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Spatial analysis and machine learning: Towards integrated predictive modeling advancements
- 1 The University of Adelaide
- 2 The University of Newcastle
* Author to whom correspondence should be addressed.
Abstract
This paper presents a comprehensive exploration of the integration of spatial analysis with machine learning techniques, aiming to enhance predictive modeling capabilities across various domains. Spatial analysis, a methodological approach for understanding geographic patterns and relationships, when combined with the computational power of machine learning, offers unprecedented opportunities for analyzing complex spatial datasets. Through quantitative analysis and the application of mathematical models, this study demonstrates the effectiveness of this integration in improving the accuracy and efficiency of predictive models. The research encompasses a range of applications, from environmental monitoring to urban planning and public health, showcasing the versatility and potential of combining spatial analysis with machine learning.
Keywords
Spatial Analysis, Machine Learning, Predictive Modeling, Geographic Information Systems (GIS), Environmental Monitoring
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Cite this article
Lu,X.;Li,Z. (2024). Spatial analysis and machine learning: Towards integrated predictive modeling advancements. Theoretical and Natural Science,36,152-157.
Data availability
The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.
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Volume title: Proceedings of the 2nd International Conference on Mathematical Physics and Computational Simulation
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