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Enhancing the interpretability of port economic modeling via implicit spatial relationship discovery

文献类型：期刊论文

作者Cai, Xiaoli1; Wu, Sheng1; Wang, Peixiao2,3; Zhang, Hengcai2,3; Cheng, Shifen2,3; Lu, Feng2,3
刊名INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION
出版日期2026-02-01
卷号146页码:105161
关键词Port economy PortCity2Vec Spatial XGBoost GeoShapley AIS data
ISSN号1569-8432
DOI10.1016/j.jag.2026.105161
产权排序2
文献子类Article
英文摘要Modeling the driving mechanisms of economic activities among port cities helps reveal their interactions and spatial spillover effects, which is crucial for promoting coordinated regional economic development. Current mainstream models of these driving mechanisms are mostly based on machine learning integrated with the SHAP method, but often neglect spatial dependencies between samples-especially the implicit spatial relationships underlying port city economic activities. In recent years, AIS data has become an important tool for uncovering these implicit spatial relationships. Therefore, we propose the PortCity2Vec framework, based on AIS data and embedding representation learning, to explicitly capture implicit spatial relationships among port cities. Furthermore, we develop a spatial XGBoost model integrated with GeoShapley to incorporate these implicit spatial relationships, thereby revealing the driving mechanisms behind socioeconomic indicators and quantifying the core roles of spatial relationships and geographic features in the port economy. The results show that: (1) Implicit economic interactions among port cities extend beyond physical adjacency, indicating that port economy is influenced not only by physical proximity but also by connections within an implicit spatial structure; (2) Introducing socioeconomic indicators and geographic features of nearby port cities within the implicit spatial structure improves model accuracy, increasing R2 from 0.6554 to 0.8541; (3) The port economy correlates positively with cargo turnover and grain crop output, but negatively with forestry and meat output. These findings highlight the key roles of economic and transport intensity and reveal resource allocation gaps. (4) Embeddings of implicit spatial relationships and geographic features effectively capture regional potential economic connections and marginal contributions that traditional models struggle to identify, thereby enhancing both the performance and interpretability of the model.
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WOS研究方向Physical Geography ; Remote Sensing
语种英语
WOS记录号WOS:001689369600001
出版者ELSEVIER
源URL[http://ir.igsnrr.ac.cn/handle/311030/221008]  
专题资源与环境信息系统国家重点实验室_外文论文
通讯作者Wu, Sheng; Wang, Peixiao
作者单位1.Fuzhou Univ, Acad Digital China Fujian, Fuzhou 350002, Peoples R China;
2.Univ Chinese Acad Sci, Coll Resources & Environm, Beijing 100049, Peoples R China
3.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, State Key Lab Geog Informat Sci & Technol, Beijing 100101, Peoples R China;
推荐引用方式
GB/T 7714		 Cai, Xiaoli,Wu, Sheng,Wang, Peixiao,et al. Enhancing the interpretability of port economic modeling via implicit spatial relationship discovery[J]. INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION,2026,146:105161.
APA Cai, Xiaoli,Wu, Sheng,Wang, Peixiao,Zhang, Hengcai,Cheng, Shifen,&Lu, Feng.(2026).Enhancing the interpretability of port economic modeling via implicit spatial relationship discovery.INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION,146,105161.
MLA Cai, Xiaoli,et al."Enhancing the interpretability of port economic modeling via implicit spatial relationship discovery".INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION 146(2026):105161.

入库方式： OAI收割

来源：地理科学与资源研究所

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