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Author (up) Heikinheimo, V.; Toivonen, T.,
Title Critical comparison of social media and other user-generated geographic information as a source of visitor information – lessons learned in the SoMeCon-project Type
Year 2021 Publication The 10th MMV Conference: Managing outdoor recreation experiences in the Anthropocene – Resources, markets, innovations Abbreviated Journal
Volume MINA fagrapport Issue Pages 294-295
Keywords MMV10
Abstract Up-to-date information about outdoor recreation experiences is important for the planning and management of national parks and other outdoor destinations. User-generated data such as geotagged social media posts and GPS-tracks shared via sports applications have emerged as potential new data sources to complement on-site counters and surveys. There are considerable amounts of geographic information available from digital platforms and mobile devices representing the movements, activities and preferences of visitors, and these data have been increasingly used for studying visits to national parks and green spaces..This presentation draws together our findings from the Social Media Data for Conservation Science -project SoMeCon (2016-2021). Our main objectives were to 1) gain methodological understanding about social media and other user-generated data sets as a source of geographic information, and 2) to provide new information about the spatial and temporal patterns of human activities in national parks and green spaces. We compared social media data to official visitor statistics from Finnish and South African national parks, and social media to other sources of user-generated geographic information (sports app data, mobile network data, PPGIS data) from urban green spaces in Helsinki, Finland.
Call Number Serial 4331
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Author (up) Olafsson, A.S.; Purves, R.S.; Garcia-Martin, M.; Wartmen, F.; Fagerholm, N.; Torralba, M.; Albert, C.; Verbrugge, L.; Heikinheimo, V.; Kaaronen, R.; Hartmann, M.; Plieninger, T.; Raymond, C.,
Title Comparing landscape value patterns between participatory mapping and social media content across Europe. Type
Year 2021 Publication The 10th MMV Conference: Managing outdoor recreation experiences in the Anthropocene – Resources, markets, innovations Abbreviated Journal
Volume MINA fagrapport Issue Pages 292-293
Keywords MMV10
Abstract Visitor monitoring and mapping techniques are rapidly evolving fuele…Visitor monitoring and mapping techniques are rapidly evolving fueled by open georeferenced data and social media opportunities. Knowledge on how visitors use and value landscapes is increasingly elucidated by social media data or user-generated data passively contributed by online communities. Examples of this is the use of data from social media such as Flickr, where users share and store geocoded images in an online platform. Here images, locations and associated tags is opportunistic crowdsourced by researchers and planners to conceptually and spatially elicit landscape values such as cultural ecosystem services and relational values.At the same time, integrated landscape planning and management has increasingly focus on planning ideals of deliberative processes, co-creation and inclusion of diverse values. Examples of this is participatory mapping techniques aimed to support the inclusion of diverse values held by residents and visitors into integrated landscape management. By the use of online public participation GIS (PPGIS), participants are actively recruited to purposely map socio-cultural values about specific landscapes.The values data collated using active participatory mapping techniques and passive user generated data is rarely compared.In this study, we bring PPGIS and Flickr together in an exploration and discussion of the similarities and differences. In contrast to previous comparative studies focused on single study site, we expand the analyses from a single site to cross-site analyses of 19 landscapes across Europe (in 11 countries). We argue that in order for planners to harness the qualities of both – we need to place a spotlight on strengths and shortcomings of each method and core opportunities for complementary use. We do this by a direct comparison of the spatial distribution, intensity and type of landscape values elicited using PPGIS and Flickr data.Moreover, we relate similarities or differences to specific landscape characteristics and types of landscape values.
Call Number Serial 4330
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Author (up) Toivonen, T.; Heikinheimo, V.,
Title Using Mobile Big Data to assess visits to national parks before and during COVID- 19 Type
Year 2021 Publication The 10th MMV Conference: Managing outdoor recreation experiences in the Anthropocene – Resources, markets, innovations Abbreviated Journal
Volume MINA fagrapport Issue Pages 78-79
Keywords MMV10
Abstract The mobility restrictions related to COVID-19 pandemic have resulted in the biggest disruption to individual mobilities in modern times. The crisis is clearly spatial in nature, and examining the geographical aspect is important in understanding the broad implications of the pandemic (Oliver et al. 2020). Visitations to national parks or other natural areas have experienced a tremendous change during the pandemic. In some areas, like in Finland, visitors seeking for experiences or less crowded places have crowded national parks. This has caused a need for national park managers to add services to the parks. In other places attracting high numbers of international tourists, like Madagascar, the visitor numbers have dropped drastically leading to problems of local livelihoods and even pressure to use land for alternative purposes like food production (Eklund et al. 2020).To understand these changes and their impact, there is a need to monitor how and where people use natural areas. Mobile big data (data collected by mobile phone operators or various apps) has been considered valuable for conservation already for some time (Di Minin et al. 2015; Tenkanen et al. 2017; Toivonen et al. 2019). The pandemic has highlighted the high potential of mobile big data even further (Poom et al. 2020). Mobile Big Data makes it possible to study the spatial effects of the crisis with spatiotemporal detail at the national and global scales. The data is being collected continuously, allowing monitoring change over time. The importance has not been left unnoticed: Some companies, like Google and Apple, have shared previously inaccessible information about peoples mobility patterns openly online, allowing, for a limited time period, new analyses also about visits to nature.
Call Number Serial 4233
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Author (up) Vaisanen, T.; Heikinheimo, V.; Hiippala, T.; Toivonen, T.,
Title Exploring human-nature interactions in national parks with social media photo-graphs and computer vision Type
Year 2021 Publication The 10th MMV Conference: Managing outdoor recreation experiences in the Anthropocene – Resources, markets, innovations Abbreviated Journal
Volume MINA fagrapport Issue Pages 248-249
Keywords MMV10
Abstract Understanding the activities and preferences of visitors is crucial for managing protected areas and planning conservation strategies. User-generated geographic information such as photographs shared on social media have emerged as new data sources to complement more traditional visitor information such as on-site surveys. However, analyzing large volumes of photographs manually is a laborious task. Automated analysis of the rich textual and visual content on social media data offers new opportunities for understanding human presence and activities in nature (Toivonen et al. 2019). Approaches for textual and content analysis have been widely developed under the umbrella of conservation culturomics (Ladle et al. 2016). They have been recognized as a useful data source for nature conservation. At the same time, automated analysis visual content has remained rather underexplored when mapping human activities in nature. In this presentation we present our findings of using computer vision methods to explore human-nature interactions from social media photographs and their applicability to visitor monitoring of protected areas. Our main questions are: What types of information can off-the-shelf computer vision methods extract from social media photographs, in terms of activities and preferences of people? Do different visitor groups share different types of photographs from national parks? How does photographic content vary between different types of national parks? 
Call Number Serial 4311
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