近年網際網路、行動裝置及巨量資料之發展帶動資訊產業之革命，顛覆了傳統資料蒐集、處理、流通及分享之模式。在Web 2.0所帶動之風潮下，一般使用者擁有更為便捷之工具來建立及分享資訊，產生例如群眾外包(crowdsourcing)等新一代的資訊運作機制。自願性地理資訊Volunteer Geographic Information，VGI）對過去由政府單位擔負資料產製及系統發展之模式帶來新的思維，借助群眾與社群之力量，可達成更為廣泛與更為即時圖資蒐集之成效，並可降低獲取空間資訊之成本。自願性地理資訊近年在物種調查、時空趨勢分析、救災資訊匯集，甚至地圖平台內容之更新上已累積大量之成果。由於資料來自於群眾，資料品質不但無法與傳統之專業資料相提並論，更可能發生品質不一之情形，廣泛蒐集之優勢反而可能成為正確決策之隱憂。有別於在最終分析階段面對各類資料之品質差異問題，本研究擬由強化資訊供應內容之觀點著手，透過標準化之手段，達成提升決策品質之目標。
本研究之主要策略為分析自願性地理資訊所必須具有之描述內容，經由時間、空間及品質之考量，設計為強化描述之標準化架構。此架構之內容須在資料建置時即決定，並內嵌於流通之資料格式中，如此將可在資訊分析時，透過其提供之資訊而具體掌握各類資料之差異，即令自願性地理資訊分由不同資料格式記錄，其成果仍可在決策者更為完整之了解下建立，避免錯誤之決策。本研究整理與分析VGI應用中常使用之流通格式，並規劃強化描述架構嵌入之策略及歸結其使用之限制。由於嵌入資訊之內容有所差異，本研究進一步探討資料剖析及品質評估之策略，並具體分析對於整體應用之影響。
相較於現行普遍採用之自願性地理資訊處理方式，本研究因應資料格式之特性而使流通資料「自動」隱含時空品質資訊，資訊雖然更為完整，但使用者不會感覺額外之工作負擔。在決策時，由於資料帶有考量之時間、空間與品質資訊等描述內容，更易彰顯各類資料之差異，使用者對於決策之品質將可建立更「正確」之評估。本研究之成果可突破自願性地理資訊應用中，資料提供者缺乏專業及決策者沒有品質評估基礎之兩大瓶頸。在此基礎下，未來之所有蒐集及流通之自願性地理資訊均將具有符合其本身特性之描述，可滿足更為多元之應用需求，尤其在巨量資料之分析上，透過此方式而蒐集之資料將成為更有價值之資源彙整機制。

VGI provides clear advantages of scalability, flexibility, efficiency and less spending to serve as an alternative approach for better understanding of the changing world. Despite of the advantages VGI can bring, data quality remains a difficult issue because VGI data is typically collected from general public without professional training and knowledge. Different from the major principles of the past research, we suggest a new approach to enhance the content of VGI data by adding necessary spatio-temporal and quality information into the VGI data before it is submitted. After examining the essential characteristics of VGI data, the framework of enhanced information is subdivided into four major categories: volunteer identification information, spatio-temporal information, positioning information and mission information. By including the enhanced information, the experiment shows this framework can not only provides a solid and consistent basis for establishing VGI data, but also successfully helps decision makers to effectively compare and analyze VGI data collected from different sources. The developed prototype system also demonstrates the additional required information can be collected automatically, which implies it will not increase volunteers’ loading or demand more training, but can effectively improve the potential use of VGI data. As we focus on the premier stage of VGI process, the proposed approach will have a pervasive influence on how VGI data is collected, aggregated and analyzed in the future.

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