- 规划与建设
- 文章编号：1009-6000(2026)05-0076-08
- 中图分类号：TU982.21/.27 文献标识码：B
- Doi：10.3969/j.issn.1009-6000.2026.05.011
- 项目基金：教育部人文社会科学研究青年基金项目（24YJCZH428）；山东省城市更新学会重点课题（250101）；山东省自然科学青年基金项目（ZR2025QC428）。
- 作者简介：林伟鹏，博士，山东建筑大学建筑城规学院副教授，主要研究方向为城乡统筹与国土空间规划、乡村规划与空间治理；
郑淇文，硕士，山东建筑大学建筑城规学院城乡规划专业研究生，主要研究方向为国土空间规划；
张洋华，通信作者，博士，山东建筑大学建筑城规学院副教授，主要研究方向为城乡功能空间识别与动态监测；
刘元亮，硕士，山东省国土测绘院工程师，主要研究方向为空间数据建模与处理。
- 山东省技术密集型产业空间分布特征及影响因素分析
- Spatial Distribution Characteristics and Influencing Factors of Technology-Intensive Industry: A Case Study of Shandong Province
- 林伟鹏 郑淇文 张洋华 刘元亮
- LIN Weipeng ZHENG Qiwen ZHANG Yanghua LIU Yuanliang
- 摘要：
技术密集型产业是战略性新兴产业实现融合集群发展、区域新质生产力优化布局的重要载体，准确把握其细分行业的空间聚集与影响因子强度分异特征具有现实急迫性。文章基于热点分析、随机森林模型和地理加权回归的整合技术路径，以山东省技术密集型产业为例展开实证研究。结果表明，山东省技术密集型产业空间呈现“东强西弱”总体聚集特征，以及“双核多点”“中高西低”的空间分异格局；经济实力、人才优势、金融支持、高等级开发区是影响技术密集型产业空间聚集的重要因子，但分行业空间分布不仅具有差异化的聚集特征和关键影响因子，而且因子影响强度也存在明显的空间异质性。 - 关键词：
技术密集型产业；随机森林；地理加权回归；空间分布；影响因素 - Abstract: Technology-intensive industry is an important carrier for strategic emerging industries to achieve integrated cluster development and optimize the distribution of regional new quality productivity. It is urgent to accurately grasp the spatial clustering and strength differentiation characteristics of influencing factors in its segmented industries. Based on the integrated method of hot spot analysis, random forest model and geographical weighted regression, the empirical study is carried out with technology-intensive industries in Shandong province as an example. The results indicate that the technology-intensive industrial space in Shandong province presents the general clustering characteristics of “strong in the east and weak in the west”, and the spatial differentiation pattern of “dual-core multi-point” and “medium-high and west-low”. Economic strength, talent advantage, financial support and high-grade development zones are important factors affecting the spatial clustering of technology-intensive industries. However, the spatial distribution of different industries not only has differentiated spatial clustering pattern and key influencing factors, but also has different influencing strength of factors.
- Key words: technology-intensive industry; random forest; geographical weighted regression; spatial distribution; influencing factors