探討某縣市運用商業智慧系統建構視覺化資料分析菸害防制稽查熱點

Explorating the utilization of business intelligence and visual data analysis on hot spot inspection for tobacco hazards prevention in a metropolitan

背景：某院轄市每年依據菸害防制法規範之室內及戶外禁止吸菸場所，派稽查人員進行稽查，惟對於民眾陳情、媒體報導等菸害案件，無法第一時間蒐集違規吸菸熱點。本研究目的運用商業智慧系統探勘與菸害相關之衛生單位部門網路系統資訊，將大數據資料整合以視覺化資料分析禁止吸菸場所熱點分布，縮短熱點產出工作天數，蒐集與違規吸菸熱點之關聯性，降低檢舉案件及提升行政效率。
方法：本研究以安裝Power BI增益集的方式為開發平臺，使用Power Query將資料從陳情系統、派案系統、裁罰系統、稽查系統之數據經由萃取、轉置等方式至Excel資料表，再以Power Pivot載入為資料模型，最終以Power View視覺效果呈現。
結果：藉由商業智慧系統(Power BI)，並使用Power Query將資料從陳情系統、派案系統、裁罰系統、稽查系統之資料庫數據分析商業智慧系統將資料庫數據分析發現，陳情案件與臨時派員稽查案件於105年至107年前呈一正相關趨勢；主動稽查不合格場域依序以其他、休閒娛樂及提供民眾消費場所最多，臨時派員稽查場域以公園及及電子遊戲場(休閒娛樂)等十大行業最多，因違反菸害法之室內或戶外禁止吸菸場所之違規裁罰場所以戶外公告禁止吸菸場所最多、其次以公園綠地、休閒娛樂場所被裁罰案件數為多，故主動稽查案件、臨時派員稽查案件與裁罰案件其關聯性一致。另透過Power Query導入後，使操作步驟精簡化，使導入前後菸害熱點產出工作作業時間減少天數達75%；處理步驟簡化操作流程達67%。
結論：重要研究發現，無論是系統面、稽查人員、決策人員等面向皆能提升稽查效能，減少重複檢舉發生，進而降低民眾陳情，未來具有引進全國，並提供各縣市執行菸害稽查業務之參考。

Background: A city under the jurisdiction of a hospital sends inspectors to conduct inspections on indoor and outdoor smoking places regulated by the Tobacco Harm Prevention Law every year. However, for smoking harm cases such as public complaints and media reports, it is impossible to collect illegal smoking hot spots in the first place. The purpose of this research is to use the business intelligence system to explore the network system information of health units and departments related to tobacco harm, integrate big data to analyze the distribution of hot spots in non-smoking places with visual data, shorten the working days of hot spot production, collect and illegal smoking. The relevance of hotspots, reducing the number of reported cases and improving administrative efficiency.
Methods: This study uses the installation of Power BI gain set as the development platform, and uses Power Query to extract and transpose the data from the reporting system, case dispatching system, punishment system, and inspection system to Excel data sheets, and then use Power Query to extract and transpose the data to Excel data sheets. Loaded with Power Pivot as the data model, and finally presented with Power View visual effects.
Result: By using the business intelligence system (Power BI) and using Power Query to analyze the data from the database data of the petition system, case dispatching system, punishment system, and inspection system. There was a positive correlation trend between 105 and 107 years ago when temporary personnel were dispatched to inspect cases; the unqualified venues for active inspections were followed by others, leisure and entertainment, and public consumption places, and the temporary inspection venues were parks and electronic games. The top 10 industries such as casinos (leisure and entertainment) are the most, and the number of places that violate the regulations of indoor or outdoor smoking places that violate the smoking hazard law is the largest number of places that prohibit smoking, followed by parks, green spaces, and leisure and entertainment places. The number of cases is large, so the voluntary inspection cases, the temporary dispatch of personnel to inspect the cases, and the sanction cases have the same relevance. In addition, after importing through Power Query, the operation steps are streamlined, and the working time for the production of smoke hot spots before and after the import is reduced by 75%; the processing steps are simplified by 67%.
Conclusion: Important research has found that whether it is the system, inspectors, decision makers, etc., can improve the efficiency of inspections, reduce the occurrence of repeated reports, and then reduce public complaints refer to.