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  "Title": "Mitigating Spatial Bias Through Geographical Complexity",
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  "Description": "The geographical complexity of individual variables can be\ncharacterized by the differences in local attribute variables,\nwhile the common geographical complexity of multiple variables\ncan be represented by fluctuations in the similarity of vectors\ncomposed of multiple variables. In spatial regression tasks,\nthe goodness of fit can be improved by incorporating a\ngeographical complexity representation vector during modeling,\nusing a geographical complexity-weighted spatial weight matrix,\nor employing local geographical complexity kernel density.\nSimilarly, in spatial sampling tasks, samples can be selected\nmore effectively by using a method that weights based on\ngeographical complexity. By optimizing performance in spatial\nregression and spatial sampling tasks, the spatial bias of the\nmodel can be effectively reduced.",
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      "commit": "89af59c8213a4e9205f78a65718776266ebc5fca",
      "fileid": "06d1956ef6b6668b0229e577fd638056bba30f391b98a1f7ad390ab98a1522f3",
      "status": "success",
      "check": "OK",
      "buildurl": "https://github.com/r-universe/ausgis/actions/runs/26446146443"
    }
  ]
}