Abstract
Spatial analysis is the core of geographic information system (GIS), of which, spatial overlay of vector data is a major job. Computational intensity of the spatial overlay has a direct effect on the overall performance of the GIS. High precision modeling for the computational intensity and analysis of the vector data overlay has been a challenging task. Thus, the paper proposes a novel approach, which utilizes self-learning and self-training features of optimized random forest algorithm to the vector data overlay analysis. Simulation experiments show that the proposed model is superior to non-optimized random forest algorithm and support vector machine model with higher prediction precision and is also capable of eliminate redundant computational intensity features.
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Thanks to the support by the National Natural Science Foundation of China (No. 41271387)
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Wang, Q., Cao, H., Guo, YH. (2017). Computational Intensity Prediction Model of Vector Data Overlay with Random Forest Method. In: Zou, B., Li, M., Wang, H., Song, X., Xie, W., Lu, Z. (eds) Data Science. ICPCSEE 2017. Communications in Computer and Information Science, vol 727. Springer, Singapore. https://doi.org/10.1007/978-981-10-6385-5_49
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DOI: https://doi.org/10.1007/978-981-10-6385-5_49
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