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Fig 1 ‘Happy Map Tuesday 18th February 2014’.
Fig 2 ‘Happy Map Sunday 16th March 2014’.
Fig 3 'Happy Map Hyde Park’.
Fig 4 'Happy Map Traffic’.
Fig 5 'Happy Map Traffic Raw Data’.
Fig 6 'Happy Map Tuesday Compared to Friday’.
Fig 7 'Happy Map Saturday 22nd February 2014’.

Happy Map

Mapping Happiness in London

Happy Map visually represents, in real time, the location and volume of happiness or joy in London. Active data has been collected successfully before, one project, Mappiness is still ongoing, it directly asks participants about the state of their happiness (MacKerron et al, 2011). However, this method, it could be argued, collects a skewed perspective. Only collecting data from willing participants, people, who therefore, are already self-conscious of their state of wellbeing. The collection technique, an iPhone app also limits the demographic of the research sample. Happy Map, by contrast collects passive data from unknowing participants, across multiple social media platforms.

It was deduced that, in an international city such as London, this data had to come in non-text form, due to the many languages used across the city. Images could be scanned for facial expressions, however this wasn’t within the temporal scope of the project. Instead, emoticons, the textual representation of facial emotions, were explored. It was found that different cultures and ethnic backgrounds, used the same two emoticons ‘:) – happy’ and ‘:( – sad’ in all languages. While eastern cultures tend to use vertical emoticons, the standard two were found to be used in similarly and consistent proportions (Park et al, 2009). To collect the data, software had to be developed that would scan social media platforms for the use of these two emoticons. This method does discriminate against non-social media users but given the potentially very large sample-size, this discrimination was acknowledged but overlooked.

The software then outputs the raw data to an online browser based Google Map, similar to FBomb (fbomb.co, 2014) which locates and highlights use of the word ‘fuck’ on a map. Happy Map represents the information visually as green and red temperature-style datasets. Using the Google Maps method allows for the easy integration of other data-sets as layers, similar to the way in which #floodplain (Cummings et al, 2014) compares such datasets. These other datasets include weather, traffic and transit lines for example. We are then able to find patterns, positive and negative correlations enabling the user to interpret and deduce different conclusions.

Defining Happiness

Abstract: In the world of politics, expressing heightened concern for a nation’s happiness has become a popular method of obtaining ‘soft power’. Though, without proper definition, how can politicians argue their success in the ‘war of well-being’? This paper, to some degree is meant as a satirical comment on the ludicrous nature of the ongoing debate.

Key Terms: Happiness, happy planet index, depression,  joy, well-being, life satisfaction, tranquility, Buddhism, serotonin, perception, consciousness, drugs, equation, psychology, smile, flow, challenge, skill, low expectations.

Mapping Happiness

Abstract: This paper is a follow up to the project titled ‘Happy Map’ (Armitage, 2014). The project collected live, passive ‘happiness data’ and represented it, visually on a map. This paper discusses the findings and what they could mean for societal, political, statistical and philosophical fields.

Key terms: Happiness, happy planet index, depression, joy, wellbeing, life satisfaction, tranquility, consciousness, psychology, expectations, mapping, cartography, information design, data visualisation, London, traffic, weather, datasets, floodplain, fbomb, SINTEF.

Links

Note: Happy Map was originally intended to work live, online. However with my limited web dev skills, I was unable to get this working. If anyone out there has the ability and willingness to help me get it up and running for real, please get in touch. I have the software to collect the data and the online styled google map, it’s simply a case of taking a CSV file from the software, storing it online and parsing the info into a javascript array. For an experienced web dev, I wouldn’t imagine this to be too much of a challenge. Drop me an Email.