Using Big Data to Better Understand the Socioecological Implications of Protected Areas
Creators: name: Trammell, Jamie E.
name: Specht, Alison
name: Reddy, Sheila
name: Evans, Jeffrey
name: Bayarsky, Alec
name: Stall, Shelley
Publisher: name: Zenodo
Year: 2020

description: This poster was presented at the AGU Fall Meeting 2020 during the session: Bridging Systems Modeling Advances Across Socioecological Domains II Posters (GC008) Link to Meeting Site Link to iPoster <strong>Abstract:</strong> Protected areas (PAs) are regarded as the backbone of biodiversity conservation, but their existence is often tenuous, and they often occupy contested space. To be sustainable, the local and wider community needs to be supportive, and for this to happen, they should not be disadvantaged by the existence of a PA, rather the PA should enhance the well-being of the communities that live around them. With large amounts of satellite and GIS data being generated over the past 40 years, and a wealth of data published on the socio-economic status of many countries around the world, we have unprecedented spatial and longitudinal resources. Coupled with the increasing sophistication of statistical methodologies, we have a great opportunity to dig deeper into the relationship between a PA and their surrounding community, and to develop ways to share this knowledge to improve decision-making across the globe. We present here some initial work that begins to address this opportunity through a multi-national project called “Building New Tools for Data Sharing and Re-use through a Transnational Investigation of the Socioeconomic Impacts of Protected Areas” (abbreviated to PARSEC). Hypothesizing that the relationship between PAs and local communities varies by ecoregion and regional economics, we highlight some of the geospatial approaches used to socio-ecologically characterize PAs in the context of local communities in order to statistically explore these important relationships. The PARSEC project is funded by the Belmont Forum, Collaborative Research Action on Science-Driven e-Infrastructures Innovation.
description: We want to acknowledge the entire PARSEC team, especifically David Mouilot for leading the synthesis science effort, and Rodolphe Devillers and Vincent Delbar for their geospatial work on Protected Area selection criteria.
description: {"references": ["Jean, N., Burke, M., Xie, M., Davis, W.M., Lobell, D.B., Ermon, S., 2016. Combining satellite imagery and machine learning to predict poverty. Science 353, 790\u2013794. https://doi.org/10.1126/science.aaf7894", "Omernik, J.M. and G.E. Griffith. 2014. Ecoregions of the conterminous United States: evolution of a hierarchical spatial framework. Environmental Management 54(6):1249-1266.", "Schiavina, Marcello; Moreno-Monroy, Ana; Maffenini, Luca; Veneri, Paolo. 2019 GHS-FUA R2019A - GHS functional urban areas, derived from GHS-UCDB R2019A (2015). European Commission, Joint Research Centre (JRC) [Dataset] doi:10.2905/347F0337-F2DA-4592-87B3- E25975EC2C95 PID: http://data.europa.eu/89h/347f0337-f2da-4592-87b3-e25975ec2c95", "Specht A., Machicao J., Stall S., Vellenich D., Corr\u00eaa P. and the PARSEC consortium. 2020. Research Across Disciplinary Boundaries: Data Challenges and Solutions in the Environmental and Eco-social Sciences. In, International Symposium \"Global Collaboration on Data beyond Disciplines\", 23-25 September 2020. https://ds.rois.ac.jp/wp-content/uploads/2020/10/Abstract-booklet_DSWS-2020.pdf", "WDPA. 2019. World database on protected areas. URL http://www.unep-wcmc.org/wdpa/. Accessed 5 Oct 2019."]}