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Crowdsourcing safety perceptions of people: Opportunities and limitations

  • Martin Traunmueller*
  • , Paul Marshall
  • , Licia Capra
  • *Corresponding author for this work

    Research output: Chapter in Book/Report/Conference proceedingConference Contribution (Conference Proceeding)

    14 Citations (Scopus)

    Abstract

    Online crowdsourcing has successfully been used as a paradigm to collect large amount of perceptions about our cities quickly and cheaply, enabling social scientists to quantitatively test urban theories at scale. While doing so, researchers have not focussed on getting answers from specific demographics, relying on a self-selected crowd instead. However, existing theories suggest that knowing who the respondents are is crucial for understanding safety perceptions about people (instead of, for example, about the built environment). In this case to quantitatively validate theories, it is not just the amount of data that matters, but also what demographics participate (or not). In this paper we investigate to what extent online crowdsourcing can be used for the specific case of safety perceptions about people.We built an image-based online crowdsourcing platform, collected safety perception ratings and background information from more than 700 people and used them to quantitatively evaluate established theories based on qualitative research. On one hand, we show in this paper that online, image-based crowdsourcing can be used to gather perceptions about people too, not just architecture, confirming established theories based on qualitative work. Furthermore, we are able to uncover detailed interactions that would be challenging to grasp using qualitative methods. On the other hand, we show limitations of using crowdsourcing as a method. By not controlling who makes up the crowd, we were not able to investigate all theories as we did not reach all user groups that have been discussed in qualitatitve research.

    Original languageEnglish
    Title of host publicationSocial Informatics - 7th International Conference, SocInfo 2015, Proceedings
    PublisherSpringer-Verlag Berlin
    Pages120-135
    Number of pages16
    Volume9471
    ISBN (Print)9783319274324
    DOIs
    Publication statusPublished - 1 Jan 2015
    Event7th International Conference on Social Informatics, SocInfo 2015 - Beijing, China
    Duration: 9 Dec 201512 Dec 2015

    Publication series

    NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
    Volume9471
    ISSN (Print)0302-9743
    ISSN (Electronic)1611-3349

    Conference

    Conference7th International Conference on Social Informatics, SocInfo 2015
    Country/TerritoryChina
    CityBeijing
    Period9/12/1512/12/15

    UN SDGs

    This output contributes to the following UN Sustainable Development Goals (SDGs)

    1. SDG 16 - Peace, Justice and Strong Institutions
      SDG 16 Peace, Justice and Strong Institutions

    Research Groups and Themes

    • Bristol Interaction Group

    Keywords

    • Crime studies
    • Crowdsourcing
    • Perception
    • Social studies
    • Theory validation

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