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Can We Foresee Landscape Interest? Maximum Entropy Applied to Social Media Photographs: A Case Study in Madrid

Author

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  • Nicolas Marine

    (Department of Architectural Composition, Escuela Tecnica Superior de Arquitectura de Madrid, Universidad Politécnica de Madrid, 28040 Madrid, Spain)

  • Cecilia Arnaiz-Schmitz

    (Department of Biodiversity, Ecology and Evolution, Universidad Complutense de Madrid, 28040 Madrid, Spain)

  • Luis Santos-Cid

    (Department of Biodiversity, Ecology and Evolution, Universidad Complutense de Madrid, 28040 Madrid, Spain)

  • María F. Schmitz

    (Department of Biodiversity, Ecology and Evolution, Universidad Complutense de Madrid, 28040 Madrid, Spain)

Abstract

Cultural Ecosystem Services (CES) are undervalued and poorly understood compared to other types of ecosystem services. The sociocultural preferences of the different actors who enjoy a landscape are intangible aspects of a complex evaluation. Landscape photographs available on social media have opened up the possibility of quantifying landscape values and ecosystem services that were previously difficult to measure. Thus, a new research methodology has been developed based on the spatial distribution of geotagged photographs that, based on probabilistic models, allows us to estimate the potential of the landscape to provide CES. This study tests the effectiveness of predictive models from MaxEnt, a software based on a machine learning technique called the maximum entropy approach, as tools for land management and for detecting CES hot spots. From a sample of photographs obtained from the Panoramio network, taken between 2007 and 2008 in the Lozoya Valley in Madrid (Central Spain), we have developed a predictive model of the future and compared it with the photographs available on the social network between 2009 and 2015. The results highlight a low correspondence between the prediction of the supply of CES and its real demand, which indicates that MaxEnt is not a sufficiently useful predictive tool in complex and changing landscapes such as the one studied here.

Suggested Citation

  • Nicolas Marine & Cecilia Arnaiz-Schmitz & Luis Santos-Cid & María F. Schmitz, 2022. "Can We Foresee Landscape Interest? Maximum Entropy Applied to Social Media Photographs: A Case Study in Madrid," Land, MDPI, vol. 11(5), pages 1-13, May.
    • Handle: RePEc:gam:jlands:v:11:y:2022:i:5:p:715-:d:812103
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    References listed on IDEAS

    as
    1. Patricio Sarmiento-Mateos & Cecilia Arnaiz-Schmitz & Cristina Herrero-Jáuregui & Francisco D. Pineda & María F. Schmitz, 2019. "Designing Protected Areas for Social–Ecological Sustainability: Effectiveness of Management Guidelines for Preserving Cultural Landscapes," Sustainability, MDPI, vol. 11(10), pages 1-20, May.
    2. Richards, Daniel R. & Tunçer, Bige, 2018. "Using image recognition to automate assessment of cultural ecosystem services from social media photographs," Ecosystem Services, Elsevier, vol. 31(PC), pages 318-325.
    3. Oleksandr Karasov & Stien Heremans & Mart Külvik & Artem Domnich & Igor Chervanyov, 2020. "On How Crowdsourced Data and Landscape Organisation Metrics Can Facilitate the Mapping of Cultural Ecosystem Services: An Estonian Case Study," Land, MDPI, vol. 9(5), pages 1-17, May.
    4. E. Seda Arslan & Ömer K. Örücü, 2021. "MaxEnt modelling of the potential distribution areas of cultural ecosystem services using social media data and GIS," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 23(2), pages 2655-2667, February.
    5. Yoshimura, Nobuhiko & Hiura, Tsutom, 2017. "Demand and supply of cultural ecosystem services: Use of geotagged photos to map the aesthetic value of landscapes in Hokkaido," Ecosystem Services, Elsevier, vol. 24(C), pages 68-78.
    6. Cheng, Xin & Van Damme, Sylvie & Li, Luyuan & Uyttenhove, Pieter, 2019. "Evaluation of cultural ecosystem services: A review of methods," Ecosystem Services, Elsevier, vol. 37(C), pages 1-1.
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    Cited by:

    1. Jiani Zhang & Xun Zhu & Ming Gao, 2022. "The Relationship between Habitat Diversity and Tourists’ Visual Preference in Urban Wetland Park," Land, MDPI, vol. 11(12), pages 1-19, December.

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