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An Urban Image Stimulus Set Generated from Social Media

Author

Listed:
  • Ardaman Kaur

    (Department of Advertising and Public Relations, Michigan State University, East Lansing, MI 48824, USA)

  • André Leite Rodrigues

    (Centre of Geographical Studies, Institute of Geography and Spatial Planning, University of Lisbon, 1600-276 Lisbon, Portugal)

  • Sarah Hoogstraten

    (Department of Advertising and Public Relations, Michigan State University, East Lansing, MI 48824, USA)

  • Diego Andrés Blanco-Mora

    (Institute of Physiology, Lisbon School of Medicine, University of Lisbon, 1649-004 Lisbon, Portugal)

  • Bruno Miranda

    (Institute of Physiology, Lisbon School of Medicine, University of Lisbon, 1649-004 Lisbon, Portugal
    Institute of Molecular Medicine, University of Lisbon, 1649-004 Lisbon, Portugal)

  • Paulo Morgado

    (Centre of Geographical Studies, Associate Laboratory TERRA, Institute of Geography and Spatial Planning, University of Lisbon, 1600-276 Lisbon, Portugal)

  • Dar Meshi

    (Department of Advertising and Public Relations, Michigan State University, East Lansing, MI 48824, USA)

Abstract

Social media data, such as photos and status posts, can be tagged with location information (geotagging). This geotagged information can be used for urban spatial analysis to explore neighborhood characteristics or mobility patterns. With increasing rural-to-urban migration, there is a need for comprehensive data capturing the complexity of urban settings and their influence on human experiences. Here, we share an urban image stimulus set from the city of Lisbon that researchers can use in their experiments. The stimulus set consists of 160 geotagged urban space photographs extracted from the Flickr social media platform. We divided the city into 100 × 100 m cells to calculate the cell image density (number of images in each cell) and the cell green index (Normalized Difference Vegetation Index of each cell) and assigned these values to each geotagged image. In addition, we also computed the popularity of each image (normalized views on the social network). We also categorized these images into two putative groups by photographer status (residents and tourists), with 80 images belonging to each group. With the rise in data-driven decisions in urban planning, this stimulus set helps explore human–urban environment interaction patterns, especially if complemented with survey/neuroimaging measures or machine-learning analyses.

Suggested Citation

  • Ardaman Kaur & André Leite Rodrigues & Sarah Hoogstraten & Diego Andrés Blanco-Mora & Bruno Miranda & Paulo Morgado & Dar Meshi, 2023. "An Urban Image Stimulus Set Generated from Social Media," Data, MDPI, vol. 8(12), pages 1-12, December.
    • Handle: RePEc:gam:jdataj:v:8:y:2023:i:12:p:184-:d:1292511
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    References listed on IDEAS

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    1. Agnieszka Olszewska-Guizzo & Angelia Sia & Anna Fogel & Roger Ho, 2020. "Can Exposure to Certain Urban Green Spaces Trigger Frontal Alpha Asymmetry in the Brain?—Preliminary Findings from a Passive Task EEG Study," IJERPH, MDPI, vol. 17(2), pages 1-10, January.
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