Using crowdsourced spatial data from Flickr vs. PPGIS for understanding nature's contribution to people in Southern Norway

Crowdsourced data can provide spatially explicit data on the contribution of nature to people. Spatial information is essential for effectively managing the diverse relationships that people have with nature, but the potential and limits of using crowdsourcing data to generate maps for conservation purposes need further research. Passive crowdsourcing tools include social media platforms where photos and user‐generated tags are shared among users, whereas active crowdsourcing, such as public participatory geographic information system (PPGIS), provides an online platform for mapping place attributes such as values, experiences and preferences. In this study, we assess the spatial information gained through using Flickr (a photo sharing platform) and PPGIS (an online mapping platform) platforms for conservation planning to understand differences and similarities on the spatial distribution of values captured by the two platforms, and to identify what environmental and infrastructure variables correlate best with the distribution of values. We test these tools in Southern Norway including protected areas and the surrounding zones. We analysed non‐spatial (using chi‐square and Spearman rank correlation) and spatial (using clustering, Maxent and distribution overlap) data to identify differences between the two datasets and the values represented therein. We found large differences in spatial distribution using these two datasets, with Flickr data concentrated outside the protected areas and near roads, whereas PPGIS provided more fine‐scale data on diverse values in locations inaccessible by roads within the protected areas. Flickr can be used for generating regional scale data of scenic landscapes or routes, but PPGIS performs better for management of nature qualities appreciated by different user groups within protected areas. We discuss the pros and cons of using each data source and when each dataset is more suitable to be used in protected area management.