arbiter
JournalsPricingAccount

Geographical-XGBoost: a new ensemble model for spatially local regression based on gradient-boosted trees

George Grekousis

Journal of Geographical Systems2025https://doi.org/10.1007/s10109-025-00465-4article
AJG 1
Weight
0.73

What the paper says

No abstract available.

46 citations

Open paper page →

Cite this paper

https://doi.org/https://doi.org/10.1007/s10109-025-00465-4

Or copy a formatted citation

@article{george2025,
  title        = {{Geographical-XGBoost: a new ensemble model for spatially local regression based on gradient-boosted trees}},
  author       = {George Grekousis},
  journal      = {Journal of Geographical Systems},
  year         = {2025},
  doi          = {https://doi.org/https://doi.org/10.1007/s10109-025-00465-4},
}

Paste directly into BibTeX, Zotero, or your reference manager.

Flag this paper

Geographical-XGBoost: a new ensemble model for spatially local regression based on gradient-boosted trees

Flags are reviewed by the Arbiter methodology team within 5 business days.

Evidence weight

0.73

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.88 × 0.4 = 0.35
M · momentum1.00 × 0.15 = 0.15
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.