Predicting the environment from social media: a collective classification approach

Jeawak, Shelan S., Jones, Christopher B. ORCID: https://orcid.org/0000-0001-6847-7575 and Schockaert, Steven ORCID: https://orcid.org/0000-0002-9256-2881 2020. Predicting the environment from social media: a collective classification approach. Computers, Environment and Urban Systems 82 10.1016/j.compenvurbsys.2020.101487

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Abstract

We propose a method which uses Flickr tags to predict a wide variety of environmental features, such as climate data, land cover categories, species occurrence, and human assessments of scenicness. The role of Flickr tags in our method is two-fold. First, we show that Flickr tags capture information which is highly complementary to what is found in traditional structured environmental datasets. By combining Flickr tags with traditional datasets, we can thus make more accurate predictions than is possible using either Flickr tags or traditional datasets alone. Second, we propose a collective prediction model which crucially relies on Flickr tags to define a neighbourhood structure. The use of a collective prediction formulation is motivated by the fact that most environmental features are strongly spatially autocorrelated. While this suggests that geographic distance should play a key role in determining neighbourhoods, we show that considerable gains can be made by additionally taking Flickr tags and traditional data into consideration.

Item Type:	Article
Date Type:	Publication
Status:	Published
Schools:	Computer Science & Informatics Advanced Research Computing @ Cardiff (ARCCA)
Publisher:	Elsevier
ISSN:	0198-9715
Date of First Compliant Deposit:	20 April 2020
Date of Acceptance:	29 March 2020
Last Modified:	30 Nov 2024 17:15
URI:	https://orca.cardiff.ac.uk/id/eprint/131089

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