Exploring People's Emotional Bond with Places in the City

Capturing, Exploring and Sharing People’s Emotional Bond with Places in the City using Emotion Maps

“…Inspired by Nold's work on emotional cartography (Nold 2009) and the rise of the Quantified-Self movement (Li et al 2013), there currently is an increased interest in exploring how mobile, wearable and Quantified-Self technology could potentially be used to capture and collect people's emotional experiences of urban places (Rooksby et al 2014;Matassa 2013;Resch et al 2015;Stals, Smyth, and Mival 2017b;Quercia, Schifanella, and Aiello 2014) and the potential for sharing this personal data with other people using emotion maps (Al-Husain, Kanjo, and Chamberlain 2013; Leahu, Schwenk, and Sengers 2008;Matassa and Rapp 2015;Mody, Willis, and Kerstein 2009;Nold 2004;Nold 2009). Based on social science studies of the concept of place attachment (Manzo 2005;Gustafson 2001; Scannell and Gifford 2010) and these urban HCI studies attempting to leverage people's emotional experience of the urban environment, we argue that places that are meaningful to people on a personal level, could provide a suitable lens for further investigation, as these personally meaningful places are typically the places that a person has a strong emotional bond with (Stals, Smyth, and Mival 2017a).…”

“…The aim is to investigate how people would like to capture their experience of a personally significant place, the different forms this data could take, and the potential for sharing this personal data with other people that the participant has different types of social relationships with (e.g. strangers, friends, and family) (Stals 2017;Stals, Smyth, and Mival 2017a;Stals, Smyth, and Mival 2017b).…”

“…As this research is part of an ongoing PhD dissertation, the full data corpus gathered over a six-month period in the city of Edinburgh (United Kingdom) is expected to consist of 40 personally significant places and will be analysed using a thematic, bottom-up analysis typical of a grounded-theory approach. However, initial analysis of the data gathered during our pilot study of in total 10 personally significant places, which was conducted prior to the main data gathering in Edinburgh over a one-month period, suggests that for participants there is not just one emotion connected to the experience of each personally significant place, but there can be multiple different ones (Stals, Smyth, and Mival 2017a). This poses a potential problem for the use of emotion maps as a tool to create a more accurate understanding of a person's emotional experience of, and relationship with personally meaningful places in the urban environment.…”

“…This poses a potential problem for the use of emotion maps as a tool to create a more accurate understanding of a person's emotional experience of, and relationship with personally meaningful places in the urban environment. The design and evaluation of the Walking & Talking method as a way to in-situ, elicit qualitative measurements of the subjective emotional experiences that participants have in their personally significant urban places (Stals, Smyth, and IJsselsteijn 2014), and the limited efficacy of using Automatic Facial Expression Recognition (AFER) software to retrospectively analyse video recordings of those Walking & Talking interviews to gather quantitative emotion data, have already been discussed in more detail in earlier work (Stals, Smyth, and Mival 2017a). However, based on the results of our pilot study and a systematic review of emotions maps in existing literature, this journal paper aims to highlight and discuss the strengths, limitations and potential of using emotion maps as a means to subsequently capture, visualize, explore and share this personal, geo-located emotion data of a person's emotional experience of, and relationship with personally meaningful places in the urban environment.…”

“…What GPS unfortunately cannot do, is tell you what a person's emotional experience of that location is, let alone the quality or cause of that experience. As it turns out, this is also still a challenge for other technologies such as EEG, GSR and AFER, which are often deployed in combination with GPS to Please do not adjust margins automatically collect emotion-related data, especially when utilized outside a lab setting such as an urban environment (Resch et al 2015;Stals, Smyth, and Mival 2017a;Tilley et al 2017;Westerink et al 2008). This raises interesting challenges regarding the visualization, use, and sharing of geo-located emotion data using emotion maps, which are often based on these types of data.…”