@inproceedings{sany-etal-2026-similar,
    title = "Similar Region Search using {LLM}s on Spatial Feature Space",
    author = "Sany, Al-Amin  and
      Islam, Mohaiminul  and
      Hashem, Tanzima  and
      Islam, Md. Ashraful  and
      Ali, Mohammed Eunus",
    editor = "Demberg, Vera  and
      Inui, Kentaro  and
      Marquez, Llu{\'i}s",
    booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {EACL} 2026",
    month = mar,
    year = "2026",
    address = "Rabat, Morocco",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-eacl/2026.findings-eacl.201/",
    pages = "3885--3898",
    ISBN = "979-8-89176-386-9",
    abstract = "Understanding regional similarities is crucial for applications such as urban planning, tourism recommendations, business expansion, and disease prevention. While spatial data, including POI distributions, check-in activity, and building footprints, offer valuable insights, existing similarity methods{---}based on distance metrics, embeddings, or deep metric learning{---}fail to capture the contextual richness and adapt to heterogeneous spatial data. To overcome these limitations, we introduce a novel similar region search framework that ranks candidate regions based on their similarity to a query region using large language models. To further enhance performance, we fine-tune the model through self-supervised learning by introducing controlled noise into spatial data. This generates similar and dissimilar samples without relying on extensive labeled data. By transforming spatial data into natural language descriptions, our method seamlessly integrates heterogeneous datasets without requiring structural modifications, ensuring scalability across diverse urban contexts. Experiments on multiple real-world city datasets, including cross-city evaluation, demonstrate that our framework significantly outperforms state-of-the-art methods in both accuracy and ranking performance."
}