Pontius, R.G., Krithivasan, R., Sauls, L. orcid.org/0000-0001-8868-7465 et al. (2 more authors) (2017) Methods to summarize change among land categories across time intervals. Journal of Land Use Science, 12 (4). pp. 218-230. ISSN 1747-423X
Abstract
Time-series maps have become more detailed in terms of numbers of categories and time points. Our paper proposes methods for raster datasets where detailed analysis of all categorical transitions would be initially overwhelming. We create two measurements: Incidents and States. The former is the number of times a pixel’s category changes across time intervals; the latter is the number of categories that a pixel represents across time points. The combinations of Incidents and States summarize change trajectories. We also describe categorical transitions in terms of annual flow matrices, which quantify the additional information generated by intermediate time points within the temporal extent. Our approach summarizes change at the pixel and landscape levels in ways that communicate where and how categories transition over time. These methods are useful to detect hotspots of change and to consider whether the apparent changes are real or due to map error.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2017 Informa UK Limited. This is an author-produced version of a paper subsequently published in Journal of Land Use Science. Uploaded in accordance with the publisher's self-archiving policy. |
Keywords: | Category; GIS; land change; flow matrix; time; transition |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Social Sciences (Sheffield) > Department of Geography (Sheffield) |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 08 Sep 2020 07:11 |
Last Modified: | 08 Sep 2020 09:24 |
Status: | Published |
Publisher: | Informa UK Limited |
Refereed: | Yes |
Identification Number: | 10.1080/1747423x.2017.1338768 |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:165209 |