Urban Emotions—Geo-Semantic Emotion Extraction from Technical Sensors, Human Sensors and Crowdsourced Data

Resch, Bernd; Summa, Anja; Sagl, Günther; Zeile, Peter; Exner, Jan–Philipp

doi:10.1007/978-3-319-11879-6_14

Cited by 94 publications

(84 citation statements)

References 15 publications

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“…Several research papers deal with emotional mapping (e.g., Panek et al, 2016), in which web applications collect the emotional perception of urban space. A more complex research project (Resch et al, 2015) uses technical and human sensors and georeferenced social media posts, from which the researchers extract contextual emotional information.…”

Section: Indroductionmentioning

confidence: 99%

Measuring Citizens’ Fear of Crime Using a Web Application: A Case Study

Pődör¹,

Révész²,

Rácskai³

et al. 2016

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The authors evaluate the use of a Google API web application to test where people feel safe and unsafe with respect to crime in Nagykanizsa, a relatively small settlement in Hungary. The web application provided users with tools to show areas where they feel safe or feel fear, which they indicated with polygons. The authors introduce how the application was developed and how the participants used it.

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Section: Indroductionmentioning

confidence: 99%

Measuring Citizens’ Fear of Crime Using a Web Application: A Case Study

Pődör¹,

Révész²,

Rácskai³

et al. 2016

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“…This development has given rise to the following problems and questions, as Jacobs's original approach has been adapted and integrated into the Urban Emotions initiative (Resch et al, 2015): How can all heterogeneous interests be integrated into the planning process? Are citizens' spatial perceptions measurable?…”

Section: State Of Researchmentioning

confidence: 99%

Combining Biosensing Technology and Virtual Environments for Improved Urban Planning

Zeile

Resch

2018

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The Urban Emotions initiative uses biosensing technology to determine how people feel in the city, which is of particular relevance for architecture and urban planning. While past experiments focused more on pedestrian or bicycle traffic, accessibility and wayfinding, this paper proposes the use of virtual models as a basis for human sensorial measurement. Virtual space offers the possibility of minimizing external (environmental) influences to focus on the evaluation of design impressions. Inspiration for the method was 'Q-sorting' according to Stephenson (1953) and, in the context of urban planning, Krause (1974). Virtual models of real situations are used to determine whether test participants respond positively or negatively to the architecture or their environment. 360° videos, virtual reality ambience and VR glasses are used as output devices. In this virtual environment, it is possible to create standardized, comparable laboratory situations allowing researchers to draw more reliable and focused conclusions about human responses to their physical environment. The challenge for the future will be to transfer this knowledge of citizens' responses to the built environment into real design processes.

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“…Jane Jacobs was one of the pioneers of a bottom-up and citizen-centric planning approach (Jacobs, 1961 The Urban Emotions approach addresses these questions by using "human sensor" data, generated by social media, wearable sensor technology, and participatory sensing approaches, to develop a method set that creates a new point of view, viewing the "city as an organism" (Resch, Summa, Sagl, Zeile, & Exner, 2015). This approach is clearly influenced by the work of (Castells, 1996).…”

Section: Citizen-centric Urban Planningmentioning

confidence: 99%

Citizen-Centric Urban Planning through Extracting Emotion Information from Twitter in an Interdisciplinary Space-Time-Linguistics Algorithm

Resch

Summa

Zeile

et al. 2016

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Traditional urban planning processes typically happen in offices and behind desks. Modern types of civic participation can enhance those processes by acquiring citizens' ideas and feedback in participatory sensing approaches like "People as Sensors". As such, citizen-centric planning can be achieved by analysing Volunteered Geographic Information (VGI) data such as Twitter tweets and posts from other social media channels. These user-generated data comprise several information dimensions, such as spatial and temporal information, and textual content. However, in previous research, these dimensions were generally examined separately in single-disciplinary approaches, which does not allow for holistic conclusions in urban planning. This paper introduces TwEmLab, an interdisciplinary approach towards extracting citizens' emotions in different locations within a city. More concretely, we analyse tweets in three dimensions (space, time, and linguistics), based on similarities between each pair of tweets as defined by a specific set of functional relationships in each dimension. We use a graph-based semi-supervised learning algorithm to classify the data into discrete emotions (happiness, sadness, fear, anger/disgust, none). Our proposed solution allows tweets to be classified into emotion classes in a multi-parametric approach. Additionally, we created a manually annotated gold standard that can be used to evaluate TwEmLab's performance. Our experimental results show that we are able to identify tweets carrying emotions and that our approach bears extensive potential to reveal new insights into citizens' perceptions of the city.

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Urban Emotions—Geo-Semantic Emotion Extraction from Technical Sensors, Human Sensors and Crowdsourced Data

Cited by 94 publications

References 15 publications

Measuring Citizens’ Fear of Crime Using a Web Application: A Case Study

Measuring Citizens’ Fear of Crime Using a Web Application: A Case Study

Combining Biosensing Technology and Virtual Environments for Improved Urban Planning

Citizen-Centric Urban Planning through Extracting Emotion Information from Twitter in an Interdisciplinary Space-Time-Linguistics Algorithm

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