1. Introduction & Objectives
Big Data and digital communication have the potential to enhance the study of Ecosystem Services in profound ways. For instance, the high-fidelity of user-generated content, social media data, and mobile phone data offer a unique window into the complex benefits of ecological systems, providing insight into the co-production of environmental values and knowledge, illuminating perceptions and interactions with the natural world, as well as revealing ecological patterns and processes through the captured media. These data are often several orders of magnitude larger than what can be obtained by traditional data collection such as surveys, and thus require unique approaches. Such novel data will likely play an increasing role in shaping our understanding of (a) relational values, (b) monetary estimates of ES at scale, and (c) ecological values across time and between countries.
While the rapidly growing body of research using big data is impressive, additional guidance is needed in this burgeoning field to increase its legitimacy and robustness in the context of ES research. Specific challenges include: (1) the lack of shared protocols to identify and deal with potentially sensitive issues, including protection of user privacy and safe data storage; (2) the development of best-practice and scalable approaches given the computational challenges associated with these huge datasets (e.g. machine learning); (3) the analysis of social, spatial and temporal representativeness biases; (4) ensuring data access given increasing regulation and restrictions by private and public entities, and shifting popularity and self-selection across social groups; and (5) developing approaches to groundtruth the results obtained with these data and explore their complementarity with alternative techniques and related data sources (e.g., PGIS, surveys, remote sensing).
Objective: This Thematic Working Group aims to stimulate debate and promote the application of big data in the characterisation of ES, facilitate communication within the growing community leveraging these techniques, and develop methods and support for the reproducibility and comparability of studies. Our aim is to promote the application of big data broadly in the characterisation of ES, facilitate communication within the growing community leveraging these techniques, and develop methods and support for the reproducibility and comparability of studies. It will produce a best practice for the reproducibility and ethical use of these data. It will synthesise and distribute new developments in the field for the benefit of the members. It will establish relationships with other working groups and related networks in order to broaden big data usage and application in other ES contexts.
2. Lead Team & Members
- Andrea Ghermandi, University of Haifa (co-chair)
- Johannes Langemeyer, Autonomous University of Barcelona (co-chair)
- Derek Van Berkel, University of Michigan (co-chair)
If you are interested in becoming a member of this Working group, please contact the current lead team members.
3. Activities and Outputs
- Database of researchers/ practitioners working on or interested in Big Data
- Sharing of key Big Data publications and reports
- Joint publications on development of conceptual and methodological frameworks for Big Data ES assessments
- Organization of special sessions at Global and Regional ESP conferences
- Future discussion group/ forum
- Consolidated collaboration with and WG representation in other related networks