Article
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Assessing Perceived Landscape Change From Opportunistic Spatio-Temporal Occurrence Data
Version 1
: Received: 5 July 2024 / Approved: 5 July 2024 / Online: 9 July 2024 (06:04:50 CEST)
How to cite: Dunkel, A.; Burghardt, D. Assessing Perceived Landscape Change From Opportunistic Spatio-Temporal Occurrence Data. Preprints 2024, 2024070527. https://doi.org/10.20944/preprints202407.0527.v1 Dunkel, A.; Burghardt, D. Assessing Perceived Landscape Change From Opportunistic Spatio-Temporal Occurrence Data. Preprints 2024, 2024070527. https://doi.org/10.20944/preprints202407.0527.v1
Abstract
The exponential growth of user-contributed data provides a comprehensive basis for assessing collective perceptions of landscape change. A variety of possible public data sources exist, such as geospatial data from social media or Volunteered Geographic Information (VGI). Key challenges with such ‘opportunistic’ data sampling are variability in platform popularity and bias due to changing user groups and contribution rules. In this study, we use five case studies to demonstrate how intra- and inter-dataset comparisons can help to assess the temporality of landscape scenic resources, such as identifying seasonal characteristics for a given area, or testing hypotheses about shifting popularity trends observed in the field. By focusing on the consistency and reproducibility of temporal patterns for selected scenic resources and comparisons across different dimensions of data, we aim to contribute to the development of systematic methods for disentangling the perceived impact of events and trends from other technological and social phenomena included in the data. The proposed techniques may help to draw attention to overlooked or underestimated patterns of landscape change, fill in missing data between periodic surveys, or corroborate and support field observations. Despite limitations, the results already provide a comprehensive basis for developing indicators with a high degree of timeliness for monitoring perceived landscape change over time.
Keywords
spatial-temporal; landscape change; opportunistic data; photo content; perception
Subject
Environmental and Earth Sciences, Environmental Science
Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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