Abstract
Spatial ordered-pair pattern mining can be used to discover the potential spatial association between industrial emitted outdoor air pollutants and cancer cases. However, due to the significant correlation between the influence of pollution sources on cancer and the distance between them, and the influence of pollution sources by weather, emission concentration, and other factors is different, there are still some challenges in spatial ordered-pair pattern mining. In this paper, we design an online system MIPC-SHOPs for mining the influence of industrial pollution on cancer based on spatial high-influence ordered-pair patterns. Unlike previous works, first, a new influence measure based on Gaussian kernel density estimation is proposed in MIPC-SHOPs, which solves the problem that the influence of pollution sources on cancer cases decays with distance. Second, to restore the diffusion influence of pollution sources in the real world as much as possible, the urban wind direction, wind speed, and pollution emission concentration were considered to set a new spatial neighbor relationship measurement criterion. Considering the different carcinogenic levels of the pollution sources, a weighted method is proposed to calculate the influence of pollutant on cancer. In particular, the user can obtain the mining results through a simple interaction with the system.
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References
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Acknowledgements
This work was supported by the National Natural Science Foundation of China (62276227, 62266050, 62306266), and the Yunnan Fundamental Research Projects (202201AS070015, 202401AT070450).
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Zhang, L., Wang, L., Yang, P., Zhou, L. (2024). MIPC-SHOPs: An Online System for Mining the Influence of Industrial Pollution on Cancer Based on the Spatial High-Influence Ordered-Pair Patterns. In: Zhang, W., Tung, A., Zheng, Z., Yang, Z., Wang, X., Guo, H. (eds) Web and Big Data. APWeb-WAIM 2024. Lecture Notes in Computer Science, vol 14965. Springer, Singapore. https://doi.org/10.1007/978-981-97-7244-5_28
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DOI: https://doi.org/10.1007/978-981-97-7244-5_28
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