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導入民眾外包整合低品質災害通報資料

USING CROWDSOURCING TO PROCESS LOW-QUALITY DISASTER INFORMATION

Abstracts


隨著行動裝置普及,大量來自民眾的災害通報易有錯誤與重複等問題,造成通報品質低落,本研究期望藉由民眾力量,快速精確地整合通報。本研究透過社群網站募集民眾,先由關鍵字篩選器,剔除無意義的通報資料,並使用地理編碼定位及GIS 空間資訊分析,結構化原始通報資料,設計網頁操作介面,引導民眾完成重複事件過濾之任務。本研究於2015 年8 月蘇迪勒颱風及9 月杜鵑颱風期間,進行可行性驗證,共募集210 位民眾參與眾包任務,完成5906 組任務,結果顯示民眾透過社群網站的任務引導,能正確地完成判斷,協助過濾及整合通報資料。

Parallel abstracts


With the popularity of mobile devices, the vast amount of disaster responses from the crowd results in the error and duplication of the disaster responses, which is not conducive for the government to relief disaster. This research aims to eliminate the error and duplication of the disaster responses through crowdsourcing. We used community website to recruit crowd and integrated both artificial intelligence (AI) and crowd intelligence (CI). We first used the AI filter with keywords to delete the inaccurate responses and structuralized the accurate responses by geocoding and GIS spatial analysis. Then, in the CI filter, designed the website with the user-friendly user interface to guide the crowd in completing the mission of consolidating duplicated responses and redefining unstructured responses correctly. We verified the feasibility of the process in an actual case during Typhoon Soudeloron August 2015 and Typhoon Dujuan on September 2015 in Taiwan. 210 volunteers from the Internet participated in the crowdsourcing missions and completed the 5906 groups of mission integration. The results showed that crowd are pleased to complete crowdsourcing mission through community website and have the ability to consolidate duplicated disaster responses accurately and quickly with the guidance of the platform.

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