利用階層式分群法點熱圖以瞭解工業污染地區的死亡風險因子

The Use of Heat Map with Hierarchical Clustering in Mortality Risk to Identify 'Hot-Spots' Risk Factors in Polluted Industrial Areas

有鑒於中部沿海工業區自西元1976年起，各種石化工廠、煉油廠等大型重工業陸續進駐，將工業發展附加的空氣污染、水污染及土壤污染等環境問題帶入此地區。部份地區性或特殊事件的健康研究結果也顯示，在工廠進駐的沿海鄉鎮之癌症、呼吸道疾病、皮膚疾病等的發生率明顯高於一般商業區或都市地區，嚴重影響到民眾的生命安全。研究將利用地理群聚分析及集群點熱圖(Cluster Heat Map)的階層式分層法，並使用免費R語言，發展一套及時產生疾病熱點分析功能的風險呈現工具，藉行與列的顏色深淺，分別呈現各鄉鎮的疾病死亡程度，並找出主要影響居民健康的時間變遷。本研究率先將此圖形表示法應用在醫學資訊上，可快速且一目了然表示疾病的空間、時空資料，找出地區或時間上嚴重的疾病群聚狀態，透過疾病群聚工具之開發，探討長期重大疾病之集中地區，藉以減少污染暴露，增近居民健康。

Changhua and Yunlin are two major agricultural counties in Taiwan, where heavy environmental pollution occurred due to rapid industrialization in the 1970's. As a result, health of residents in certain areas was severely affected. To facilitate health risk assessment in these areas, a tool was developed to identify pollution 'hot-spot' areas using a spatial autocorrelation model and heat map with hierarchical clustering in mortality risk. We can then examine the important health risk factors these particular areas. The system was developed with a freeware of R script that displayed the results of a cluster analysis by permuting the rows and the columns of a matrix of place similar values near each other. This method was used to survey distribution of mortality by year and identify the possible trend of certain disease. The study used cluster heap map to display the relationship between spatial and temporal information of mortality data visually and rapidly. The disparity of health conditions can be greatly through the identification and mitigation of priority risk factors in environment.