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首页> 外文会议>International Conference on Geoinformatics;Geoinformatics 2012 >Spatial Statistical Modeling of the Pollution Impact of Old Industrial Sites on Colon and Lung Cancer Incidents in New York State,USA
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Spatial Statistical Modeling of the Pollution Impact of Old Industrial Sites on Colon and Lung Cancer Incidents in New York State,USA

机译：美国纽约州旧工业场地对结肠癌和肺癌事件的污染影响的空间统计模型

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This research visualizes the spatial patterns of diagnosed colon and lung cancer mortalities across the New York State. Kernel density analysis was applied to visualize the spatial patterns of old industrial sites across the state. Geographically Weighted Regression (GWR) was applied to model the possible pollution impact of old industrial sites on colon and lung cancer incidents. GWR is a local spatial statistical model analyzing the relations of non-stationary factors across the space. It generates a separate spatially-weighted least square regression for each of the observations across the study area. The results shows that the high density clusters of the old industrial sites occur in the large urban areas of New York City, Rochester, Buffalo, Syracuse, and Albany. GWR modeling indicates that high local R2 coincides with moderate cancer mortality incidents. The overall GWR predictions are satisfactory as the R2 of colon cancer is 0.8848 and that of lung cancer is 0.8755. However, AICc values are large for both types of cancer predictions.
机译：这项研究可视化了纽约州诊断出的结肠癌和肺癌死亡率的空间格局。内核密度分析用于可视化全州旧工业用地的空间格局。地理加权回归（GWR）用于模拟旧工业场所对结肠癌和肺癌事件的可能污染影响。 GWR是一个局部空间统计模型，用于分析整个空间中非平稳因素之间的关系。它为研究区域中的每个观测值生成了单独的空间加权最小二乘回归。结果表明，旧工业区的高密度集群发生在纽约，罗切斯特，布法罗，锡拉库扎和奥尔巴尼等大城市地区。 GWR模型表明高局部R2与中等癌症死亡率事件相吻合。总体GWR预测令人满意，因为结肠癌的R2为0.8848，肺癌的R2为0.8755。但是，对于两种类型的癌症预测，AICc值都很大。

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