Discovering Spatial Co-location Patterns with Dominant Influencing Features in Anomalous Regions

Abstract

As one of the important exogenous factors that induce malignant tumors, environmental pollution poses a major threat to human health. In recent years, more and more studies have begun to use data mining techniques to explore the relationships among them. However, these studies tend to explore universally applicable pattern in the entire space, which will take a high time and space cost, and the results are blind. Therefore, this paper first divides the spatial data set, then combined with the attenuation effect of pollution influence with increasing distance, we proposed the concept of high-impact anomalous spatial co-location region mining. In these regions, industrial pollution sources and malignant tumor patients have a higher co-location degree. In order to better guide the actual work, the pollution factors that have a decisive influence on the occurrence of malignant tumors in the pattern is explored. Finally, a highly targeted new method to explore the dominant influencing factors when multiple pollution sources act on a certain tumor disease at the same time is proposed. And extensive experiments have been conducted on real and synthetic data sets. The results show that our method greatly improves the efficiency of mining while obtaining effective conclusions.