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    <datestamp>2026-09-10T14:06:15Z</datestamp>
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  <dc:date>2026-09-10</dc:date>
  <dc:title>Local transfer-risk routing for geographically transferable spatial prediction: an application to farmland rents in China</dc:title>
  <dc:identifier>doi:10.57760/sciencedb.0122j</dc:identifier>
  <dc:language>en</dc:language>
  <dc:description>This folder contains the data and code needed to examine the results reported in the manuscript.The material to upload to the review repository consists of folders 01 to 11. Folders 08 to 11 contain the analysis-ready table, derived climate summaries, out-of-fold predictions, simulation outputs, statistical results, and analysis scripts. These files are sufficient to check the sample counts, model comparisons, uncertainty calculations, simulation results, and econometric estimates without downloading the full raster archive.Large files kept outside this package1. China Crop Dataset GeoTIFFs, 2021 to 2024: about 6.6 GB. The files are available from the cited Science Data Bank record.2. China-clipped nighttime-light GeoTIFFs, 2021 to 2024: about 2.3 GB. The analysis uses parcel-level values extracted from these rasters; those values are included in 08_analysis_ready_data/analysis_dataset.csv.gz.3. Daily ERA5-Land extraction files, 1991 to 2025: about 0.2 GB. The grid-year summaries used by the analysis are included in folder 08.</dc:description>
  <dc:subject>geospatial artificial intelligence; model transferability; mixture of experts; spatial cross-validation; spatial nonstationarity</dc:subject>
  <dc:creator>Wu Yujie</dc:creator>
  <dc:creator>Lin Jiani</dc:creator>
  <dc:creator>Xiao Tidong</dc:creator>
  <dc:creator>Wu Mengze</dc:creator>
  <dc:rights>PUBLIC</dc:rights>
  <dc:rights>https://creativecommons.org/licenses/by-nc/4.0/</dc:rights>
  <dc:type>dataset</dc:type>
  <dc:publisher>Science Data Bank</dc:publisher>
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