Emotions make cities live: towards mapping emotions of older adults on urban space

Understanding of interaction between people and urban spaces is crucial for inclusive decision making process. Smartphones and social media can be a rich source of behavioral and declarative data about urban space, but it threatens to exclude voice of older adults. The platform proposed in the paper attempts to address this issue. A universal tagging mechanism based on the Pluchik Wheel of Emotion is proposed. Usability of the platform was tested and prospect studies are proposed.

[1]  L. Phillips,et al.  Age and the understanding of emotions: neuropsychological and sociocognitive perspectives. , 2002, The journals of gerontology. Series B, Psychological sciences and social sciences.

[2]  Judit Bar-Ilan,et al.  Structured versus unstructured tagging: a case study , 2008, Online Inf. Rev..

[3]  Saif Mohammad,et al.  CROWDSOURCING A WORD–EMOTION ASSOCIATION LEXICON , 2013, Comput. Intell..

[4]  Rossano Schifanella,et al.  The shortest path to happiness: recommending beautiful, quiet, and happy routes in the city , 2014, HT.

[5]  Henriette Cramer,et al.  Aesthetic capital: what makes london look beautiful, quiet, and happy? , 2014, CSCW.

[6]  Marco Painho,et al.  Emotion & stress mapping: Assembling an ambient geographic information-based methodology in order to understand smart cities , 2015, 2015 10th Iberian Conference on Information Systems and Technologies (CISTI).

[7]  Abdulmotaleb El-Saddik,et al.  The affect-aware city , 2015, 2015 International Conference on Computing, Networking and Communications (ICNC).

[8]  Radoslaw Nielek,et al.  A Location-Based Game for Two Generations: Teaching Mobile Technology to the Elderly with the Support of Young Volunteers , 2017, eHealth 360°.

[9]  Elizabeth A. Yost,et al.  Getting Grandma Online: Are Tablets the Answer for Increasing Digital Inclusion for Older Adults in the U.S.? , 2015, Educational gerontology.

[10]  Paul Marshall,et al.  "...when you're a Stranger": Evaluating Safety Perceptions of (un)familiar Urban Places , 2016, Urb-IoT.

[11]  Adam Wierzbicki,et al.  Emotion Aware Mobile Application , 2010, ICCCI.

[12]  Daniele Quercia Chatty, Happy, and Smelly Maps , 2015, WWW.

[13]  Daniel Gatica-Perez,et al.  The young and the city: crowdsourcing urban awareness in a developing country , 2014, Urb-IoT.

[14]  Abdulmotaleb El-Saddik,et al.  Detection and Visualization of Emotions in an Affect-Aware City , 2014, EMASC '14.

[15]  Jung-Hsien Chiang,et al.  Predicting Negative Emotions Based on Mobile Phone Usage Patterns: An Exploratory Study , 2016, JMIR research protocols.

[16]  Nina Runge,et al.  Tag your emotions: a novel mobile user interface for annotating images with emotions , 2016, MobileHCI Adjunct.

[17]  K. Scherer,et al.  Introducing a short version of the Geneva Emotion Recognition Test (GERT-S): Psychometric properties and construct validation , 2016, Behavior research methods.

[18]  Mykola Pechenizkiy,et al.  Rule-based Emotion Detection on Social Media: Putting Tweets on Plutchik's Wheel , 2014, ArXiv.

[19]  Daniel Gatica-Perez,et al.  InnerView: Learning Place Ambiance from Social Media Images , 2016, ACM Multimedia.

[20]  Giovanni Quattrone,et al.  Measuring Urban Deprivation from User Generated Content , 2014, CSCW.

[21]  Vijay Sivaraman,et al.  Mobile crowdsourcing older people's opinions to enhance liveability in regional city centres , 2014, 2014 IEEE Ninth International Conference on Intelligent Sensors, Sensor Networks and Information Processing (ISSNIP).

[22]  Katherine Ludwin,et al.  Place memory and dementia: Findings from participatory film-making in long-term social care. , 2015, Health & place.