{
  "id": 14448,
  "url": "https://arxiv.org/abs/2607.25124v1",
  "title": "UrbanTrace: LLM-Assisted Discovery and Semantics-Aware Integration of Spatial Data",
  "summary": "Urban decision-making requires integrating heterogeneous spatial data. While current GIS tools handle geometric computation efficiently, they lack the semantic reasoning to guide complex workflows. Analysts manually manage data discovery, spatial boundaries, and measurement semantics, risking aggregation errors. We present UrbanTrace, a visual analytics system that transforms manual spatial data-wrangling into a transparent, node-based collaborative workflow with context-aware AI agents. Using a",
  "authors": "Sonia Castelo, Eden Wu, Joao Rulff, Harish Doraiswamy, Juliana Freire, Claudio Silva",
  "category": "research",
  "topics": "agents-autonomy,transparency",
  "orgs": null,
  "regions": null,
  "published_at": "2026-07-27T22:35:18.000Z",
  "fetched_at": "2026-07-29T05:10:12.205Z",
  "source_slug": "x-arxiv-cs-hc",
  "source_name": "arXiv cs.HC",
  "source_homepage": "https://arxiv.org/list/cs.HC/recent",
  "ethics_ai_record_url": "https://ethics.ai/record/14448",
  "original_url": "https://arxiv.org/abs/2607.25124v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}