A large-scale LLM-generated dataset for exploring social interactions and urban experiences across 21 global cities

We present a dataset designed to evaluate the capacity of Large Language Models (LLMs) to generate context-aware simulations of urban experiences across diverse cultural and socioeconomic settings. The dataset provides a globally distributed and scalable resource for the systematic analysis of daily routines, mobility patterns, and social interactions in major cities. The dataset comprises 1260 simulated urban experience narratives generated by three Large Language Models (LLMs) across 21 major cities in seven global regions. Each narrative captures a structured weekend scenario for one of four predefined actor profiles, representing distinct socioeconomic roles within specific city contexts. A standardized and iteratively refined prompt framework was applied to ensure comparability across cities, actor profiles, and models while preserving controlled generative variability. Actor-level attributes and city-specific contextual information were incorporated to enhance contextual grounding. Additional quality control procedures were implemented to reduce redundancy and maintain temporal and spatial coherence within individual simulations. The resulting corpus constitutes a scalable and systematically structured resource for comparative urban analysis. It enables examination of how contemporary LLMs encode and reproduce representations of urban environments across diverse cultural and socioeconomic settings, supporting the assessment of narrative coherence, cross-model variability, and potential representational bias. As such, it establishes a structured basis for systematic comparison and methodological evaluation of LLM-generated urban narratives.