Figure 2 process flowchart of the new Zhao et al. paper in Marine Policy
Another nice lab output this week in Marine Policy led by Qiqi Zhao, a China Scholarship Council visiting PhD student in my lab last year, including a bunch of other lab-affiliated students as co-authors: Modelling cultural ecosystem services in agricultural dykelands and tidal wetlands to inform coastal infrastructure decisions: a social media data approach. It is a bit of a companion piece to the Chen et al (2020) piece in Ocean and Coastal Management, as it uses the same Instagram dataset collected for every dykeland area in Nova Scotia back in 2018, but in a very different way. Chen et al. took a very qualitative ‘small data’ approach to the dataset, analyzing the photographs (and accounts) only of posts that included the words dyke*/dike*/wetland/marsh in the captions. Zhao et al. used a ‘big data’ text mining approach, extracting and associating bi-grams (two-word strings) from geolocated post captions to particular cultural ecosystem services (CES), modelling those CES using SolVES and comparing (as with Chen et al.) dykeland and wetland services. Whereas Chen et al. only found direct mentions of freshwater marshes (specifically Miner’s Marsh), in Zhao et al. we leveraged the coordinates to locate those geolocated to tidal wetland sites. This will help us better understand the tradeoffs associated with climate change-driven adaptations of the dykeland system in the Bay of Fundy, the focus of NSERC ResNet Landscape 1.
