Crowdsourcing geo-information on landscape perceptions and preferences: A review

Abstract

This paper offers a summary of different crowdsourcing modes to collect geo-information on landscape perception & preferences and cultural ecosystem services. Crowdsourcing modes range from harvesting information passively transmitted by large groups on the web to actively engaging the crowd to generate data by using dedicated mobile apps and web-platforms. The latter, active crowdsourcing projects, were described in more detail by analysing the organizational variables of the twelve projects that were identified. Crowdsourcing has great potential to advance the field of landscape perception & preference research as it enables the in-situ collection of real-time, location-based data. One of the main limitations of reviewed active and passive crowdsourcing modes, lies in the fact that sample selection bias easily occurs and sample representativeness of any target population has been proven hard to achieve. Often crowdsourcing projects are implemented with a strong focus on technical aspects and content, but with insufficient attention for participant engagement. Projects would benefit from more inter-and transdisciplinary approaches and professionalizing campaigns, and thereby bringing participant engagement to the heart of the project. We recommend more attention to be placed towards awareness raising, diversification of formats and activities to reach a larger diversity of participants, structured tracking of performance indicators and learning from participants' feedback. Such strategies aim at enhancing participation and reducing bias in participant selection, which constrains the usefulness of the results for research, planning and policy.

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

“…In our case, there was no visitor data available to assess whether the PPGIS data were biased towards mid‐aged males and educated participants as shown in similar studies (Brown et al, 2015; Bubalo et al, 2019). Sampling design plays a crucial role in capturing a representative sample of the population or a targeted population segment (Brown, 2017; Brown & Kyttä, 2014; Brown et al, 2019).…”

“…Third, researchers need to make choices between the number of participants, representativeness and time frame available when using these different crowdsourced data. Crowdsourced data might be biased towards different users depending on the type of social media platform, knowledge about an area or place of residence (Brown & Kyttä, 2014; Bubalo et al, 2019; Ruths & Pfeffer, 2014). Demographic data are often not reported by the social media platforms.…”

“…A wide range of methods and approaches have been used to elucidate the diverse perspectives on the cultural benefits provided by nature (Small et al, 2017; Teff‐Seker & Orenstein, 2019; Tew, Simmons, & Sutherland, 2019). Among these are crowdsourcing methods which have the potential to deliver spatial information of NCP from a diverse range of citizens at a large scale of relevance to conservation (Bubalo, van Zanten, & Verburg, 2019). There are two main crowdsourcing approaches that have gained popularity in recent years: passive and active crowdsourcing.…”

StarBorn: Towards making in‐situ land cover data generation fun with a location‐based game

“…However, even though technological advances have provided scientists with new accessible tools for data generation, how to motivate users to contribute data remains an issue (Crall et al., ). Gamification has been identified as one approach to motivating users to contribute to scientific crowdsourcing efforts (Bubalo, Zanten, & Verburg, ; Fritz, See, & Brovelli, ; Hamari & Sarsa, ).…”

“…Gamification has been identified as one approach to motivating users to contribute to scientific crowdsourcing efforts (Bubalo, Zanten, & Verburg, 2019;Fritz, See, & Brovelli, 2017;Hamari & Sarsa, 2014).…”