以我國現行之防救災體系而言，當面對緊急事件應變時，各部會依其職責提供相關之災情資訊，應變中心則肩負災情資訊綜整與決策之任務。多來源之災情資訊不僅止於來自於政府的結構化資訊，隨著社群媒體與大眾的生活緊密度上升，社群也成為提供資訊之管道來源，然而其資料特性影響後續的標準化過程。此外，以聚集性的觀點分析災情通報資料之時空因子，具有較高時空聚集性之區域可被視為可能之災害區域，並藉由各項指標判斷聚類之時空表現，以期在應變時能作為決策的輔助資訊。
EDXL-SitRep由OASIS提出，適用於緊急應變中資訊交換的一套準則架構，而本研究以該標準為基礎，檢視其元素是否能滿足5W1H以概括緊急應變資訊，並基於滿足5W1H框架 之EDXL-SitRep標準探討不同來源之資訊是否能夠提供足夠的資訊，來源包含來自於政府的感測網資訊與來自於大眾的社群資料。結果顯示EDXL能滿足災情資訊之標準化，尤其來自政府的資料因其結構化之內容與政府之規範，更容易提供完整且持續更新的SitRep；社群資料中又可概略區分出直接來自大眾的資料與透過志工、協力單位提供的資料，而前者代表性的資料為來自臉書的資料，統計資料顯示約三成的臉書資料與約五成的協力單位資料，經過對比驗證後是可直接作為使用，意味著未來仍需透過後端資料庫的建立、教育推廣、志工訓練等方式以提高社群資料的使用性。
經驗證後的社群資料，可在政府現有災情監控之機制外提供更貼近真實的災情狀態。聚類分析為常見之資料分析方法，而災情資訊同時具備空間與時間之考量，因此藉由時間聚集性與時空聚集性的觀點來解析社群資料。結果顯示，單考慮時間因素的聚類在空間指標上表現並不理想，能反映出整體資料的時間趨勢以及擴展之方向，但在空間表現上沒有良好的表現。不同聚類方式能凸顯之重點不同，在同時考慮時間與空間因素下，DBSCAN透過參數設定，找到相對高密度的聚類，反映出災害通報頻傳的地點，也能顯示出熱區之類的訊息。階層式聚類則適用於找出主要聚類，但資料筆較少時，多階層的分類容易形成規模很小的聚類。k-means則能找出整體資料的趨勢。

Taiwan is located in an area which is usually affected by the natural disasters. After the disasters, each agencies would start to collect the disaster information which is related to their duty. And the CEOC would play a role to integrate all kinds of resources then take corresponding response. On the other hand, people like to share everything through the social media. So it also become one way to collect the disaster information. So the problem would be how to deal with the heterogeneous data and improve the usability. EDXL is a set of standard which is used to promote the data sharing between agencies. So we use the EDXL-SitRep to standardize the data from the government and social media. The result shows that the EDXL-SitRep can help to regulate the form of disaster information. However the social media needs the certification before the standardization. Because of the characteristics of data uncertainty and incompleteness. As for improving the usability of social media, clustering analysis is a common method to analysis social media. So we use the temporal clustering and spatio-temporal clustering to test the social media data. The result shows that DBSCAN is the more suitable way to make the spatio-temporal clustering. And consider the spatial and temporal factors simultaneously would be a better way to analysis the disaster
situation reports.

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