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University Students’ Perceptions of the Inner Cities of Murcia and Valencia

Author

Listed:
  • Morales Yago Francisco José

    (Department of Geography, Universidad Nacional de Educación a Distancia, Madrid, Spain)

  • de Lázaro y Torres María Luisa

    (Department of Geography, Universidad Nacional de Educación a Distancia, Madrid, Spain)

  • Gomez Ruiz María Luisa

    (Department of Didactics of Experimental, Social and Mathematical Sciences, Universidad Complutense de Madrid, Spain)

Abstract

Inner city perceptions create a mental representation from different approaches: a visual approach, carried out through observation and description; a second approach, focused on evaluation and analysis of a city; and a third approach, which integrates the feelings that a space evokes in individuals known as the sense of the place. In the final analysis the aforementioned approach condition the behaviour (action-decision) of individuals. Image capture mainly happens while people walk in, travel to or visit a city using different ways to get around and they organize a mental map of the city. University students were selected from two Spanish cities: Murcia (215 respondents) and Valencia (300 respondents) to reply to a survey and to draw a map of their city. Results of the images of the cities in which they were currently living also proved useful in providing guidelines on sustainable growth of cities and in detecting deficiencies in order to correct them. The research model could be used in other cities throughout the world.

Suggested Citation

  • Morales Yago Francisco José & de Lázaro y Torres María Luisa & Gomez Ruiz María Luisa, 2018. "University Students’ Perceptions of the Inner Cities of Murcia and Valencia," Quaestiones Geographicae, Sciendo, vol. 37(3), pages 75-85, September.
  • Handle: RePEc:vrs:quageo:v:37:y:2018:i:3:p:75-85:n:5
    DOI: 10.2478/quageo-2018-0026
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    References listed on IDEAS

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    1. Philip Salesses & Katja Schechtner & César A Hidalgo, 2013. "The Collaborative Image of The City: Mapping the Inequality of Urban Perception," PLOS ONE, Public Library of Science, vol. 8(7), pages 1-12, July.
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