City-scale AI Development and Regional AI Divide Assessment Datasets for China’s 283 Cities from 2003 to 2023

Artificial intelligence (AI) has become a key driver of urban development in the digital age. Meanwhile, the uneven development of AI among cities is creating a new “digital divide”. As one of the pioneering countries in AI development, China has seized the opportunities presented by urban AI development while also encountering the challenge of a regional AI divide. This provides a unique context for the trajectory and spatial layout of regional AI development. However, existing city-scale AI development index assessments generally use a single indicator and have limitations such as insufficient indicator relevance, short coverage period, and lack of regional AI divide assessment. This study constructed a Chinese city-scale AI development index assessment dataset containing 283 cities and a province-scale regional AI divide dataset for 26 provinces from 2003 to 2023. hese datasets encompassed a composite indicator system and evaluation method consisting of three metrics: the number of AI enterprises, the number of patent applications, and the industrial robot installation density, and utilized the Dagum-Gini coefficient method to assess the regional AI divide index. These datasets can comprehensively assess the situation and development trends of AI development in Chinese cities and the regional AI divide, providing data support for academic research related to regional AI development and government policy-making.

人工智能（Artificial Intelligence，以下简称AI）已成为数字时代城市发展的核心驱动力。与此同时，城市间AI发展的不均衡性正催生新的“数字鸿沟”。作为人工智能发展的先驱国家之一，中国既把握住了城市AI发展带来的历史机遇，也面临着区域AI发展失衡的挑战，这为研究区域AI发展的路径与空间布局提供了独特的研究语境。然而，现有城市尺度的AI发展指数评估普遍采用单一指标，存在指标关联性不足、覆盖周期较短、缺乏区域AI发展失衡评估等诸多局限。本研究构建了覆盖2003年至2023年、包含283个中国城市的城市尺度AI发展指数评估数据集，以及面向26个省份的省级区域AI发展失衡数据集。上述数据集采用由AI企业数量、专利申请量、工业机器人安装密度三项指标构成的复合指标体系与评估方法，并运用达古姆-基尼系数法（Dagum-Gini coefficient）测算区域AI发展失衡指数。上述数据集可全面评估中国城市AI发展现状与发展趋势，以及区域AI发展失衡状况，为区域AI发展相关学术研究与政府决策提供数据支撑。