Corresponding author: Andrea Ros-Candeira (
Academic editor: Yiannis Matsinos
This dataset provides crowd-sourced and georeferenced information useful for the assessment of cultural ecosystem services in the Sierra Nevada Biosphere Reserve (southern Spain). Data were collected within the European project ECOPOTENTIAL focused on Earth observations of ecosystem services. The dataset comprises 778 records expressing the results of the content analysis of social media photos published in Flickr. Our dataset is illustrated in this data paper with density maps for different types of information.
Ros-Candeira A, Moreno-Llorca R, Alcaraz-Segura D, Bonet-García FJ, Vaz AS (2020) Social media photo content for Sierra Nevada: a dataset to support the assessment of cultural ecosystem services in protected areas. Nature Conservation 38: 1–12.
The modern human epoch is characterised by dynamic social-ecological changes, with local communities and individuals showing an important role in ecosystem integrity and health (
Ecosystem services are generally known as the contributions that are obtained from nature (
Evaluations of cultural ecosystem services have been struggling with the inability to capture their subjectivity and utilitarian value (
A plethora of social media information has been produced and shared at unprecedented rates, revolutionising traditional methods to address human culture (i.e. culturomics;
Despite increasing evaluations of social media information, there is a general deficiency of publicly available databases of photo content analysis. Analysing and mapping the cultural value of ecosystems allow the identification and location of places where nature contributes most to cultural identity and heritage, human health, environmental education and opportunities for nature enjoyment (
Our expectation in describing and making available this dataset is to promote the sharing of other similar datasets in order to locate, describe and quantify potential cultural services in protected areas worldwide.
The dataset was compiled within the context of the H2020 project “ECOPOTENTIAL: improving future ecosystem benefits through earth observations” (
The dataset covers a 1,722 km2 area corresponding to the UNESCO Biosphere Reserve Sierra Nevada. Sierra Nevada is a mountainous region located in Andalusia (Granada and Almería provinces), in southern Spain. The altitude ranges from 860 m a.s.l to the summits, where the highest peak reaches 3,479 m a.s.l. The climate is Mediterranean, presenting cold winters and hot summers, with pronounced summer drought (July-August). The annual average temperature decreases in altitude from 12–16°C below 1,500 m to 0°C above 3,000 m a.s.l. and the annual average precipitation is about 600 mm. Annual precipitation ranges from less than 250 mm in the lowest parts of the mountain range to more than 700 mm in the summit areas, where, above 2,000 m altitude, winter precipitation is mainly in the form of snow. Topographically, it is a heterogeneous area, with strong climatic contrasts between the sunny, dry south-facing slopes and the shaded, wetter north-facing slopes.
Sierra Nevada hosts more than 80 endemic plant species (
Regarding its general socioeconomic characteristics, there were 61 municipalities with 90,048 inhabitants in 2017. The population average age is 48.3 years (ten years greater than the population of large urban areas closer to the national park). The main economic activity is services, mostly related to rural tourism (45% of people employed, 75% of registered businesses). Secondary economic activities are farming and construction sector (25% of people employed in each). Finally, the percentage of people working in industrial sector stands around 5%. Registered unemployment in relation to total population is lower than the urban areas (9.3% versus 10.1%), but the net income per inhabitant is half that of urban areas (3,597€ versus 7,158€) (
36°55'04"N and 37°14'25"N Latitude; 3°36'26"W and 2°35'41"W Longitude
1972–2017
We focused on the screening of photos from a popular social media platform: Flickr (
We stratified our sampling over four strata differing in their nature protection regime (National versus Natural Park) and tourist dynamics (rural versus recreational tourism). Specifically, we randomly selected a set of 210 photos across the limits of the National Park (corresponding mostly to the area with the highest elevation of the Biosphere) and another set of 210 photos within the remaining area, coincident with the Natural Park. A third set of 210 photos was considered across ski resorts, corresponding to areas with the highest movement of visitors in autumn and winter. The remaining photos (n = 259) were selected considering the rural areas of the reserve, which were expected to host more visitors during spring and summer. Our final dataset comprised 778 photos from 708 different Flickr users.
We checked each individual photo (n = 889) to evaluate its suitability for the content analysis: unidentifiable photos (e.g. due to poor quality) or photos capturing non-natural and indoor elements (e.g. inside parking places or private and business properties) were not considered for the content analysis. Additionally, photos which were not available, for instance, since they were eliminated or protected by users’ rights, were also not analysed. After applying the former exclusion criteria, we conducted a “directed content analysis” (following, for example,
Variables and categories considered for the classification of social media photo content, including their description and classification criteria.
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Coordinates | Latitude of the photo | Coordinate reference system: EPSG 4326-WGS 84 |
Longitude of the photo | Coordinate reference system: EPSG 4326-WGS 84 | |
Date | The date when the photo was taken | Format: day/month/year |
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Main content | Birdwatching | A bird or group of birds are the main content of photo |
Fauna/Flora | Fauna and/or flora are the main content of the photo | |
Nature & Landscape/Seascape | The photo is mainly focused on nature or landscapes/seascapes in general | |
Cultural | Cultural elements are the main content, including harvest of pine nuts or traditional buildings | |
Religious | Religious elements are the main content, including processions, pilgrimage, churches, carriages or carts as part of pilgrimage | |
Rural | Elements associated with rural tourism are the main content, including lodges, rural activities, villages or other related infrastructures | |
Sports | Sports elements are the main content, such as those associated with biking, hiking or running | |
Gastronomy | Gastronomy is the main content of photo, including dining at restaurants or traditional products | |
Recreation | The main content of the photo is on recreational areas or similar public infrastructures, including barbecues or playgrounds | |
Other type | The photo is dominated by other elements that are not related with the former categories | |
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Sports activities | Alpine skiing | Alpine skiing is the topic of photo |
Cross country skiing | Cross country skiing the topic of photo | |
Climbing | Climbing is the topic of photo | |
Road cycling | Road cycling is the topic of photo | |
Mountain cycling | Mountain cycling is the topic of photo | |
Downhill cycling | Downhill cycling the topic of photo | |
Running | Running is the topic of photo | |
Hiking | Hiking is the topic of photo | |
Paragliding | Paragliding is the topic of photo | |
Horse riding | Horse riding is the topic of photo | |
Canoeing | Canoeing is the topic of photo | |
Other type | Other type of sports activity is the main topic of photo | |
Not applicable | The photo is not focused on any sports activity | |
Nature and human features | High mountain | High mountain is the topic of photo |
Mid-mountain | Mid-mountain is the topic of photo | |
Mountain peak | Mountain peak is the topic of photo | |
Horizon | Horizon is the topic of photo | |
Natural forest | Natural forest is the topic of photo | |
Anthropic forest | Anthropic forest is the topic of photo | |
Shrub | Shrub is the topic of photo | |
Grassland | Grassland is the topic of photo | |
Lake, pond | Lake is the topic of photo | |
River | River is the topic of photo | |
Sky | Sky is the topic of photo | |
Urban/built environment | Urban/built environment is the topic of photo | |
Non-urban/built environment, infrastructures | Non-urban/built environment, infrastructure, is the topic of photo (e.g. rural infrastructure, refuges and recreation infrastructure) | |
Humans, selfies | People, including selfies, are the topic of photo | |
Other type | Other type of feature is the main topic of photo | |
Not applicable | These categories are not applicable | |
Faunal groups | Mammal | Mammal is the topic of photo |
Ungulate | Wild ungulate is the topic of photo (e.g. Iberian ibex) | |
Waterbird | Waterbird is the topic of photo | |
Wader | Wader is the topic of photo | |
Raptor | Raptor is the topic of photo | |
Passerine | Passerine is the topic of photo | |
Reptile | Reptile is the topic of photo | |
Fish | Fish is the topic of photo | |
Insect | Insect is the topic of photo | |
Other type | Other type of fauna is the main topic of photo | |
Not applicable | The photo is not focused on any type of fauna |
In order to provide more detailed information about the photo’s content, we further classified each photo considering: (1) The main nature and human features represented in the photo (e.g. lake, natural forest, mountain peak etc.). Again, more than one category per variable could be attributed in cases in which an individual photo showed the dominance of different nature and human features. (2) The type of prevailing sports activity (e.g. hiking, horse riding etc.), when one of the main topics of the photo was “Sports”. (3) The represented faunal groups (e.g. ungulate, insect etc.), in those cases in which the main content of the photo was focused on fauna (e.g. categories “Fauna/Flora” and “Birdwatching”). Therefore, these last two variables (Sports activities and Faunal groups) depended on the classification attributed to the first variable “Main content”.
The classification of photos into the above-mentioned categories was evaluated by two independent users. Before analysing the content of the whole dataset, a test set of 100 randomly chosen records was first considered and classified. After analysing this test set, the classification procedure was refined for a second round. For both classification rounds, the consistency between the two users was analysed through general agreement and kappa statistics. The statistics indicated an increase in classification consistency from the first to the second test set. Specifically, a good consistency between users was found, with agreement levels ranging between 65% (sports activities) and 88% (faunal groups) and kappa values between 0.58 (nature and human features) and 0.60 (sports activities).
Figure
Location of Flickr photos considered in the dataset (n = 778). The location of each photo is represented by a dark circle. For visualisation purposes, the map also shows heatspots of photos (kernel density), highlighting the areas with the highest and lowest photo densities in Sierra Nevada Biosphere Reserve.
This spatial pattern is also evident for the different categories assigned to the dataset (Figure
Illustration of Flickr photos showing: (
We are confident that our dataset (and derived maps) add detail on the potential location of different cultural contributions to people. Specifically, the spatial projections derived from this study can provide useful information for management decisions, for example, on prioritising land planning efforts and resources (
Despite the usefulness of our dataset, some considerations must be recognised when using this and other similar datasets, in the cultural services’ arena. For instance, the spatial reference precision of social media photographs can bias the geolocation of collected data (
This work has been carried out within the H2020 project “ECOPOTENTIAL: Improving future ecosystem benefits through earth observations” (