Using Big Data from Internet to Improve Land Use Transition Effects on Eco-Environment

Abstract

The intricate relationship between land use and ecological changes is a critical area of eco-environmental research. However, there is a notable lack of quantitative, analytical studies that explore this relationship in depth. This study aims to bridge this gap by applying a contrastive learning approach to a practical land use application, utilizing big data. We analyzed 2,197 publications from the Web of Science Core Collection spanning from 1998 to 2022. Our methodology correlates textual and visual data, enhancing the categorization and thematic analysis of existing literature. The contrastive learning technique uncovers nuanced relationships between textual descriptions and visual data, providing a comprehensive view of research trends. Key findings include an increasing volume of publications, indicating growing interest and potential in this field; predominant focus areas such as the relationship between climate change and land use, dynamic land use modelling, urbanization impacts, land use management, ecosystem services, and biodiversity. Emerging research hotspots identified through contrastive learning include the interactions between urban thermal environments and land use changes, land use effects on biological communities and soil organic matter, and the relationship between ecological conditions and land use in vulnerable regions. This study not only maps the current research landscape but also employs a contrastive learning approach to predict future research directions, thus enriching the methodological framework for analyzing land use applications with big data.