaldevices has come to arise.

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5. Social media and Inequality

“In the last few years, one of the most frequently discussed public issues hasbeen the rise in income inequality (Stiglitz 2012, Piketty 2014, Atkinson 2015).But inequality does not only refer to distribution of income. It is a more generalconcept, and it has been used for decades in a number of academicdisciplines besides economics, such as urban planning, sociology, education,engineering, and ecology. The quantitative measurements of inequality allowresearchers to characterise a set of numbers or compare multiple sets,regardless of what the data represents. In addition to income inequality, wecan measure inequality in wealth, education levels, social well-being, andnumerous other social characteristics.”

The most common quantitative measure of inequality we used today is the Giniindex , it was introduced more than a century ago by the Italian statistician andsociologist Corrado Gini. In economics and social sciences, inequalitymeasurements take the job to quantify how particular aspects are distributedamong members of a determined group. Several examples of such characteristicsinclude income, wealth, health, education, etc. Thinking about all of thiscomponent as resources then we can see inequality as the quantification of howeven or uneven a certain resource is distributed among a group. The results of this,is a decimal number between 0 and 1.

We live our daily routines organise by the thousands or Millions in different spacesor scenarios which carry a geopolitical binding, we introduce ourselves to thisplaces by the name of: towns, cities, countries or institutions which can vary fromclubs, companies, social media platforms, etc. Because we inhabit those places ona common basis, we tend to forget that first of all this places exist as a concept.The idea of what a city or a country is, the institutions or the platforms we makeourselves part of. Forgetting this often misplaces the knowledge that we are ableto actively influence this spaces with our behaviour and actions as well asrethinking the ideas that binds this collective places, under this idea I want toparticularly address social inequalities. In every of this single spaces we createand repeat unconscious inequality driven behaviours, they are hard to grasp in ourdaily habits, we accept certain conditions that do not necessarily work well for allof the participants, or we tend to disregard the behaviour to the point of not eventhinking about the rules we accept when we make ourselves part of any of thisplaces. I’ll call it the “Read the terms effect”. Most of us click on it without thinkingbecause we convey the trust that someone else should take care of the basic rules

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of the platform we are registering with and with this we already start to replicateactions behaving just like the rest without being fully aware of the possibleshortcomings for us or for the rest of the people involved.

Segregation map

“Segregation, or income inequality, is one of the major problems of our society.Residential segregation has long been of research interest in fields such associology, economics, and psychology. But our behaviours are also segregated. Inthis project we want to visualise how human behaviour—like conversations inTwitter, mobility around the cities, or purchasing—show patterns of segregation.Our objective is to allow people to understand the segregation of behaviours intheir cities to increase the awareness of that problem, but also to show how we canaddress potential solutions by using different layers of big datasets.”

How can we address the possibility to visualise segregated human behaviour? Ismaybe easier in the more segregated places on earth but in places like Germanymaybe not that quick to catch the behavioural repetitions that create anunnecessarily stratified society.

One important key of the approach to an inequality map is public space. It containsa layer of invisible barriers we navigate every day without paying too muchattention, for example which street we are able to leave our car outside and whichone we can’t, or which type of light we use.

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Berlin west/east lights, photo Astronaut Chris Hadfield

“In 2013, astronaut Chris Hadfield took a photo of the city from the 200-mile-high vantage point of International Space Station, and something immediatelystuck out. To the west, the lights are white; to the East, they’re yellow. Theboundary is a sharp, clear line, mirroring where the wall once stood. Hadfieldtweeted the photo with the comment”

5.1. GentrificationGentrification and segregation go hand by hand and they both have stirringeffects around the globe. Places like the Tenderloin in San Francisco a poordeprived looming community and yet so close to the most highly paid tech workersin the world or Tamarindo bay in West palm beach Florida a completely derail with ahuge rampage in crime and drugs lurking

Area incredible close to Mar-a-lago the highly expensive ostentatious club whereDonald trump expends his days golfing. Downtown L.A has very depressedneighbourhoods all around the periphery of the new booming cafes, restaurantsand co-spaces created taking advantage of cheap rent and distance of high paidwhite collar jobs.

Visual artist Herwig Scherabon create a series of photographies call “landscape ofInequality” showing the huge difference between several U.S cities between theskyscrapers and all the neighbourhood ghetto projects surrounding them.

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landscape of Inequality L.A by Herwig scherabon

After Creating the database and analysing data in this areas the project was able tocome out with significant results to be use in the series.

Analysing latest monthly tweets in the cities related to our visual related culture(foodies) we can see that the places where this photographies get share have adirect representation between and mostly around gentrified neighbourhoods.There was a follow up during 4 different months trying to determine, how much theresults vary but even when some months ratio decrease significantly there wasalways the possibility to see a trend around gentrified areas, local economydistribution and regulations also play a role in why some areas establishthemselves as gastronomical niches.


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6. Previous research and projects

After a series of data mining and transpose visualisation experiments using thelearned methods plus other layers of data varying from usual demographic studiesprovided for official agencies, public API’s, social media and any online dataavailable in which I could find any sociological value, most of the projects analysedwere commissioned for institutions like the New York Public Library and enabled byseveral people dedicated to a specific parts of it mostly on the Software StudiesLab which is a research lab and a design studio working on analysis of big culturaldatasets supported for several universities around the world. being just me, I wroteseveral scripts worth mention later to automatise several part of the work on datagathering and filtering.

6.1. Analysing Lev Manovich InequeligramThis study used more than 7M geo-coded instagram images shared in Manhattanduring 5 months in 2014. The dataset for this project was acquired as part of otherproject call on-broadway for a team of researchers specifically dedicated to gatherdifferent data varying from taxi pick up and drop off instagram images, foursquarecheck-ins twitter messages and household income on Manhattan. the end result ofthat project was an interactive installation which is still touring around the world,and was one of the earliest projects to transpose different layers with digitalsociological value on several platforms and try to visualised in a continuesinterface stream.

Inequaligram and several of the projects that follow after that was a more concretelook almost like having a zoom or a selective cut on the data using differentconceptual scopes.

http://www.on-broadway.nyc/


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local vs tourist: “To visually see the differences in sharing by locals and visitors, weplot locations of 200,000 images randomly selected from our dataset.”

For Inequaligram the project intention was to use economics inequality indexeswith social media data to see if there was any correspondence on the final outputthey extensible use the Gini coefficient to produce inequality results betweenresidents and non residents distributions of social media info, by doing so theyelaborate the conceptual meaning of social media inequality which they define asthe “measures of distribution of characteristics of social media content shared in aparticular geographic area or between areas.” An example of such characteristicsis the number of photos shared by all users of a social network in a given city or cityarea, or the content of these photos, distribution of likes comments, etc.

Table 1. Gini coefficients for hashtags assigned to images by locals and tourists.

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Locals Visitors tourist and localsmeasures Ratio

Gini coefficient forimages

0.494 0.669 1.352

Gini coefficient fortags

0.514 0.678 1.318

Gini coefficient forunique tags

0.467 0.604 1.293


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6.2. Analysing Lev Manovich SelfiecityIn this project selfies were investigated using a mix of theoretic, artistic andquantitative methods, the data collection process is extensively explained andlater will influence my own tool development to compensate for the lack of a teamto do so.

They choose randomly 20k photos from a set of over 600k instagram pics then theyuse Mechanical Turk workers multiple times to narrow down to single preciseselfies and result with a 1k for each city batch, after that they ran automatic faceanalysis, supplying us with algorithmic estimations of eye, nose and mouthpositions, the degrees of different emotional expressions, etc, then they manuallywent over all the dataset and selected a final 640 photos version of them.

Selficity exploratory app

The amount of sociological findings using only quantitative methods is impressiveand very insightful. Been able to corroborate premises just by seeing the data like“People take less selfies than often assumed, only 4% are selfies” or “the averageof a person taking a selfie is 22.7 years” - “there are significantly more women than

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men selfies (from 1.3 times as many in Bangkok to 1.9 times more in Berlin). And 4.6in Moscow.” - “Sao Paulo and Bangkok smile the most almost double than the lesssmiled city”

Even using highly paid amazon Turk workers the proclivity to error in the datagathering and even in the final result is very evident and still unless doing manuallythe libraries used to quantify expressions and face disposition are not advancedenough to perform an acceptable accuracy ratio, being this the case is better tostick to easily quantify parameters, Image plots are render on a black backgroundwhich performs well to be able to being able to read them as a whole as much asread differentiates images between themselves individually.

6.3. Analysing the Visual earth project from the Cultural Analytics LabThis project became important as the first to analyse the growth of image sharingaround the world in relation to economic, geographic, and demographicdifferences. They use a dataset of 270 million geotagged images shared on Twitteraround the world between September - 2011 to June 2014. Exploring 100 citiesaround the world as part of the project there is a lot of relevant information aboutwhich parts of the world we can rely on information and of course is the morestraight forward option to spot inequality on a global scale.

Tweets share globally

The project took good care of planning how to choose the cities to transverseinformation and represent the diversity of urban life today. Choosing by a list thatvaries from size, history, culture, and global importance spotting all five continentsin countries with different levels of economic development. The socio-economicalclassification of this cities was attach to the World Bank distribution that divides allcountries into four groups based on gross national income (GNI) per capita . The15


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final project list has 20 cities in “low-income” countries, 20 in “lower-middleincome” countries, 27 in “upper-middle income” countries and 33 in “high-income”countries.

“We analysed these differences in relation to the level of economic development ofthe countries where our 100 cities are located, and found a systematicrelationship. The lower the level of economic development, the faster the rate ofgrowth of image sharing.”

Watching this trend narrowed the current project search for cities to be able toproposed differentiated areas from different incomes and continents in places withenough share flow of information.

7. Foodiestratum visualising Social media habits

After analysing all of the previous papers and projects I started to see a gap in theway previous projects were thereat and is that they don’t have a close look into aspecific photo culture and also the desire to zoom in inside on particular behaviourdoesn’t see how can it relate to other social media patterns.

7.1. ProposalBased on the previous conceptual scope the project aimed to enclosed the termsjust to work with the following inputs:

1. Twitter food pics with their corresponding geolocation.2. 5 different city datasets of Instagram food pics chose with the corresponding

foodie tags (1k each) (New York, Bogotá, Berlin, Tokyo, London).3. Basic Food prices by city.4. Text and metatags of the post.5. A Global trend dataset with at least 5K images

That way there is sufficient data be able to recognise which sort of food isportrayed (nutrition), where(geography), at what time (sociology) and by who(sociology) correlating all this inputs between each other to get demographicsperspective and cultural, nutrition and economic stratification layers the output isbecame dynamic website of the 5 selected cities where users can see and navigateacross the presented information.

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The cities were chosen based on data from the visual earth project and Numbeodatabase trying to represent the clear majority of segmented demographic areasby continent and middle to upper sharing image percentage, the perspective ofimage quantity itself as a data volume is important to apply a quantitative researchover the whole image collection.

To be able to address and compare behavioural data across different platforms inthis particular study case instagram and twitter we need a binding connection withsimilar accepted rules , this is when setting aside for observation a particularphotography related culture becomes handy, previous studies using big data havebased their approach on single out one particular cultural behaviour to be able todiscriminate better and compare the results if we talk about instagram as a wholeor twitter as a whole, those two single online communities are so big and have somany different and diametrically opposite patterns that we cannot easily refer tothem a single trend but a collection of active micro and macro cultures which growmostly independently inside this different platforms.

Tokyo - San Francisco - New York twitter foodie / gentrification

different cities provide different aesthetics but it will be hard to get any comparisonor stand in an objective point of view if we look at it the data in with geotagged dataas the only filter which in practice means let’s see all of the images produce in aspecific city and compare with other, they have been papers and previous workswitch intend to do this and that I took the time to analyse beforehand, arriving tothe conclusion to create different layers to be able to see this data with a moreattention to details and subtle difference to achieve this goal a preselection of acultural and global narrative will serve helpful in this case the food sharing trend.

7.2. What is the food sharing trend?After instagram was created and got massify as a common denominator ofphotography centred image sharing social media for every day people to share theirexperiences and professional and amateur photographers to show their work atsome point in 2015 people start using hashtags to catalog the information being avast social network the need of some filtering to classify and divide topics was

http://visual-earth.net/

https://www.numbeo.com/common/


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needed then some strong trends start to appear selfies, foodie pics, architecture,etc. the global overview of images shared between the app needed a way tocatalog itself the influence of twitter was also deterministic to reach thismomentum in October 2014 twitter started to allow pictures as a native process intheir app and some trends from other social network.

If we took this common global type of photography and correlated between eachother we can visually explore a cultural trend embed inside photography, in aglobalise world a trend can be digest in their own terms for different local cultures Ipropose to methodologies to explore this photography global cultures one is goinginto a visual exploration of the images itself and the second it’s to compare thegeographical correspondence of this images in large hyperconnected immensecities. Previous works exploring photography and photo cultures as data do so in aquantitative way correlating big data through aesthetics of the traditionalphotography.

Also we can compare the cities vs the global trend to have a media point for whichwe can stand and measure the different cities deviation from the standard in apractical sense all the findings of the global media will serve as a compare ratio,with this in mind I started scrapping from the instagram website 10k from thecollection of 104,001,783M (as seen in December 23th 2018) #foodie images toserve this purpose, after working in the practical data gathering cleaning the globaltrend was a too limiting manual task and it was decided that the collections of all ofthe cities already manually cleaned data will serve the same purpose of being thisglobal trend comparison dataset.

After exploring data using twitter and instagram for 6 months it became clear onlyvery few cities can provide enough data to be able to analyse them, the min.requirements to visually access significant changes will be at least 1K images percity and been able to technically get to the geotagged data with at least 100comparable places a month, using twitter api, this was easily achievable indifferent US cities but outside US having a sample of significant data in othercontinents wasn’t that simple Africa and latin-america was completely excludeddue to the amount of real data obtain in the twitter api to get geo-positionalrelevance was too low, European cities for example the defacto city popular talkabout with a gastronomical point of view, Paris was left behind due to hispractically irrelevance on the amount of data display monthly, I choose Berlinalthough it barely reach the minimum amount to be able to have data in a reachablecity(ab origine).


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this is I’ll say the more obvious and clear form of an explicit segregation even whensome of this social media trends present itself as a global structure the limits ofinternet providers efficient data plans, recreational time and eventually interestdirectly affects the global correspondence this form of segregation

7.3. Camera Eats first

“‘Camera Eats First’ is the behaviour and global phenomenon of people takingphotos of their meals with digital or smartphone cameras before they eat,mostly followed by uploading the photos to the social media. The term refersto how people feed their cameras first by taking photos of their food beforefeeding themselves. derives from professional food photography while thebehaviour of the ‘Camera Eats First’ is generally for personal use such askeeping photographic food diaries instead of commercial purposes. It can alsobe referred as online food photography, food porn and photogenic food.”

for this project we enclosed the behaviour and described as a photography culture,a specific trend inside the massive digital

sharing platforms that have gain relevance by itself as a cultural expression morespecifically a visual cultural expression, this behaviour in particular represent theraw material into which all research is extended, the variants and fluctuations thatinfluence its aesthetical appearance over time is also part of this study.

7.4. Selecting the right tags:This are the fifteen tags selected for the project:

‘#foodie’,‘#foodies’,‘#foodporn’,‘#instafood’,‘#yum’,‘#food’,‘#foodpics’,‘#yummyfood’,‘#geilesessen’,‘#abendessen’, ‘#foodstagram’, ‘#foodgasm’, ‘#coffee’, ‘#cafe’,‘#japanesefood’

They were choose with the help of online services that map the more used tags forspecific topics like travel, family, food, fashion, etc. ex: www.hashtagsforlikes.coplus making a quantitative measure of the comments under the pictures acquirefrom instagram under the hashtag ‘#foodie’.

Different tags point to specific visual cultures in social media identify the ones thatserve the purpose became in the beginning a learning curve plus a trial an errorexperimentation , for example #foodporn has right now (23.10.2018) 176,466,482images associated to it, but most of the images are associated to fast food, heavydesserts, and enormous meals not easy to digest for a regular human, than normal

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everyday meal, also the term just “#food” gives many different varieties of topicsbesides the “Camera eats first” desirable macro that the project is aiming to get onthe other hand specific cities/countries have incorporated different language andslang terms in Tokyo “#japanesefood” is wide spread Berlin has “#geilsessen”,Bogotá has “#desayunando” which has a version of itself also in Berlin as“#frühstücken” most of this words become tags on specific languages (#dinner,#abendessen, #cena etc.) The tags were added according to the correspondenceof popularity and the ones that are less able to introduce images that aren’t part ofthe CEF(Camera Eats First) behaviour

7.5. Conceptual Frame and Literature theoryMassive cities represent and organisational achievement of humanity Urbanstudies have dedicated a lot of time to clarify the way they work as a organisedsociety, in the particular scope of this project and in general for social mediadatasets there is only enough information on main distributed urban areas, maybeone day not so far away that will change but right now all the following conceptualframe is, as the rest of the project delimited by urban analytics.

7.5.1. On PhotographyThere has been photographers working representing and embedding itself in thecities mostly since the 60`s Edward Ruscha map every Building on the Sunset Stripin 1966, an artist book that unfolds to a 8.33 Mts. Continues show which align withthe ground base of the information.

Every Building on the Sunset Strip by Edward Ruscha (1966)

Joel Meyerowitz, famously documented touring around the city with his 35mmcamera the New Yorkers everyday life, like him several photographers which weresomehow involved with photo reportage or design agencies took their equipmentto the streets and started photographing micro cultures, crime scenes or particularrandom places, the shift of information was also providing a shift on creating evenwhen now we spent so much time organising and trying to convey sense to data we


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haven’t stop been producers. Garry Winogrand Bruce Gilden, Jeff Mermelstein,Bruce Davidson, Martha Cooper, Rebecca Lepkoff started blurring the linesbetween art, photojournalism, design and photography.

Susan Sontag published on the late 70´s a series of essays On Photography. Onthese essay she started to theorise about the democratisation of photographyeven when she couldn’t imagine a world like ours in the developed country when somany people has access to a camera all the time she attribute the success ofphotography to a highly labor organise nations like Germany, United States orJapan, as a substitute from work (productive activity) when people was going onvacations. She was perceiving a substantial boost of photography outside of whatonce was a special profession or hobby only accessible for few people in thedeveloped countries.

Today photography has extend itself way beyond of Sontags dreams but even todaythey are privilege rules to be play, having access to a cellphone with camera (whichdiffer a lot according of what people can afford) and been able to sustain a Internetprovider plus having enough amusement time to dedicate to yourself and the basicdigital literacy to be part of the ongoing trend and communication.

7.5.2. SoftwareEvolution of commercial smartphones have serve the purpose to massify the trendin the last decades, today a large group of several devices makes the “food” modea default part of his basic camera functions with out even talking about specialisephotography apps like Snapseed or VSCO the de facto camera of the Samsunggalaxy S8+ just like any of this food effects increase the red and green channels tointensify the colors and increase the overall exposure to a predetermined limit alsocreates a circular (dish shape) focus that we are able to control to create first closeup impact the overall resources are place in order to accomplish an appetizingdefault shot at the end.


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S8 food mode demo

The fact that a culture trend is so popular that permeates the hardware andsoftware we produce and consume is a powerful thought, one we can definitelyexplore further to a very specific level popular photography trends influencescorporations to adjust their devices to it, highlighting a cultural influence in themarket, this fact itself and the millions of time and money the manufactures havespent to make their devices attractive to the cultural photo trend users brings a lotof power to our side, smartphone markets is one of the more widespread marketsin the tech industry affecting millions of people that consume their products, inshort we are responsible for the features and the development of the software andhardware we consume, our cultural trends directly affect the market.


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7.5.3. Big Mac Index

the economist, Big Mac index US dollars

In economics analytics “The Economist” is a well renown magazine, starting inSeptember 1986 Pam Woodall introduced The Big Mac index as a colloquial mannerto measure the differences between two countries to acquire the same productusing they respective currencies. And this semi-humorous illustration ofpurchasing power parity(PPP) has been published by that paper annually sincethen. with a list of prices of basic food and the Big Mac Index there is aneconomical note to point out comparing the amount of times a specific productwas mention in the dataset between the different cities plus calculating thespecific cost it has in the city in particular then we can talk about collective foodpreferences by city and the cost of this particular diet.

One suggested method of predicting exchange rate movements is that the ratebetween two currencies should naturally adjust so that a sample basket of goodsand services should cost the same in both currencies. In the Big Mac Index, thebasket in question is a single Big Mac burger as sold by the McDonald’s fast foodrestaurant chain. The Big Mac was chosen because it is available to a commonspecification in many countries around the world as local McDonald’s franchisees

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at least in theory have significant responsibility for negotiating input prices. Forthese reasons, the index enables a comparison between many countries’currencies.

7.5.3.1. variantsA Swiss bank expanded the idea of the Big Mc.Index including the amount of time alocal worker should work to be able to buy it.

There are several variants of this index visual representation the same journal andother economics reviews has do so with Starbucks coffee also Bloomberg L.P.made the same index comparing a specific IKEA bookshelf.

7.6. Technical overviewFacebook acquired instagram on April 9, 2012 there were a public API to access allthe data from the public and even when it wasn’t possible to trace back post from ageolocation and a tag at the same time for example “give me back all the Instagrampost containing the hashtag #foodporn from the location new york” it was possibleto use a secondary paid service created on top of the instagram API by anothercompany to do so with a web UI to accomplish this results. unfortunately facebookplanned to role a deprecation of this services on July 31, 2018 or December 11, 2018and even more disastrously for all the industry and data research after severalprivacy scandals involving facebook involvement in altering the results of USAelections like the popular Cambridge Analitica case they decided to right awayclose all available endpoints to public instagram users information, on March 2018.

7.6.1. Data gathering and parsingTo be able to parse images not just for the day in twitter but at least accessing thelast month collecting with real Geographical position, the project gain access to aspecial researching API which is able to handle request for a 30 days period of timeup to 250 request per month which will be not enough for even for a low transitwebsite so a cache was placed for the rest API backend calls using the same 15previously specified tags we were able to get about 100 tweets per city exceptBerlin and Bogotá so I’ll threat this as special cases.

I try to avoid running the script around holiday or events that will increase thespecific use of some tags or pattern to arise for example halloween.

For every selected city an amount of 1200 to 1500 images were collected runningthe previously mention scrapper script after that manually editing was made toremove any remaining unnecessary photos that were completely non related to thevisual culture selection like advertisement illustration or selfies from model coming


24

from the #healthyfood, #burncarbs, #fitness photo cultures. Or social role viralsharing trends The final set was 1k photos to allow numerical correspondencebetween all of the urban areas selected.

Every image has an associated list of tags associated to it; author, image url,author name and tag list, part of this related information was used to make a textbased and quantitative analysis of emojis relevance and food preferences by city.

At first there was a collection of 7.000 non geolocated images gathered also for theproject to be taken as a root comparison for all of the cities but cleaning all thedataset would have been a arduous work and the same results could be achieve byusing the same 1.000 images set by city all together.

7.6.1.1. berlin and the privacy actEurope has battle to protect different online platforms users personal data, soonline companies can collect or share this information to their employees or thirdparties under no circumstance even retargeting already active user is a no go foronline markets since the General Data Protection Regulation (GDPR) got in place .This regulations come in place after several years of customer awareness of howvaluable their data is and how also we get to be manipulated using our own dataagainst us for different online companies again the US elections were a decisivewake up call for European and rest of the world governments, while trying to collectdata for Berlin having an accurate geolocation embedded on the tweet had a verylow rate so even when there is thousands of foodie pics share a day in Berlinsomething is possible to confirm by the instagram parsed data the amount of thisthat gets republish in twitter or in instagram by itself with geographical data is verylow, even when using the full archived twitter api the amount of data extracted getsis limited, the amount of facebook and google advertising directly targetingGerman society with privacy concerns just in a personal thought is very large andit’ll be a raw material for another study to analyse if this is also happening in othercountries or is a very targeted type of ads the ones directed to privacy info onEurope or German soil.

18


25

7.6.2. Data analysis and visualisationTo be able to analyse the images gathered image-plot was used as a tool tovisualise the image collections, implemented as a macro which works withImageJ, another open source program for image scientific big data image analysisand transformations.

Taking the main source (the 5k foodie images) as a starting point for comparison,several visualisations of the entire image collections were generated to compareover brightness, saturation and hue.

The radial hue organisation macro was chosen at the end because it allow us todifferentiate in a more intuitive manner several food categories, several nutritionagencies divide food on 4 to 11 different categories the simplest division of all areFruits, cereals(carbohydrates and grains), Meat, Sugary food and Vegetables) thecolors of this types of food in a over simplified idea varied just like the color huedistinction.

Radial increase by hue from the main dataset overview

19

20


26

The secondary option for visualisation was an image montage again organisingall the images in a reticular format this time imitation the same morphologicalquality of the photographies themselves.

Every montage starts with red which get majorly associated with red meat and weget to see a standard predominance of it in all the pictures, BBQ, grills, pork chops,grilled chicken, sausages, salmon, etc. The predominant color of food in all datasetis allocated in the middle dark yellow or dark red range range, it could more likelybe associated with cereals in general (bread, pizza, pancakes, confectionery,potatoes, lentils etc.) the deviation from the standard is very little for all the cities,making it a universal preferred food or at least food photography representation.The green hue data represents vegetables like salads, olive oil, avocado, asparagusetc, on purple there is a combination of desserts and coffee Aldo there is also asignificant amount of background relevance here to take into account for examplethe color of the plate or the surface, Individually Bogotá has a big amount of greenbackground surfaces interference meaning there is a big amount of pictures inwhich the main hue color representation comes from the background and not fromthe food itself.

London, New York, Bogotá

The more liked image comes from Tokyo with a medium high saturation of 200/255and 927 likes. As stated previously on the software section, most of the foodeffects created for different cameras tend to apply a strong saturation on theoverall picture nevertheless we see the majority of images generated today have alow saturation and to the contrary a more highlighted brightness, in the saturationmedia vs likes Image-plot we can see the medium high brightened images performbetter according to likes distribution getting higher values over more than 400 likesper image.

20


27

The aspect ratio from city to city didn’t varied much and even now when instagramstarted to accept different format and no longer constrain their users to a 1:1 ratiothe preference of horizontal 4:3 or 16:9 is palpable over a vertical position.

Another study could analyse the relative perspective historically from completefrontal direction to a more diagonal or orthogonal position.

Historically the representation of food comes associated with the history ofpainting and photography from the still live composition that studied light andcolor more than food itself associations between those practices in the 16th to 19thcentury to our now a day camera first eat behaviour will be hard to trace due to thelack of enough digitalised data but at least one of the reason in several of thisperiods that provide the effort to do so was the desire to represent the foodavailable to consume in a form of status something that is not far away from whatwe can see in this cultural trend now a day, food and more specifically a picture ofthe food as a status valuable object, proof of being able to enter into a restaurant orpaid/afford a specific plate, even when is cooked by the person itself the plate andthe composition is curated into a luxury mode, carefully selecting all the elementsin the composition and illumination to resume into a display of adquisition power.The pictures of restaurants and chef or the images of models eating low caloriefood also fit in this category The other type of picture reflected in the foodie picphotography phenomena is the one more usually associated with #foodporn whichshows a usually high fat and calorie content, or exotic dishes that arouse a desireto eat somehow a food glorification presented as a pleasure comparable with sex,from this trend we can usually see street food enormous sandwiches, side dishes,pizzas, ice cream, and cakes in enormous pieces, both categories are valid andboth represent the camera eat first behaviour, from a different perspective, someaccounts play a role posting photos sometimes from one or the other genre othersare exclusive to one way of representation, there is not a clear distinction betweenbrightness, hue or contrast or likes and a direct influence of one of this categories,at least not one we can perceive with the current image-plot made, we could gofurther and categorise every image into on of this two preferences to see if there isa differentiation, but at the moment there is no easy way to train a computer modelor made a script to recognise the pattern making this study possibility it a lot ofmanual work.


28

foodie categories: foodie status vs foodporn

Color and color on food is something we as a society have pursued to transformaccording to our cultural preferences, artificial food colorants have been sold toachieve desired results on the plate itself and now color filter are release and alsosold to apply specific color configurations to our food pictures highligthing portionsof it, the Hue montage distribution shows a clear color based differentiation, thepreferences of Tokyo for red meat that we can see analysing the comments textcan be also be seeing just by this color separation as a big portion of the overallimage.

7.6.2.1. text based analysisAs stated in the conception of the project parsing the accompanying text andcommentary for every post creates a quantification opportunity to gain insights onevery city photo cultural and contextual differences also basic economical rulesplay a role, how many times a banana (the cities different languages were took intoaccount to parsed the info, banana, banane, ϝϗϗ) is mentioned on the baseddataset and the different cities, how much a banana cost in this different citieseven when huge price differences the representation of it across the cities is veryequal on average 26 times with Berlin been the most 26 and Tokyo the less 4. if wecompare that Bogotá mentions it only 10 times It’s impressive the amount of timeis mention on Berlin when this is not a native fruit geographically speaking and theprice to get one between the cities varies in –53.74% according to the numbeodatabase, making it a clear case of how food preferences overcome geographicalan economical barriers; coffee, bananas the appetite for this products on a dailybasis is bigger than the importance of how difficult is to acquire the product or if ithas a native history with a particular region displaying a economical pattern thatrefers food as a comodity, which is how a bigger portion of the foodie trend threatsfood, as a status statement.

https://www.numbeo.com/cost-of-living/compare_cities.jsp?country1=Germany&country2=Colombia&city1=Berlin&city2=Bogota&tracking=getDispatchComparison


29

This methodologies can be replicated with several raw and primary products andextended even further to work with emojis.

For example

!

(#1 food Bogotá 77), pizza

"

(#1 food New York 90) etc. also emojisand text quantification is correspondent the same city with more hamburger emojiscontains the more hamburger text, same for coffee etc. Also we can quantify lesstangible expression and emotions like

#

fulfilment(#1 Tokyo 235) or joy

$

(#1Tokyo 137), city nation flag

%

(#1 Tokyo 75) On overall Tokyo and Berlin are thecities with more widespread use of emojis on their text also could be referenced asthe amount of commentaries and complete associated text there is on their posts.

From city to city the amount of text shared didn’t vary much on average 600.000characters in total were written in text by city been NY the max. with 693.280 andTokyo the minimum with 507.271 also the amount of emojis or tags generated inevery city corresponds so we cannot speak about a culture than writes more orexpress more in emojis than other at least not with enough relevancy to make adifference.

Analysing the top ten most used emoji by city we see every city has a big share ofunique emojis although they do share some common ground fire

&

emoji is highlyused in every city mostly to represent BBQ or spicy also the cooking emoji

'

which is represented by a pan with an egg, made it to the top 10 of every city indifferent positions but besides this two all of the rest of the dietary-emojipreferences varied significantly from city to city so its possible to visualise thespecific food culture of a city through emojis.

London (

☕

#coffee 472) gets to be the city of coffee lovers with more than doublethe mentions than the rest of the cities on an average of 320 followed by Berlinwith 351, Bogota gets to be the (

)

#beer 50) capital.

(

*

#meat 405) and (

+

#chicken 470) are the most expensive products all thecultures choose to spent their money with a 18.5€ x 1kg in Tokyo and been mention116 times gets to be the most expensive camera eats first dietary preference fromall of the cities.

7.6.3. Technology stackThe development of several visualisation frameworks have contributed to enabledthe graphical representations of data and big data on research projects like thisprocessing , open frameworks, imageJ, D3.js etc. and other R and python librariesare the most visible part of several open source projects that create and toolsenvironment to work with in projects like this. It’s this kind of software and

21

https://processing.org/

https://openframeworks.cc/

https://imagej.nih.gov/ij/

https://d3js.org/

https://www.r-project.org/about.html

https://pypi.org/project/pythong/


30

••••••

•••

•••

programming languages what allows us to go ahead with this kind of studies andhighly influence the possibilities of end results we can achieve at then end of theprojects.

Personally I think several papers and projects lack enough information about thesoftware involved in their process which make access to replicate or continue theprojects unnecessary difficult, even for professional scholars.

Ruby v2.5.0p0 (2017–12–25 revision 61468) [x86_64-darwin16].NokogiriMechanizeOpen-uriJsonSelenium-webdriver

Python v3.6.4Django v2.1.3Emoji library

Vue v2.9.6ImagePlot v1.1ImageJ

This paper was wrote using markdown language an iA Writer template system.

7.6.4. Code review and DocumentationThe website is divided between backend and front end technologies using specificlanguages for different jobs, besides quoting the basic technologies used on theoverall project there are some of them which deserve a specific quote becausethey were writing specially for this project because of the lack of a 3rd party libraryor framework already providing the service.

After instagram discontinued their api services the need of a instagram parser wasraised and therefore the following code got contributed publicly in the followinggist.

!/usr/bin/env ruby

require 'rubygems'

require 'nokogiri'

require 'mechanize'

require 'open-uri'

require 'json'

require 'selenium-webdriver'

https://pypi.org/project/emoji/

https://gist.github.com/fabianrios/6db2612d3382204b6bfe1d870a05f845


31

BASE_URL = "https://www.instagram.com"

id = ARGV[0].to_i

which = ARGV[1]

fl = ARGV[2]

@wait = Selenium::WebDriver::Wait.new(:timeout => 15)

def navigate (which, fl, id)

@id = id

@fl = fl

url = "#{BASE_URL}/explore/#{which}/"

@filename = '/Volumes/External/data/'

@file = "#{fl}.json"

@driver = Selenium::WebDriver.for :chrome

@driver.navigate.to url

p @driver.title

sleep(0.5) \#Half a second

options = @driver.execute_script("return document.querySelectorAll('a')")

options[0].click

sleep(3)

@rightarr = @driver.find_elements(:class,

"coreSpriteRightPaginationArrow").first

@rightarr.click

article = @wait.until {

element = @driver.find_elements(:tag_name, 'article')

}

def getArticle(article)

@time = 0.5

unless article.nil?

str = article.attribute("innerHTML")

contain =

['\#foodie','\#foodies','\#foodporn','\#instafood','\#yum','\#food','\#foodpics','\#

yummyfood','\#geilesessen','\#abendessen', '\#foodstagram', '\#foodgasm',

'\#coffee', '\#cafe', '\#japanesefood'].any? { |word| str.include?(word) }

if contain

@doc = Nokogiri::HTML(str)

author = @doc.at('h2').text.strip

image = @doc.at_css('div div div div img')

if !image.nil?

img = image.attr('src')

else

p 'image nil next'

@rightarr.click

sleep(1)



}

return getArticle(article.last)

end

# check the file

file = File.read(@filename+@file)


32

data_hash = JSON.parse(file)

saveimage = data_hash.any? { |h| h['m'] == img }

# check if is not there

if !saveimage

@time = 2

@id += 1

tags = @doc.search('a').text.strip.scan(/#\w+/).flatten

text = @doc.search('span').text.strip

data = {"id": @id, "author": @doc.at('h2').text.strip, 'm': img,

'text': text, 'url': @driver.current_url}

unless tags.nil?

# p tags

data['tags'] = tags

end

p JSON.pretty_generate(data)

data_hash.push(data)

File.open(@filename+@file, "w") do |f|

f.puts JSON.pretty_generate(data_hash)

end

begin

download = open(img)

IO.copy_stream(download, "#{@filename}#

{@fl}/"[email protected]_s+'.jpg')

# open("#{@filename}#{@fl}/"[email protected]_s+'.jpg', 'wb') do

|file|

# file << open(img).read

# end

rescue SystemCallError

p 'error saving file'

retry

end

end

@rightarr.click

sleep(@time)



}


else

@rightarr.click

p 'does not contain any tags wait: and again'

sleep(@time)



}


end

else

p 'no article found wait'

p @time

sleep(@time)


33

•



}


end # /unless

end

getArticle(article.last)

# @driver.quit

end

navigate(which, fl, id)

After all the images were downloaded to introduce the amount of likes, text andtags into the graphical analysis data so Image-plot were created a secondparser was developer to iterate the text and put associated this properties withevery image.

!/usr/bin/env ruby

require 'rubygems'

require 'open-uri'

require 'json'

require 'csv'

require "unicode/emoji"

which = ARGV[0]

file = File.read(which)

data_hash = JSON.parse(file)

new_image_csv = []

images = CSV.read(ARGV[1], { col_sep: ' ', headers: true })

new_images = CSV.read('/Volumes/External/data/image_data.txt', { col_sep: ' ',

headers: true })

images.each do |row|

if row['where'] == "london"

data_hash.each do |pic|

if row['filename'].split('.')[0].to_i == pic['id'].to_i

puts row['filename']

row['text'] = pic['text'].length

row['tags'] = pic['tags'].length

row['emoji'] = pic['text'].scan(Unicode::Emoji::REGEX)

row['emojicounter'] = pic['text'].scan(Unicode::Emoji::REGEX).length

firstten = pic['text'].slice(1,10)

if firstten.include? 'likes'

if !/\A\d+\z/.match(firstten.slice(0,4))





34

row['likes'] = 0

else

row['likes'] = firstten.slice(0,1).to_i

end

else

row['likes'] =firstten.slice(0,2).to_i

end

else

row['likes'] =firstten.slice(0,3).to_i

end

else

row['likes'] = firstten.slice(0,4).to_i

end

# p firstten

else

row['likes'] = 0

end

end

end

new_image_csv << row

end

end

CSV.open('/Volumes/External/data/measurements_all_hue_median_new.txt', 'w', {

col_sep: ' ', headers: true }) do |csv| # Create a new file updated_guests.csv

csv << ['filename', 'imageID', 'brightness_median', 'brightness_stdev',

'saturation_median', 'saturation_stdev', 'hue_median', 'hue_stdev',

'filepath', 'where', 'likes', 'text', 'tags', 'emoji', 'emojicounter'] # Add new

headers

new_image_csv.each do |row|

# Since we now have the entire updated CSV file in this variable as a double

array,

# we iterate over each (array) element

csv.puts row

end

end


35

8. Final Conclusions

Contemporary photography creates a niche according to specific and limitedphoto-cultures and also into trendsetter hyper-globalize groups. the possibility tomanipulate our information (divide, rearrange, organise, etc.) on different socialmedia using tags has been internalise for the vast majority of digital native bornusers. if during the past century we get to talk about several decades to have asame conceptual and aesthetically frame now we see this frames come and go intoa continuous flux of experiments. The massive democratisation of hardware onhigh income and middle income countries provided a fast track to approachdifferent photo cultures without the need of a big learning curve to be part of it.

Several more Business intelligence companies started to exist in the last decadeprofiting from the access to this flux of information trying to make sense anddiscover insightful patterns in people’s habits to predict possible clients is a highlyrewarded not so easy to accomplish skill. A lot of academic research can also bedone over the same line, this project try to dissect a specific behaviour from thehuge amount of photographies shared to zoom into a specific pattern visualising inthis colloquial manner parity distribution differences can help everyone tounderstand better the and provide a different perspective into local and globaleconomies and how our habits help to shape this, there are more connectionsbetween the industries, the food trade economy and our direct behaviour than wethought about it, again shaping the mobile and hardware industry to fit the cameraeat first behaviour as a average user requirement is a great indicator of our ability toshape an industry.

There are a lot of correspondences between the virtual mapping of most usedwords and emojis and the correspondence with reality, for example: Colombia is acoffee exporter it profits from selling coffee to countries that like to consume it, butwhat Colombia doesn’t have is a coffee internal consumption tradition, that’s not apopular beverage to have and it gets reflected in the data been the city that lessmention coffee in the dataset even when for all cities is a highly mentioned worddue that #Coffee pics are a popular tendency in the overall camera eat first culture,a cappuccino shot is a must have in almost every foodie instagram account and alot of people adopt this behaviour even when they don’t frequently post foodie picsin their social networks. Of course as a producer the price for a coffee in Colombiagets lower than any other city that way the ratio of sharing x price gets to be almost1:1. That way coffee gets to be one of the most used and shared words fromabsolutely all the cities been that true also for Bogotá, but we can still appreciatedthe significant drop in prices and shared contribution between develop andunderdeveloped countries/cities. The general expenses for every country food


36

preferences has a fixed value on average of almost 4.000€ for the individualdataset but for Bogotá that prices drop to a little more than 1.000€ this jumpsremarks when we can see at the same time information from a very localisedportion to a global standardised comparison was precisely the goal of the projectallowing other interested scholars and people to see glocal social media andeconomical distributions.

Camera eat first behaviour just like selfie pics or other social media photographytrend has different layers inside itself that contribute of feel itself representedenough to adhere to it, for our specific photography-culture we can talk about theexisting food trends like fitness (low fat) food, vegan foodies, coffee lovers, all ofthis layers can fit on both categories, the prestige food foodie pics and #foodpornpictures. This division was developed based on direct observation of the montagesand the image-plot series, a better quantification to inquire about the real presenceof this distribution can be done.

22


37

•

•

•

•

•

•

9. Future research

There is still more information about the authors it-selves that can be extractedand analyse, gender, geographical location.Image proportion (aspect ratio) and size can be exactly quantify to checkaesthetic correlations between all of the different urban areas, seems at firstsight than some places are getting used quicker to get read of the instagramonce constrained 1:1 proportion than others, maybe in the future a visualpreference will be determined either for a geographical location or a mainstreamphotography behaviour just like camera eat first, selfies for example usuallyprefer vertical ratios due to the relevance with the body standup verticalposition itself.A small machine learning algorithm can be develop to semantically analyse theintention of the adherent text from all of the images to search for commonpatterns or variations between cities.With enough access to geolocated data which unfortunately is been restrictedit’ll be possible to check for geographical changes and trends in the city spacewith a variation between years or at least seasons.The ingredients of each course meal on the photography and in general moreassociated data only possible get through a human vision could be manuallyassociated to every picture to make a exact evaluation of food distribution,corroborate dietary preferences of every city.With access to a more detail rent price ratio on the cities, we could explore thefoodie dataset and corroborate if it’s possible to visualise gentrificationpatterns with this photo-culture timeline movement.


38

10. A Personal reference

Colombia is the second more unequal country in Latin America and the 12th in theworld according to the CIA Gini index. As a personal background growing up thereas a “rich” kid in a medium class neighbourhood in the middle of more lower classand poorer districts, it put myself surrounded by social injustice and made meexperienced it in a granular level in a regular basis. It’s impossible to leave yourhouse and tour around any city in the country using any possible form oftransportation available without noticing social inequality which is directly feed bycorruption and Colombian armed conflict which fortunately never had toexperience first hand.

Make a good statement or change something within the line between art, designtechnology and activism. There are a lot of problems derived from social inequalitythe ones that use to affect me like having unnecessary arguments or securityissues in residential areas to very crude and horrible problems like political partiesassociated with the Army and militia taking advantage of it to kill around poorpeople in looming city corners and vulnerable habitants in rural areas all of thisbased in unbelievable feudal outdated economies.

It’s incredible frustrating to bump into fellow citizen and peer equals around theglobe that have no idea about what is going on never, They don’t interact at all withpeople or places outside their own echoed culture and they are more familiarisedwith Florida cities that any portion of land in others states inside their own countrybesides where they live. I surely don’t think every single individual need to arisewith actions to achieve sanctification or become an activist but the minimumresponsibility as a citizen of a country will be to be inform himself a little aboutwhat happens, It surely seems less void and superficial.

“When most people envision giving no fucks whatsoever, they envision a kindof perfect and serene indifference to everything, a calm that weathers allstorms. This is misguided. There’s absolutely nothing admirable or confidentabout indifference. People who are indifferent are lame and scared. They’recouch potatoes and internet trolls. In fact, indifferent people often attempt tobe indifferent because in reality they actually give too many fucks. They areafraid of the world and the repercussions of their own choices. Therefore, theymake none.”23


39

I’m convinced now about talking and working about this utterly annoying feelingthat produces me apathetic disengage people and segregated circumstances. Isaid that knowing we all are up to some extend part of it, I more likely had createopportunities around me for this to develop, we all do. I would like to center myselfin spot this moments, a lot of data analysis in the case of Manovich work usingsocial media is run through entire cities or countries regardless of any socialdistinction which I think also permeate social media liveliness, so don’t take thatinto account is a big gap. I would like to debug data analysis of social media slicingas many layers of social stratification that move around as I can. Of course we caneasily distinct inequality through media just pointing who can access internet

against who cannot but I’m more interested in the subtle differences that perhapsdon’t even met the eye in human organisations that supposed to essentially equal.I don’t want to complaint, I value the society that more social democracies havecreated and I value Germany the society I choose to live in

a lot, being that the case I do think one of the principles that keep this societysharp and thinking about what can still be better is autocrictisism.


40

•••••••

•

•

••

••

•

•

•

11. Bibliography

Lev Manovich, Software Takes Command, Bloomsbury Academic, 2013.Lev Manovich, Info-Aesthetics. Bloomsbury, 2015.Peter Burke, Popular Culture in Early Modern Europe. Ashgate, 1978.Anna Maria Guash, Autobiografías visuales. Ediciones Siruela, 2009.Gilda Williams, How to write about contemporary art. Thames & Hudson, 2014.Jonathan Harris, The new art history. Routledge, 2001.Nicolas Bourriaud, Relational Aesthetics, Screen relations chapter. Dijon: Lespresses du réel, 2002.Andreas Huyssen & Klaus Scherpe, Postmoderne: Zeichen eines kulturellenWandels. Rowohlt, 1986.Georges didi Huberman Devant le temps. Histoire de l’art et anachronisme desimages. Broché, 2000.Pierre Bourdieu, Science of Science and Reflexivity. Polity,2001.Arnold Hauser, The social history of art, Sozialgeschichte der Kunst undLiteratur. Routledge, 2015.Arnold Hauser, The Sociology of Art, Soziologie der Kunst. Routledge, 1974.Joseph Stiglitz, The Price of Inequality: How Today’s Divided Society EndangersOur Future. W. W. Norton & Company, 2012.Thomas Piketty, Capital in the Twenty-First Century. Cambridge Massachusetts:Belknap Press, 2014.Anthony Atkinson, Inequality: What Can Be Done?. Harvard University Press,2015.Almas Heshmati, Inequalities and their measurement. (Discussion paper No.1219). Institute for the Study of Labor (IZA), 2004. Retrieved fromhttp://ftp.iza.org/dp1219.pdf on 01.11.18

1. Stratum “strɑːtəm, streɪtəm” a level or class to which people are assignedaccording to theirsocial status, education, or income. “members of other social strata”, OxfordDictionary, Oxford University Press, 1884.

2. ImagePlot is a free software tool that visualizes collections of images and videoof any size. It is implemented as a macro which works with the open sourceimage processing program ImageJ.http://lab.softwarestudies.com/p/imageplot.html. Accessed on 28.11.18.


41

3. https://en.wikipedia.org/wiki/Camera_eats_first. Accessed on 03.11.18.4. “Big data represents the information assets characterized by such a high

volume, velocity and variety to require specific technology and analyticalmethods for its transformation into value”. De Mauro, Andrea; Greco, Marco;Grimaldi, Michele (2016). “A Formal definition of Big Data based on its essentialFeatures”. Library Review

5. Marshall McLuhan, Understanding Media: The Extensions of Man, W. TerrenceGordon, 1964 available onhttp://web.mit.edu/allanmc/www/mcluhan.mediummessage.pdf accessed on02.12.18

6. Jörg Huber & Alois Martin Müller, Raum und Verfahren. Friedrich Adolf Kittler.Stroemfeld 1993.

7. Lev Manovich, Software Takes Command, Bloomsbury Academic, 2013.8. Nasdaq is a global electronic marketplace for buying and selling securities, as

well as the benchmark index for U.S. technology stocks. Nasdaq was created bythe National Association of Securities Dealers (NASD) to enable investors totrade securities on a computerized, speedy and transparent system, andcommenced operations on February 8, 1971.https://www.investopedia.com/terms/n/nasdaq.asp. Accessed on 03.11.18.

9. The Gini index or Gini coefficient is a statistical measure of distributiondeveloped by the Italian statistician Corrado Gini in 1912.https://www.investopedia.com/terms/g/gini-index.asp, Accessed 01.11.18.

10. Visualizing patterns of segregation project from the MIT Human dynamics MediaLab, active from March 2017 to June 2017.https://www.media.mit.edu/projects/visualizing-patterns-of-segregation/overview/. Accessed on 03.11.18.

11. https://www.theguardian.com/world/shortcuts/2013/apr/21/astronaut-chris-hadfield-berlin-divide. Accessed on 03.11.18.

12. The term gentrification was coined in 1964 by a British sociologist – Ruth Glass– when referring to the alterations she observed in the social structure andhousing markets in certain areas of inner London. Glass observed; “One by one,many of the working class quarters have been invaded by the middle class -upper and lower … Once this process of ‘gentrification’ starts in a district it goeson rapidly until all or most of the working class occupiers are displaced and thewhole social social character of the district is changed”. Ruth Glass, London:Aspects of Change. London: Macgibbon & Kee, 1964. p.17.

13. Urban Social Media Inequality: Definition, Measurements, and Application,Agustin Indaco & Lev Manovich,

14. a marketplace for work that requires real human beings intelligence more infoon https://www.mturk.com/. Accessed on 01.11.08


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15. The gross national income (GNI), previously known as gross national product(GNP), is the total domestic and foreign output claimed by residents of acountry, consisting of gross domestic product (GDP), plus factor incomesearned by foreign residents, minus income earned in the domestic economy bynonresidents, https://en.wikipedia.org/wiki/Gross_national_income. Accessed01.11.18

16. http://visual-earth.net/. Accessed on 01.11.1817. Purchasing power parity (PPP) is an economic theory that compares different

countries’ currencies through a “basket of goods” approach. According to thisconcept, two currencies are in equilibrium or at par when a basket of goods(taking into account the exchange rate) is priced the same in both countries.https://www.investopedia.com/updates/purchasing-power-parity-ppp/.Accessed, 01.11.18

18. https://eugdpr.org/. accessed 28.10.1819. set of commands in NIH Image’s Pascal-like macro programming language.20. (/mɒnˈtɑːʒ/) is a technique in film editing in which a series of short shots are

edited into a sequence to condense space, time, and information. The term hasbeen used in various contexts. It was introduced to cinema primarily by SergeiEisenstein, and early Soviet directors used it as a synonym for creative editing.In French the word “montage” applied to cinema simply denotes editing. Theterm “montage sequence” has been used primarily by British and Americanstudios, and refers to the common technique as outlined in this article.https://en.wikipedia.org/wiki/Montage_(filmmaking). Accessed on 02.12.18.

21. https://processing.org/. Accessed on 03.11.1822. “The simultaneous occurrence of both universalizing and particularizing

tendencies in contemporary social, political, and economic systems. The term,a linguistic hybrid of globalization and localization, was popularized by thesociologist Roland Robertson and coined, according to him, by Japaneseeconomists to explain Japanese global marketing strategies.” EncyclopediaBritannica. Accessed 02.12.18.

23. Mark Manson, The Subtle Art of Not Giving a F*ck: A Counterintuitive Approachto Living aGood Life. HarperOne, 2017.

visualizing social media eating habits

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