XAI in geographic analysis of innovation: Evaluating proximity factors in the innovation networks of Chinese technology companies through web-based data

IF 4 2区 地球科学 Q1 GEOGRAPHY Applied Geography Pub Date : 2024-08-10 DOI:10.1016/j.apgeog.2024.103373
Chenxi Liu , Zhenghong Peng , Lingbo Liu , Hao Wu , Jan Kinne , Meng Cai , Shixuan Li
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Abstract

This research explores the nonlinear interactions among multidimensional proximities, including geographical, cognitive, organizational, institutional, social, and technological aspects, and their impact on innovation within networks of over three million technology firms in China. Utilizing an innovative combination of web-based hyperlink and textual data analysis, supplemented by patent information, we delve into how these proximity dimensions influence corporate innovation capabilities. Our methodology integrates text-based deep learning techniques and employs the XGBoost model along with the SHapley Additive exPlanations (SHAP) algorithm and partial dependence plots to uncover the nuanced effects of proximity on innovation. The findings reveal that while geographical distance often correlates with larger cognitive and organizational proximities, underdeveloped regions exhibit stronger technological, institutional, and social proximities compared to their developed counterparts. The study further identifies social structure and technological differences as pivotal factors impacting collaborative innovation, with both positive and negative effects fluctuating alongside changes in proximity dimensions. Notably, we uncover that geographical proximity has a pronounced boundary effect on innovation, highlighting the critical role of spatial considerations in the digital age of innovation networks. This research contributes to the understanding of urban innovation dynamics and offers valuable insights for policymakers and urban planners aiming to foster innovation ecosystems.

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XAI 在创新地理分析中的应用:通过网络数据评估中国科技企业创新网络中的邻近因素
本研究探讨了多维接近性之间的非线性相互作用,包括地理、认知、组织、制度、社会和技术方面,以及它们对中国 300 多万家科技企业网络内创新的影响。利用基于网络的超链接和文本数据分析的创新组合,并辅以专利信息,我们深入研究了这些接近性维度如何影响企业创新能力。我们的研究方法整合了基于文本的深度学习技术,并采用 XGBoost 模型、SHAP 算法和部分依赖图来揭示邻近性对创新的细微影响。研究结果表明,虽然地理距离往往与较大的认知和组织邻近性相关,但与发达地区相比,欠发达地区在技术、制度和社会方面表现出更强的邻近性。研究进一步发现,社会结构和技术差异是影响合作创新的关键因素,其积极和消极影响随着距离维度的变化而波动。值得注意的是,我们发现地理邻近性对创新具有明显的边界效应,这凸显了空间因素在创新网络数字化时代的关键作用。这项研究有助于人们了解城市创新动态,并为旨在培育创新生态系统的政策制定者和城市规划者提供有价值的见解。
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来源期刊
Applied Geography
Applied Geography GEOGRAPHY-
CiteScore
8.00
自引率
2.00%
发文量
134
期刊介绍: Applied Geography is a journal devoted to the publication of research which utilizes geographic approaches (human, physical, nature-society and GIScience) to resolve human problems that have a spatial dimension. These problems may be related to the assessment, management and allocation of the world physical and/or human resources. The underlying rationale of the journal is that only through a clear understanding of the relevant societal, physical, and coupled natural-humans systems can we resolve such problems. Papers are invited on any theme involving the application of geographical theory and methodology in the resolution of human problems.
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