An Urban Image Stimulus Set Generated from Social Media

Ardaman Kaur, André Leite Rodrigues, Sarah Hoogstraten, Diego Andrés Blanco-Mora, Bruno Miranda, Paulo Morgado and Dar Meshi ()
Additional contact information
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
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

Data, 2023, vol. 8, issue 12, 1-12

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.

Keywords: social media; urban photographs; human–urban environment interactions (search for similar items in EconPapers)
JEL-codes: C8 C80 C81 C82 C83 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
https://www.mdpi.com/2306-5729/8/12/184/pdf (application/pdf)
https://www.mdpi.com/2306-5729/8/12/184/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jdataj:v:8:y:2023:i:12:p:184-:d:1292511

Access Statistics for this article

Data is currently edited by Ms. Cecilia Yang

More articles in Data from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().