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Crowd-sourcing relative preferences for ecosystem services in the St. Louis River AOC
Citation:
Angradi, T. AND J. Launspach. Crowd-sourcing relative preferences for ecosystem services in the St. Louis River AOC. St. Louis River Summit, Superios, WI, March 14 - 15, 2017.
Impact/Purpose:
This poster describes an effort to use free information posted on social media platforms to learn relative valuation of ecosystem servies in Great Lakes Areas of Concern (AOCs). This is the first attempt to use content analysis of volunteered geographic information to inform ecosystem services based decision-making for AOCS.
Description:
Analysis of ecosystem service tradeoffs among project scenarios is more reliable when valuation data are available. Empirical valuation data are expensive and difficult to collect. As a possible alternative or supplement to empirical data, we downloaded and classified images from social media sites (SMS) Panoramio (n= 639), Instagram (n=2086), and Flickr (n=6644) for the AOC and 100 m buffer. We classified each image from the perspective of the beneficiary (photographer) according to US EPA’s Final Ecosystem Goods and Services (FEGS) classification system. After removing images not in the AOC or with bad links, 58 (10%), 361 (24%), and 1035 (16%) of the images from Panoramio, Instagram, and Flickr, respectively, depicted an ecosystem service. The most frequently occurring non ecosystem services depicted were bridges, ships, indoor scenes, and people. Across SMS with repeat images removed, AOC services were percentage ranked as follows: recreational scene viewers, 28%; boaters, 24%, flora and fauna viewers, 20%; other recreation experiencers (e.g., dog walkers, beach goers, bikers, trail and greenspace users), 15%; angling, 7%; learners, 1%, inspirational/sacred experiencers, 1%. Across SMS, recreational services were ranked as follows: scene viewing, 28%; boating, 24%, birding, 16%; fauna and flora viewing, 8%; trail and greenspace use, 7%; angling 7%; dog walking, 3%; swimming, beach and ice use, 3%; biking, 1%; excursion rail, 1%. Potential biases include SMS user profile, user gender, user age, and user type (local or visitor).