Revealing Cultural Ecosystem Services through Instagram Images: The Potential of Social Media Volunteered Geographic Information for Urban Green Infrastructure Planning and Governance

Guerrero, Paul; Møller, Maja Steen; Olafsson, Anton Stahl; Snizek, Bernhard

doi:10.17645/up.v1i2.609

Cited by 128 publications

(88 citation statements)

References 34 publications

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“…This has included preferences for biodiversity extracted from Instagram and Flickr, where Hausmann et al (2017) found no significant difference compared to traditional surveys. The spatial distributions of images from Instagram for the City of Copenhagen have been found to show the main hotspots (Guerrero et al, 2016). Image feature extraction on crowd-sourced data using a neural network has been used to ascertain outdoor elements that are found to be scenic (Seresinhe, Preis and Moat, 2017) and the use of geo-tagged photos from Flickr have been used in multiple studies as a proxy for visitation.…”

Section: Introductionmentioning

confidence: 99%

Using social media, machine learning and natural language processing to map multiple recreational beneficiaries

et al. 2019

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Information and numbers on the use and appreciation of nature are valuable information for protected area (PA) managers. A promising direction is the utilisation of social media, such as the photosharing website Flickr. Here we demonstrate a novel approach, borrowing techniques from machine learning (image analysis), natural language processing (Latent Semantic Analysis (LSA)) and self-organising maps (SOM), to collect and interpret> 20,000 photos from the Camargue region in Southern France. From the perspective of Cultural Ecosystem Services (CES), we assessed the relationship between the use of the Camargue delta and the presence of natural elements by consulting local managers. Clustering algorithms applied to results of the LSA data revealed six distinct user groups, which included those interested in nature, ornithology, religious pilgrimage, general tourists and aviation enthusiasts. For each group, we produced high-resolution spatial and seasonal maps, which matched known recreational attractions and annual festivals in the Camargue. The accuracy of the group identification, and the spatial and temporal patterns of photo activity, in the Camargue delta were evaluated by local managers of the Camargue regional park. This study demonstrates how PA managers can harness socialmedia to monitor recreation and improve their management decision making

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Section: Introductionmentioning

confidence: 99%

Using social media, machine learning and natural language processing to map multiple recreational beneficiaries

et al. 2019

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“…New methods are being developed and combined to measure social place values, such as GPS tracking and linguistic approaches (Gosal et al 2018; Wartmann and Purves 2018) as well as social media volunteered geographic information (e.g. analyses of shared images and tags from social media activities) (Jenkins et al 2016;Guerrero et al 2016). Such applications, in spite of their specific methodological limitations (e.g.…”

Section: Limitations and Recommendations For Further Researchmentioning

confidence: 99%

Integrating sense of place in planning and management of multifunctional river landscapes: experiences from five European case studies

et al. 2019

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River landscapes are complex social-ecological systems with many benefits for people. A common challenge is to integrate social values in river planning and management. In particular, there is a paucity of research on the meaning and significance of place in river recreation and how people feel emotionally and spiritually connected to river landscapes. Based on five European case studies, this study compares different methods and approaches for mapping sense of place in river landscapes and subsequently addresses the question of how these studies can inform participatory processes. The case studies are set in diverse geographical, institutional and policy contexts, including the planning and evaluation of river restoration projects in Switzerland, Denmark, Germany and Spain and the monitoring of the effects of newly constructed river dams in the Netherlands. This comparative study is a first step in understanding the breadth of analytical and spatial approaches that can be used to assess sense of place in river landscapes and their implications for resilient river landscape planning and management.

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“…Indeed, georeferenced images have been used as proxies of landscape preference at both regional (Tenerelli et al 2016, Yoshimura andHiura 2017) and continental scales (van Zanten et al 2016). The potential value of image descriptions has also been recognized in exploring landscapes (Dunkel 2015) and extracting information related to cultural ES (Richards and Friess 2015, Guerrero et al 2016, Figueroa-Alfaro and Tang 2017, Oteros-Rozas et al 2017. User-generated content can at least start to bridge the data gap between expert and bottom-up perceptions of landscape, and provide more spatially extensive data, at lower costs.…”

Section: Challenges In Landscape Characterizationmentioning

confidence: 99%

Describing and comparing landscapes using tags, texts, and free lists: an interdisciplinary approach

Wartmann

Acheson

Purves

2018

International Journal of Geographical Information Science

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How do people perceive landscapes? What elements of the landscape do they identify as characteristic of a landscape? And how can we arrive at descriptions, and ultimately representations that better reflect people's notions of landscapes? In this study, we collected landscape descriptions from five landscape types at 10 study sites in Switzerland. For each site, we collected data from three sources: free lists with participants, hiking blogs, and Flickr tags. Free lists were obtained through on-site interviews with visitors, hiking blogs were gathered by focused crawling of web content, and Flickr tags were selected based on spatial footprints obtained from the hiking blogs. We quantitatively compared landscape descriptions between data sources and landscape types using cosine similarity. We found that descriptions from the same data source were significantly more similar, irrespective of landscape type. Descriptions from the same landscape type were more similar, but only within the same data source. Through a qualitative analysis of different aspects of landscape in our content, we found that each data source offered a different distribution of landscape aspects. For example, while Flickr tags contained high proportions of toponyms, they contained little content relating to sense of place. In contrast, hiking blogs contained more information about sense of place. Our approach combining these varied textual sources thus offers a more holistic view on landscapes. This study constitutes a step toward extracting semantically rich descriptions of landscapes from a variety of sources and using this information to distinguish different landscapes, with potential applications for landscape monitoring and management.

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Revealing Cultural Ecosystem Services through Instagram Images: The Potential of Social Media Volunteered Geographic Information for Urban Green Infrastructure Planning and Governance

Cited by 128 publications

References 34 publications

Using social media, machine learning and natural language processing to map multiple recreational beneficiaries

Using social media, machine learning and natural language processing to map multiple recreational beneficiaries

Integrating sense of place in planning and management of multifunctional river landscapes: experiences from five European case studies

Describing and comparing landscapes using tags, texts, and free lists: an interdisciplinary approach

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