Urban Emotions: Benefits and Risks in Using Human Sensory Assessment for the Extraction of Contextual Emotion Information in Urban Planning

  • Peter Zeile
  • Bernd Resch
  • Jan-Philipp Exner
  • Günther Sagl
Part of the Lecture Notes in Geoinformation and Cartography book series (LNGC)


This chapter introduces the ‘Urban Emotions’ approach. It focuses on integrating humans’ emotional responses to the urban environment into planning processes. The approach is interdisciplinary and anthropocentric, i.e. citizens and citizens’ perceptions are highlighted in this concept. To detect these emotions/perceptions, it combines methods from spatial planning, geoinformatics and computer linguistics to give a better understanding of how people perceive and respond to static and dynamic urban contexts in both time and geographical space. For collecting and analyzing data on the emotional perception to urban space, we use technical and human sensors as well as georeferenced social media posts, and extract contextual emotion information from them. The resulting novel information layer provides an additional, citizen-centric perspective for urban planners. In addition to technical and methodological aspects, data privacy issues and the potential of wearables are discussed in this chapter. Two case studies demonstrate the transferability of the approach into planning processes. This approach will potentially reveal new insights for the perception of geographical spaces in spatial planning.


Global Position System Planning Process Skin Conductance Spatial Planning Emotion Information 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



The authors would like to express their gratitude to the German Research Foundation (DFG—Deutsche Forschungsgemeinschaft) for supporting the project Urban Emotions, reference number ZE 1018/1-1 and RE 3612/1-1. This research has been supported by the Klaus Tschira Stiftung GmbH. We would also like to thank Linda Dörrzapf, Anja Summa, Martin Sudmanns, Daniel Broschart, Johann Wilhelm, Claire Dodd and Dennis J. Groß for their support.


  1. Benjamin, W., & Tiedemann, R. (1983). Das Passagen-Werk (1st edn, Edition Suhrkamp). Frankfurt: Suhrkamp.Google Scholar
  2. Bergner, B. S., & Zeile, P. (2012). Ist Barrierefreiheit messbar? Planerin, 2012(2), 20–24.Google Scholar
  3. Bishop, I. D., & Hull, B. R. (1991). Integrating technologies for visual resource management. Journal of Environmental Management, 32(4), 295–312. doi: 10.1016/S0301-4797(05)80068-4.CrossRefGoogle Scholar
  4. Boucsein, W. (2012). Electrodermal activity (2nd ed.). New York: Springer Science + Business Media LLC.CrossRefGoogle Scholar
  5. Bradley, M. M., & Lang, P. J. (2007). The International Affective Picture System (IAPS) in the study of emotion and attention. In J. A. Coan, & J. J. B. Allen (Eds.), Handbook of emotion elicitation and assessment (Series in affective science). Oxford, New York: Oxford University Press.Google Scholar
  6. Burke, J., Estrin, D., Hansen, M., Parker, A., Ramanathan, N., Reddy, S., et al. (2006). Participatory sensing. World Sensor Web 2006 Proceedings, 1–5.Google Scholar
  7. Caesar, I. (2012). Social Web-politische und gesellschaftliche Partizipation im Netz: Beobachtungen und Prognosen. Berlin: Simon Verlag für Bibliothekswissen.Google Scholar
  8. Castells, M. (1996). The rise of the network society (Information age, v. 1). Malden, Mass: Blackwell Publishers.Google Scholar
  9. Cullen, G. (1961). Townscape. New York: Reinhold Publishing Corporation.Google Scholar
  10. da Silva, R., Zeile, A. N., Aguiar, P., de Oliveira, F., Papastefanou, G., & Bergner, B. S. (2014). Smart sensoring and barrier free planning—project outcomes and recent developments. In N. N. Pinto, J. A. Tenedório, A. P. Antunes, & J. R. Cladera (Eds.), Technologies for urban and spatial planning: Virtual cities and territories (pp. 93–112). Hershey, PA: IGI Global.Google Scholar
  11. Debord, G. (1956). Theory of the Dérive. Les Lèvres Nues (9). Paris. Reprinted in Situationistische Internationale (1976), Band 1. Körle: SI-Revue.Google Scholar
  12. Debord, G. (1957). Guide psychogéographique de Paris: Discours sur les passions de l’amour. Copenhagen: Permild & Rosengreen.Google Scholar
  13. Downs, R. M., & Meyer, J. T. (1978). Geography and the mind: An exploration of perceptual geography. American Behavioral Scientist. doi:  10.1177/000276427802200104.
  14. Downs, R. M., & Stea, D. (1974). Image and environment: Cognitive mapping and spatial behavior. Piscataway, NJ: Transaction Publishers.Google Scholar
  15. Fahrenberg, J., & Myrtek, M. (2001). Progress in ambulatory assessment: Computer-assisted psychological and psychophysiological methods in monitoring and field studies. Seattle: Hogrefe & Huber Publishers.Google Scholar
  16. Franke, J., & Bortz, J. (1972). Beiträge zur Anwendung der Psychologie auf den Städtebau: 1. Vorüberlegungen und erste Erkundungsuntersuchung zur Beziehung zwischen Siedlungsgestaltung und Erleben der Wohnumgebung. Zeitschrift für experimentelle und angewandte Psychologie, 19(1), 76–108.Google Scholar
  17. Geyer, M. A., & Swerdlow, N. R. (1998) Measurement of startle response, prepulse inhibition, and habituation. In C. Gerfen, A. Holmes, D. Sibley, P. Skolnick, S. Wray (Eds.), Current protocols in neuroscience (pp. 8.7.1–8.7.15). New York: Wiley.Google Scholar
  18. Hoch, C. (2006). Emotions and planning. Planning Theory & Practice, 7(4), 367–382. doi: 10.1080/14649350600984436.CrossRefGoogle Scholar
  19. Höffken, S., Wilhelm, J., Groß, D., Bergner, B. S., & Zeile, P. (2014). EmoCycling—Analysen von Radwegen mittels Humansensorik und Wearable Computing. In M. Schrenk, V. V. Popovich, P. Zeile, & P. Elisei (Eds.), Real CORP 2014 (pp. 851–860). Wien.Google Scholar
  20. Jacobs, J. (1961). The death and life of great American cities. New York: Random House Vintage Books.Google Scholar
  21. Kaiser, E. J., Chapin, F. S., & Godschalk, D. R. (1995). Urban land use planning (4th ed.). Chicago: University of Illinois Press.Google Scholar
  22. Kersten, H., & Klett, G. (2012). Mobile device management (1st edn). Heidelberg u.a.: mitp.Google Scholar
  23. Klausnitzer, R. (2013). Das Ende des Zufalls: Wie Big Data uns und unser Leben vorhersagbar macht (1st ed.). Salzburg: Ecowin-Verl.Google Scholar
  24. Kotrotsios, G., & Luprano, J. (2011). The commercialization of smart fabrics: Intelligent textiles. In A. Bonfiglio & D. de Rossi (Eds.), Wearable monitoring systems (pp. 277–294). US: Springer.CrossRefGoogle Scholar
  25. Krause, K.-J. (1974). Stadtgestalt und Stadterneuerung. Bonn: Bundesvereinigung Deutscher Heimstätten e.V.Google Scholar
  26. Lynch, K. (1960). The Image of the city: Cambridge Mass: MIT Press.Google Scholar
  27. Mann, S. (1998). Wearable computing as means for personal empowerment: Keynote address. In Keynote address for the first international conference on wearable computing, ICWC-98. Fairfax, VA, 12 May.Google Scholar
  28. Martino, M., Britter, R., Outram, C., Zacharias, C., Bidermann, A., Ratti, C. (2010). Senseable city—digital urban modelling and simulation. Working paper. MIT senseable city lab. Accessed 25 Feb 2015.
  29. Memmel, M., & Groß, F. (2011). RADAR—potentials for supporting urban development with a social geocontent hub. In M. Schrenk, V. V. Popovich, & P. Zeile (Eds.), REAL CORP 2011 (pp. 777–784). Wien: Essen.Google Scholar
  30. Mody, R. N., Willis, K. S., & Kerstein, R. (2009). WiMo: location-based emotion tagging. In N. Milic-Frayling, J. Häkkilä, J. Crowcroft, C. Mascolo, & E. O’Neill (Eds.): Proceedings of the 8th International Conference on Mobile and Ubiquitous Multimedia (pp. 1–4). New York: ACM.Google Scholar
  31. Montjoye, Y.-A. de, Hidalgo, C. A., Verleysen, M., Blondel, V. D. (2013). Unique in the Crowd: The privacy bounds of human mobility. Scientific reports, doi:  10.1038/srep01376
  32. Myrtek, M., Aschenbrenner, E., & Brügner, G. (2005). Emotions in everyday life: an ambulatory monitoring study with female students. Biological Psychology,. doi: 10.1016/j.biopsycho.2004.06.001.Google Scholar
  33. Nold, C. (2009). Emotional cartography: Technologies of the self. Accessed 15 Jan 2015.
  34. Resch, B. (2013). People as sensors and collective sensing-contextual observations complementing geo-sensor network measurements. In J. M. Krisp (Ed.), Lecture notes in geoinformation and cartography (pp. 391–406). Berlin, Heidelberg: Springer.Google Scholar
  35. Resch, B., Summa, A., Sagl, G., Zeile, P., & Exner, J. P. (2015a). Urban emotions—geo-semantic emotion extraction from technical sensors, human sensors and crowdsourced data. In G. Gartner & H. Huang (Eds.), Progress in location-based services 2014 (pp. 199–212, Lecture Notes in Geoinformation and Cartography). Berlin: Springer International Publishing.Google Scholar
  36. Resch, B., Sudmanns, M., Sagl, G., Summa, A., Zeile, P., & Exner, J. P. (2015b). Crowdsourcing physiological conditions and subjective emotions by coupling technical and human mobile sensors. In T. Jekel, A. Car, J. Strobl & G. Griesebner (Eds.), GI_Forum 2015—geospatial minds for society (in press).Google Scholar
  37. Schächinger, H. (2003). Herz-Kreislauf-Erkrankungen. In U. Ehlert (Ed.), Verhaltensmedizin (pp. 225–263, Springer-Lehrbuch): Berlin, Heidelberg: Springer.Google Scholar
  38. Schumacher, F. (2013). Angewachsen—Wie Wearables unseren Alltag verändern werden. c’t(25), 86ff.Google Scholar
  39. Schumacher, S. (2014). Psychophysiological responses to emotional stimuli and their alterations in stress—related mental disorders: Universität Konstanz.Google Scholar
  40. Shilton, K. (2009). Four billion little brothers? Communications of the ACM, 52(11), 48–53. doi: 10.1145/1592761.1592778.CrossRefGoogle Scholar
  41. Stern, R. M., Ray, W. J., & Quigley, K. S. (2001). Psychophysiological recording (2nd edn). Oxford [England], New York: Oxford University Press.Google Scholar
  42. Streich, B. (2014). Subversive Stadtplanung. Wiesbaden: Springer VS.CrossRefGoogle Scholar
  43. Trieb, M. (1974). Stadtgestaltung: Theorie und Praxis (Bauwelt-Fundamente, 43: Städtebau, Architektur). Düsseldorf: Bertelsmann-Fachverlag.CrossRefGoogle Scholar
  44. Zang, H., & Bolot, J. (2011). Anonymization of location data does not work. In P. Ramanathan, T. Nandagopal, & B. Levine (Eds.), Las Vegas, Nevada, USA, 19.9.-23.9.2011 (pp. 145–156), New York, NY: ACM.Google Scholar
  45. Zeile, P., Exner, J. P., Bergner, B. S., & Streich, B. (2013). Humansensorik und Kartierung von Emotionen in der räumlichen Planung. In E. Buhmann, S. Ervin, & M. Pietsch (Eds.), DLA Conference 2013 (pp. 129–141). Berlin: Wichmann Verlag.Google Scholar
  46. Zeile, P., Memmel, M., & Exner, J. P. (2012). A new urban sensing and monitoring approach: Tagging the city with the RADAR SENSING app. In M. Schrenk, V. V. Popovich, P. Zeile & P. Elisei (Eds.), REAL CORP 2012 (pp. 17–25). Wien.Google Scholar
  47. Zeile, P., Resch, B., Exner, J. P., Sagl, G., & Summa, A. (2014). Urban Emotions—Kontextuelle Emotionsinformationen für die räumliche Planung auf Basis von Echtzeit- Humansensorik und Crowdsourcing-Ansätzen. In J. Strobl, T. Blaschke, G. Griesebner, & B. Zagel (Eds.), Angewandte Geoinformatik: Beiträge zum AGIT-Symposium Salzburg (pp. 664–669). Berlin: Wichmann.Google Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Peter Zeile
    • 1
  • Bernd Resch
    • 2
    • 3
    • 4
  • Jan-Philipp Exner
    • 1
  • Günther Sagl
    • 2
    • 3
    • 5
  1. 1.Department of CAD and Planning Methods in Urban Planning and Architecture—CPEUniversity of KaiserslauternKaiserslauternGermany
  2. 2.Department of Geoinformatics—Z_GISUniversity of SalzburgSalzburgAustria
  3. 3.Chair of GIScienceUniversity of HeidelbergHeidelbergGermany
  4. 4.Center for Geographic AnalysisHarvard UniversityCambridgeUSA
  5. 5.Department of Geoinformation and Environmental TechnologiesCarinthia University of Applied SciencesSalzburgAustria

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