過去商圈評估通常需要藉由人力收集交通、租金、所得，與周邊相關同業經營狀況， 來估計商店在此商圈的存活機率。本研究使用地理資訊資料(政府公開資料)並結合影像 (Google 街景)，使用多模態機器學習模型(Multi-modal Machine Learning Models)對 商店在此商圈的存活機率作出預測。 除了參考過去商圈評估研究的問卷變項之外，我 們額外收集、製作相關變數、並且收集 Google 街景圖、使用人工智慧方法從圖像中抽 取街景特徵，並透過隨機森林建立預測性模型，欲驗證是否加入圖像能改善模型的辨識 能力。我們發現加入街景圖像特徵可使得預測的準確度提高。過去此領域學者主要以問 卷方法評估超商存活，通常以小範圍為主，且通常曠日費時。本研究發現同業超商的分 佈以統一超、全家影響最明顯，當地的電子發票消費情況，星巴克以及租金皆是影響超 商存活的主要原因，而交通設施的影響性較低。對零售業集團與加盟業者，本研究所提 出的方法可望降低評估商圈的時間以及人力成本。

In the past, business district evaluation usually required manpower. They collected traffic, rent, income to estimate the survival rate of convenience store. In my research, we combine geographic data with images from google street view. Then we use the multi-modal machine learning model to solve this problem.
We collect more columns of data than the past, and we use transfer learning to extract image features from street view data, and then we use machine learning to build model. We have to verify whether adding image features would boost model performance.
We found that adding street view features can boost the accuracy of whether convenience store would survive. In the past, scholars in this field mainly used questionnaires to estimate the survival of convenience stores, but they usually do it in a small area, and spend lots of time. We use big data method by multi-modal learning and transfer learning,which cost us less time. By means of this method, we could quickly copy to other industries.
We found that survival rate of convenience store are affected by the distribution of four convenience stores, e-invoice, Starbucks, rent etc. However, we found traffic factors are less important to convenience store. The benefits of this research are not only for retail franchisees, but also reduce the time and labor costs for pre-evaluating business circles.

目錄
論文審定書 i
誌謝 ii
摘要 iii
Abstract iv
目錄 v
圖次 vii
表次 viii
第一章 緒論1
第一節 研究背景與動機 1
第二節 研究目的 2
第三節 論文架構 3
第二章 文獻探討與整理4
第一節 過往商圈評估研究方法 4
第二節 零售商圈理論 7
第三節 機器學習方法 8
第四節 遷移學習(TRANSFER LEARNING) 10
第五節 多模態學習(MULTIMODAL-LEARNING) 13
第三章 研究方法15
第一節 研究方法流程 15
第二節 超商模態處理 17
第三節 街景模態處理 25
第四節 建立多模態模型 28
第四章 資料分析30
第一節 評估標準 30
第二節 分析結果 31
第三節 變數重要性 35
第五章 結論與建議39
第一節 研究結果與貢獻 39
第二節 研究限制與未來建議 40
參考文獻43

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