結合政府開放資料集於建構開業選址決策支援系統－－以店址當地消費能力與鄰近產業特性觀點

Combing the Government Open Datasets to Build the Location Selection Decision Support System–The Local Consumption Ability and Neighboring Industries Perspectives

本研究希望結合開放資料集與現今的機器學習演算法建立一個「開業選址決策支援系統」，為了協助房東找到店面適合開的行業；並幫助創業者找到適合開店的位置。本研究為了收集店家資訊串接3個跨領域的政府公開資料；接著為了建立商圈資訊計算與公共場所和其他店家的距離；並考慮行業是否有群聚效應使用空間自相關分析Moran’s I；還利用隨機森林重要性找出影響每一個行業的關鍵因素，最後使用k-最近鄰法做為推薦的依據。本研究在Precision指標中與其他熱門分類演算法進行比較至少高出26.8%，足見本研究建立之預測模型相較其他演算法有一定的預測率。 This study hope to combine the open data and current machine learning methods to establish “location selection decision support system”, which can provide suggestions to both landlords of the store, and the coming shopkeepers. For collecting store information, this work connects 3 different sources of open datasets. Furthermore, to quantify the surrounding information of a store, this study measures the distance between locations predicted to landmark or to each type of stores. For discriminating whether or not a type of store congregates, this work adopts Moran’s I spatial autocorrelation analysis. spatial autocorrelation analysis. This study utilizes the Random Forest Importance to identify the key factors of 30 distinctive types of store, and apply k-nearest neighbour for the foundation of recommendation. As the results, this work shows in Precision, the proposed method is at least 26.8% higher than other classification algorithms.