A LLM driven dataset on the spatiotemporal distributions of street and neighborhood crime in China

Crime is a significant social, economic, and legal issue. This paper presents an open-access spatiotemporal repository of street and neighborhood crime data, comprising approximately one million records of crimes in China, with specific geographic coordinates (latitude and longitude) and timestamps for each incident. The dataset is based on publicly available law court judgment documents. Artificial intelligence (AI) technologies are employed to extract crime events at the neighborhood or even building level from vast amounts of unstructured judicial text. This dataset enables more precise spatial analysis of crime incidents, offering valuable insights across interdisciplinary fields such as economics, sociology, and geography. It contributes significantly to the achievement of the United Nations Sustainable Development Goals (SDGs), particularly in fostering sustainable cities and communities, and plays a crucial role in advancing efforts to reduce all forms of violence and related mortality rates.犯罪是一项重大的社会、经济与法律议题。本文公开了一套面向街道与社区犯罪数据的时空数据库，涵盖中国境内约一百万条犯罪记录，每条记录均附带精确地理坐标（纬度与经度）及事件时间戳。该数据集依托公开可获取的法院裁判文书构建。研究采用人工智能（Artificial Intelligence）技术，从海量非结构化司法文本中提取社区乃至楼宇层级的犯罪事件信息。该数据集可支撑犯罪事件的高精度空间分析，为经济学、社会学、地理学等跨学科领域提供极具价值的研究洞见。本数据集对联合国可持续发展目标（United Nations Sustainable Development Goals，SDGs）的达成具有重要推动作用，尤其在助力可持续城市与社区建设方面成效显著，同时在推进减少各类暴力行为及相关死亡率的工作中扮演关键角色。