Mapping Emotions: Spatial Distribution of Safety Perception in the City of Olomouc

Conference paper
Part of the Lecture Notes in Geoinformation and Cartography book series (LNGC)


Places are strongly linked with emotions and can be considered safe or unsafe, pleasant or ugly, favourite or boring among other emotions. Subjective perceptions of a city can be valuable sources of information for city planners and a local administration. Among the above-mentioned emotions that have an effect on the quality of life for people in a city, the perception of safety has a prominent position. Safety and fear of criminality affect our interaction with public spaces the most. But criminality does not have to be the only reason people feel uncomfortable in a city, they may also be afraid of the darkness or the friendlessness of a place. The paper describes the mapping of unsafe places in the city of Olomouc via a paper-based questionnaire and a web-based crowdsourcing tool In total, the authors collected answers from 661 respondents; 144 used the online tool and 517 used the paper-based version. The final dataset comprises 1516 places (453 online/1063 questionnaire). The data were gathered over the period between 1st October and 2nd December 2015. The authors collected data that are gender specific as well and time of day specific, therefore it was possible to analyse the differences between daytime and night-time fearful places in the city as well as places that are perceived unsafe by women and men. The spatial density analysis, local correlations and hexagonal aggregation revealed hot-spots that are felt by the citizens of Olomouc to be unsafe. The strongest agreement in votes can mainly be found in the three localities with the densest localisation of votes. In these localities, a strong correlation exists also between the perception of fear during the daytime and the night-time. The results of the case study can be used by the local police department or administration authorities in the future development of safety strategies for the city.


Emotional mapping Subjective data Unsafe places Olomouc Spatial correlation Geovisualisation 


  1. Anselin L, Cohen J, Cook D, Gorr W, Tita G (2000) Spatial analyses of crime. Crim Justice 4(2):213–262Google Scholar
  2. Barrett LF (2006) Solving the emotion paradox: categorization and the experience of emotion. Personal Soc Psychol Rev 10(1):20–46CrossRefGoogle Scholar
  3. Bergner B, Zeile P, Papastefanou G (2011) Emotional barrier-GIS—a new approach to integrate barrier-free planning in urban planning processes. In: Proceedings REAL CORP, pp 247–257. Retrieved from
  4. Biever C (2010) Twitter mood maps reveal emotional states of America. New Sci 207(2771):14CrossRefGoogle Scholar
  5. Bollen J, Mao H, Zeng X (2011) Twitter mood predicts the stock market. J Comput Sci 2(1):1–8CrossRefGoogle Scholar
  6. Brown M, Polk K (1996) Taking fear of crime seriously: the Tasmanian approach to community crime prevention. Crime Delinq 42(3):398–420. doi:10.1177/0011128796042003004 CrossRefGoogle Scholar
  7. Burian J, Pászto V, Langrová B (2014) Possibilities of the definition of city boundaries in GIS—the case study of a medium-sized city. In: 14th SGEM GeoConference on informatics, geoinformatics and remote sensing, vol 3, pp 777–784. doi:10.5593/SGEM2014/B23/S11.099
  8. Chainey S, Ratcliffe J (2013) GIS and crime mapping. Wiley, New YorkGoogle Scholar
  9. Clemente F, Kleiman MB (1977) Fear of crime in the United States: a multivariate analysis. Soc Forces 56(2):519–531. doi:10.1093/sf/56.2.519 CrossRefGoogle Scholar
  10. Curtis JW (2012) Integrating sketch maps with GIS to explore fear of crime in the urban environment: a review of the past and prospects for the future. Cartogr Geogr Inf Sci 39(4):175–186. doi:10.1559/15230406394175 CrossRefGoogle Scholar
  11. Czech Statistical Office (2015) Population of municipalities of the Czech republic, 1 Jan 2015. Retrieved 25 Sept 2015 from,
  12. Doran BJ, Burgess MB (2011) Putting fear of crime on the map: investigating perceptions of crime using geographic information systems. Springer, BerlinGoogle Scholar
  13. Fisher BS, May D (2009) College students’ crime-related fears on campus: are fear-provoking cues gendered? J Contemp Crim Justice 25(3):300–321. doi:10.1177/1043986209335013 CrossRefGoogle Scholar
  14. Gartner G (2012) Putting emotions in maps—the wayfinding example., pp 61–65. Retrieved from
  15. Gould P (1986) Mental maps. Taylor & Francis, LondonGoogle Scholar
  16. Griffin AL, Mcquoid J (2012) At the intersection of maps and emotion: the challenge of spatially representing experience. Kartographisch Nachrichten 6:291–299Google Scholar
  17. Hauthal E, Burghardt D (2014) Mapping space-related emotions out of user-generated photo metadata considering grammatical issues. Cartogr J 53:78–90CrossRefGoogle Scholar
  18. Huang H, Gartner G, Klettner S, Schmidt M (2014) Considering affective responses towards environments for enhancing location based services. ISPRS Int Arch Photogramm Remote Sens Spat Inf Sci 1:93–96CrossRefGoogle Scholar
  19. Jíchová J, Temelová J (2012) Kriminalita a její percepce ve vnitřním městě: případová studie pražského Žižkova a Jarova. Geografie 3(117):329–348Google Scholar
  20. Kalogirou S (2011) Testing local versions of correlation coefficients. Jahrbuch Für Regionalwissenschaft 32(1):45–61. doi:10.1007/s10037-011-0061-y CrossRefGoogle Scholar
  21. Kalogirou S (2015) lctools: local correlation, spatial inequalities and other tools. Retrieved from
  22. Kloeckl K, Senn O, Di Lorenzo G, Ratti C (2011) Live singapore!—an urban platform for real-time data to program the city. In: Computers in urban planning and urban management (CUPUM), vol 4, Lake Louise, Alberta, Canada, 5–8 July 2011Google Scholar
  23. Korpela K (2002) Children’s environment. In: Robert B. Bechtel, Arza Churchman (eds) Handbook of environmental psychology, Wiley, New York, USA, pp 363–373Google Scholar
  24. Leitner M (2013) Crime modeling and mapping using geospatial technologies, vol 8. Springer, BerlinCrossRefGoogle Scholar
  25. Lipscomb S (2014) Visualizing perceived safety in a campus environment. Retrieved 1 Dec 2015, from
  26. MacKerron G, Mourato S (2010). Mappiness. Retrieved 12 Sept 2015, from
  27. Mislove A, Lehmann S, Ahn Y-Y, Onnela J-P, Rosenquist JN (2010) Pulse of the nation: U.S. mood throughout the day inferred from Twitter. Retrieved 12 Sept 2015, from
  28. Mody RN, Willis KS, Kerstein R (2009) WiMo: location-based emotion tagging. In: Proceedings of the 8th international conference on mobile and ubiquitous multimedia, p 14Google Scholar
  29. Němečková M (2014) Komparace vnímání bezpečnosti ve městech Most a Litvínov. Masarykova Univerzita. Retrieved from
  30. Nold C (2009) Emotional cartography: technologies of the self. http://WWW.EMOTIONALCARTOGRAPHY.NET. Retrieved from
  31. Oc T, Tiesdell S (1997) Safer city centres: reviving the public realm. Sage, Beverley HillsGoogle Scholar
  32. Olomouc Region (2015) Prevence kriminality. Retrieved 17 Dec 2015, from
  33. Pearce MW (2008) Framing the days: place and narrative in cartography. Cartogr Geogr Inf Sci 35(1):17–32CrossRefGoogle Scholar
  34. Pickles J (1995) Ground truth: the social implications of geographic information systems, 1st edn. Guilford Press, New YorkGoogle Scholar
  35. Raslan R, Al-hagla K, Bakr A (2014) Integration of emotional behavioural layer “EmoBeL” in city planning. In: REAL CORP 2014, vol 8, pp 309–317Google Scholar
  36. Reeve J (2014) Understanding motivation and emotion. Wiley, New YorkGoogle Scholar
  37. Reimann C, Filzmoser P, Garrett RG, Dutter R (2008) Statistical data analysis explained: applied environmental statistics with R. Wiley, New YorkCrossRefGoogle Scholar
  38. Russell JA (1980) A circumplex model of affect. J Pers Soc Psychol 39(6):1161CrossRefGoogle Scholar
  39. Santos RB (2012) Crime analysis with crime mapping. Sage, Beverley HillsGoogle Scholar
  40. Sessar K, Sirotek J (2001) Společnost v období transformace a strach z kriminality. Sociologický Časopis 37(1):7–22Google Scholar
  41. Smith M, Bondi L, Davidson J (2012) Emotional geographies. Ashgate Publishing Ltd., LondonGoogle Scholar
  42. Stasíková L (2011) Relevantnost výskumu strachu z kriminality v urbánnej geografii. Geografický Časopis 63(4):325–343Google Scholar
  43. Wilson MW (2011) “Training the eye”: formation of the geocoding subject. Soc Cult Geogr 12(04):357–376CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Department of Development Studies, Faculty of SciencePalacký University OlomoucOlomoucCzech Republic
  2. 2.Department of Geoinformatics, Faculty of SciencePalacký University OlomoucOlomoucCzech Republic
  3. 3.Department of GeographyUniversity of CanterburyChristchurchNew Zealand

Personalised recommendations