UrbanTrace: LLM-Assisted Discovery and Semantics-Aware Integration of Spatial Data
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
Record details
Published: 27 July 2026
Source: arXiv cs.HC
Category: Research
Topics: Agents & autonomy · Transparency
Retrieved: 29 July 2026
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ethics.ai (27 July 2026), “UrbanTrace: LLM-Assisted Discovery and Semantics-Aware Integration of Spatial Data,” evidence record 14448, https://ethics.ai/record/14448 (originally published by arXiv cs.HC).
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