@article {Auffret109504, author = {Alistair G. Auffret and Adam Kimberley and Jan Plue and Helle Sk{\r a}nes and Simon Jakobsson and Emelie Wald{\'e}n and Marika Wennbom and Heather Wood and James M. Bullock and Sara A. O. Cousins and Mira Gartz and Danny A. P. Hooftman and Louise Tr{\"a}nk}, title = {HistMapR: Rapid digitization of historical land-use maps in R}, elocation-id = {109504}, year = {2017}, doi = {10.1101/109504}, publisher = {Cold Spring Harbor Laboratory}, abstract = {1. Habitat destruction and degradation represent serious threats to biodiversity, and quantification of land-use change over time is important for understanding the consequences of these changes to organisms and ecosystem service provision.2. Comparing land use between maps from different time periods allows estimation of the magnitude of habitat change in an area. However, digitizing historical maps manually is time-consuming and analyses of change are usually carried out at small spatial extents or at low resolutions.3. We developed a method to semi-automatically digitize historical land-use maps using the R environment. We created a number of functions that use the existing raster package to classify land use according to a map{\textquoteright}s colours, as defined by the RGB channels of the raster image. The method was tested on three different types of historical land-use map and results were compared to manual digitisations.4. Our method is fast, and agreement with manually-digitised maps of around 80-92\% meets common targets for image classification. We hope that the ability to quickly classify large areas of historical land-use will promote the inclusion of land-use change into analyses of biodiversity, species distributions and ecosystem services.}, URL = {https://www.biorxiv.org/content/early/2017/02/17/109504}, eprint = {https://www.biorxiv.org/content/early/2017/02/17/109504.full.pdf}, journal = {bioRxiv} }