Can We Foresee Landscape Interest? Maximum Entropy Applied to Social Media Photographs: A Case Study in Madrid
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 open...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-05-01
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| Series: | Land |
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| Online Access: | https://www.mdpi.com/2073-445X/11/5/715 |
| _version_ | 1797498571702927360 |
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| author | Nicolas Marine Cecilia Arnaiz-Schmitz Luis Santos-Cid María F. Schmitz |
| author_facet | Nicolas Marine Cecilia Arnaiz-Schmitz Luis Santos-Cid María F. Schmitz |
| author_sort | Nicolas Marine |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-10T03:35:14Z |
| format | Article |
| id | doaj.art-efc7635299d14e2dbe33e6a6682ca4e7 |
| institution | Directory Open Access Journal |
| issn | 2073-445X |
| language | English |
| last_indexed | 2024-03-10T03:35:14Z |
| publishDate | 2022-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Land |
| spelling | doaj.art-efc7635299d14e2dbe33e6a6682ca4e72023-11-23T11:47:54ZengMDPI AGLand2073-445X2022-05-0111571510.3390/land11050715Can We Foresee Landscape Interest? Maximum Entropy Applied to Social Media Photographs: A Case Study in MadridNicolas Marine0Cecilia Arnaiz-Schmitz1Luis Santos-Cid2María F. Schmitz3Department of Architectural Composition, Escuela Tecnica Superior de Arquitectura de Madrid, Universidad Politécnica de Madrid, 28040 Madrid, SpainDepartment of Biodiversity, Ecology and Evolution, Universidad Complutense de Madrid, 28040 Madrid, SpainDepartment of Biodiversity, Ecology and Evolution, Universidad Complutense de Madrid, 28040 Madrid, SpainDepartment of Biodiversity, Ecology and Evolution, Universidad Complutense de Madrid, 28040 Madrid, SpainCultural 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.https://www.mdpi.com/2073-445X/11/5/715cultural ecosystem servicessocial mediageotagged photographsmaximum entropy modelsMaxEnt |
| spellingShingle | Nicolas Marine Cecilia Arnaiz-Schmitz Luis Santos-Cid María F. Schmitz Can We Foresee Landscape Interest? Maximum Entropy Applied to Social Media Photographs: A Case Study in Madrid Land cultural ecosystem services social media geotagged photographs maximum entropy models MaxEnt |
| title | Can We Foresee Landscape Interest? Maximum Entropy Applied to Social Media Photographs: A Case Study in Madrid |
| title_full | Can We Foresee Landscape Interest? Maximum Entropy Applied to Social Media Photographs: A Case Study in Madrid |
| title_fullStr | Can We Foresee Landscape Interest? Maximum Entropy Applied to Social Media Photographs: A Case Study in Madrid |
| title_full_unstemmed | Can We Foresee Landscape Interest? Maximum Entropy Applied to Social Media Photographs: A Case Study in Madrid |
| title_short | Can We Foresee Landscape Interest? Maximum Entropy Applied to Social Media Photographs: A Case Study in Madrid |
| title_sort | can we foresee landscape interest maximum entropy applied to social media photographs a case study in madrid |
| topic | cultural ecosystem services social media geotagged photographs maximum entropy models MaxEnt |
| url | https://www.mdpi.com/2073-445X/11/5/715 |
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