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題名 地理資訊系統及資料探勘技術在連鎖咖啡店設點之分析與研究
Coffee shop location analysis using GIS and data mining techniques作者 劉奕宏
Liu, Yi Hung貢獻者 何瑁鎧
Hor, Maw Kae
劉奕宏
Liu, Yi Hung關鍵詞 選址分析
地理資訊系統
資料探勘
site selection analysis
geographical information system
data mining日期 2011 上傳時間 4-Sep-2013 17:10:03 (UTC+8) 摘要 近年來台灣連鎖咖啡店消費人口的穩定成長，提升了連鎖咖啡店的市場規模與消費產值，傳統利潤導向的市場經營方式，使得連鎖咖啡店的競爭更趨激烈，如何訂定正確的選址與經營策略，成為在高度競爭市場中存活的重要關鍵。傳統的選址問題需要投入大量的人力與時間進行相關資訊的蒐集、訪查與評估，故而在新設營業點時，較少運用複雜的因素進行區位選址的分析與評估。因此能透過較多的因素，從區位選址與營利效應等觀點進行分析，協助投資者獲得更好的利潤，提高決策成功的機率，是極為重要的問題。本論文的目的，在於為連鎖咖啡店之選址決策，提出能增加成功機率之設點建議。我們依據連鎖咖啡市場雙雄在訂定選址決策的成功經驗，透過相關係數進行人口與經濟活動因素之統計分析，以找出其成功選址之關鍵因素。同時運用資料探勘的分類技術，建構成功選址之分類模型，並經由地理資訊系統提供的圖層資料，對連鎖咖啡市場雙雄之競爭關係進行分析與評估，以提供正確選址及設點之建議。實作中我們採用台北市出租店面之空間資料，以探討並評估本研究建議模型之實際效益。實驗結果顯示，透過本研究之選址分類模型進行設點類型之預測，有七成以上之達成率，顯示本研究提出之模型能有效增加選址的成功機率，同時經由競爭對手設點空間關係之分析，亦能提供有利選址決策之建議。
The number of customers of coffee shop chains has grown steadily in recent years that cause the market size as well as the total consumption value increase rapidly and continuously. The competition among the chain coffee stores get even worse under the traditional profit oriented management style. In such case, it is crucial to make the correct decisions when selecting the coffee shop locations as well as making operation strategies in opening new coffee shops.Traditionally, it takes a great amount of time and human resources in collecting relevant information, conducting field visits as well as site evaluations when making coffee shop site selections. One seldom considers complex factors of site evaluation or field analyzing in selecting the location of new coffee shop. Hence, it will be one of the major contributions if one can find a mechanism in analyzing the site selection as well as profit evaluation to help the investors to produce better profit and to improve the chance of success.The goal of this thesis is to provide recommendations to improve the success rate of chain coffee shop site selection strategy. Based on the coffee market leaders’ success experiences in formulating the site selection strategies, we analyzed the correlation coefficients of the population as well as economy activities in order to identify the key factors in successful site selection strategies. We also used data mining techniques to construct the classification models of successful site selection. In addition, we analyzed and evaluated competition relations between the two leading chain coffee brands using the geographic information systems to obtain appropriate recommendations in new site selections.The shop rental information of Taipei City was used to explore and to evaluate the models recommended in our mechanism. The experimental results showed that the prediction through the classification models for site selections can achieve 70% of success rate. This indicates our mechanism effectively improve the successful rate of site selections. Moreover, the experimental results also show that the spatial analysis of site selections between the competitors is helpful in providing appropriate site selection strategies.參考文獻 [1] 許愷，「台灣地區連鎖店及連鎖系統的發展與改進」，中國文化大學企管所碩士論文，民國70年6月。[2] 詹益盺，「咖啡連鎖店之市場區隔與定位研究－以中市星巴克與85°C為實証分析」，朝陽科技大學企業管理系碩士論文，民國96年7月。[3] 陳坤宏，「台北市民商業空間選擇行為與空間結構關係之研究」，中國文化大學實業計劃(工學組)研究所碩士論文，民國73年6月。[4] 李孟熹，「連鎖店管理-實務操演手冊」，科技圖書股份有限公司，民國87年。[5] 蔡界勝，「餐飲管理與經營」，五南圖書出版公司，民國85年。[6] 曾憲雄、蔡秀滿、蘇東興、曾秋蓉和王慶堯，「資料探勘Data Mining」，台北，旗標出版社，民國94年。[7] 周天穎、周學政，「Arc View 透視 3.X」，松崗電腦圖書資料股份有限公司，民國89年。[8] 陳章旺，「零售營銷:實戰的觀點」，北京大學出版社，民國97年1月。[9] Ankerst, M., Elsen, C., Ester, M. and Kriegel, H.-P., “Visual classification: An interactive approach to decision tree construction,” In Proc. 1999 Int. Conf. Knowledge Discovery and Data Mining (KDD’99), pp.392–396, San Diego, CA, Aug. 1999.[10] Bell, Simon J., ”Image and consumer attraction to intraurban retail areas: An environmental psychology,” Journal of Retailing and Consumer Services, vol. 6, pp.67-78, 1999.[11] Berry, M. J. A. and Linoff, G. S., “Data Mining Technique Techniques: For Marketing, Sales and Customer Support,” New York: John Wiley and Sons Inc., 1997.[12] Berson, A., Smith, S. and Thearling, K., “Building Data Mining Application for CRM,” New York, McGraw-Hill., 2001.[13] Chang, Kang-tsung, “Introduction to Geographic Information Systems, 3rd ed,” New York: McGraw-Hill., 2006.[14] Christensen, C. M., “The Innovator’s Dilemma: When New Technology Causes Great Firms to Fail,” Harvard Business School Press, 1997.[15] Han, J., Kamber, M., ”Data Mining: Concepts and Techniques,” Morgan Kaufmann Publishers, San Franscisco, 2000.[16] Kotler, P., “Marketing Management: Analysis, Planning, Implementation, and Control,” 10th ed, Pearson Canada, 2000.[17] Levy, M. and Weitz, B.A., “Retail Locations,” Retailing Management 6th ed, Irwin/McGraw-Hill, pp.184-205, 2007.[18] Mitchell, Tom M., “Machine Learning,” McGraw-Hill., 1997.[19] Nelson, R.L., “The Selection of Retail Location,” NY, 1958.[20] Quinlan, J. R., “Induction of Deciscion Trees,” Machine Learning, vol. 1, pp.81-106, 1979.[21] Quinlan, J. R., “C4.5: Programs for Machine Learning,” Morgan Kaufmann Publishers, San Mateo, 1993.[22] Quinlan, J. R., “Decision Trees and Decisionmaking,” IEEE Transactions on Systems. Man. and Cybernetics, vol. 20, no. 2, pp.339-346, 1990.[23] Star, J., Estes, J., “Geographic Information Systems: An Introduction,” Prentice-Hall, Inc., 1990.[24] Winston, P., "Learning by Building Identification Trees," in P. Winston, Artificial Intelligence, Addison-Wesley Publishing Company, pp.423-442, 1992.[25] Yeung, Raymond W., “A First Course in Information Theory,” Kluwer Academic/Plenum Publishers, 2002.[26] ArcGIS, http://www.esri.com/products/index.html[27] Weka, http://www.cs.waikato.ac.nz/ml/weka/[28] Correlation, http://zh.wikipedia.org/wiki/%E7%9B%B8%E9%97%9C [29] Buying Power Index, http://wiki.mbalib.com/zh-tw/%E8%B4%AD%E4%B 描述 碩士
國立政治大學
資訊科學學系
98971011
100資料來源 http://thesis.lib.nccu.edu.tw/record/#G0098971011 資料類型 thesis dc.contributor.advisor 何瑁鎧 zh_TW dc.contributor.advisor Hor, Maw Kae en_US dc.contributor.author (Authors) 劉奕宏 zh_TW dc.contributor.author (Authors) Liu, Yi Hung en_US dc.creator (作者) 劉奕宏 zh_TW dc.creator (作者) Liu, Yi Hung en_US dc.date (日期) 2011 en_US dc.date.accessioned 4-Sep-2013 17:10:03 (UTC+8) - dc.date.available 4-Sep-2013 17:10:03 (UTC+8) - dc.date.issued (上傳時間) 4-Sep-2013 17:10:03 (UTC+8) - dc.identifier (Other Identifiers) G0098971011 en_US dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/60260 - dc.description (描述) 碩士 zh_TW dc.description (描述) 國立政治大學 zh_TW dc.description (描述) 資訊科學學系 zh_TW dc.description (描述) 98971011 zh_TW dc.description (描述) 100 zh_TW dc.description.abstract (摘要) 近年來台灣連鎖咖啡店消費人口的穩定成長，提升了連鎖咖啡店的市場規模與消費產值，傳統利潤導向的市場經營方式，使得連鎖咖啡店的競爭更趨激烈，如何訂定正確的選址與經營策略，成為在高度競爭市場中存活的重要關鍵。傳統的選址問題需要投入大量的人力與時間進行相關資訊的蒐集、訪查與評估，故而在新設營業點時，較少運用複雜的因素進行區位選址的分析與評估。因此能透過較多的因素，從區位選址與營利效應等觀點進行分析，協助投資者獲得更好的利潤，提高決策成功的機率，是極為重要的問題。本論文的目的，在於為連鎖咖啡店之選址決策，提出能增加成功機率之設點建議。我們依據連鎖咖啡市場雙雄在訂定選址決策的成功經驗，透過相關係數進行人口與經濟活動因素之統計分析，以找出其成功選址之關鍵因素。同時運用資料探勘的分類技術，建構成功選址之分類模型，並經由地理資訊系統提供的圖層資料，對連鎖咖啡市場雙雄之競爭關係進行分析與評估，以提供正確選址及設點之建議。實作中我們採用台北市出租店面之空間資料，以探討並評估本研究建議模型之實際效益。實驗結果顯示，透過本研究之選址分類模型進行設點類型之預測，有七成以上之達成率，顯示本研究提出之模型能有效增加選址的成功機率，同時經由競爭對手設點空間關係之分析，亦能提供有利選址決策之建議。 zh_TW dc.description.abstract (摘要) The number of customers of coffee shop chains has grown steadily in recent years that cause the market size as well as the total consumption value increase rapidly and continuously. The competition among the chain coffee stores get even worse under the traditional profit oriented management style. In such case, it is crucial to make the correct decisions when selecting the coffee shop locations as well as making operation strategies in opening new coffee shops.Traditionally, it takes a great amount of time and human resources in collecting relevant information, conducting field visits as well as site evaluations when making coffee shop site selections. One seldom considers complex factors of site evaluation or field analyzing in selecting the location of new coffee shop. Hence, it will be one of the major contributions if one can find a mechanism in analyzing the site selection as well as profit evaluation to help the investors to produce better profit and to improve the chance of success.The goal of this thesis is to provide recommendations to improve the success rate of chain coffee shop site selection strategy. Based on the coffee market leaders’ success experiences in formulating the site selection strategies, we analyzed the correlation coefficients of the population as well as economy activities in order to identify the key factors in successful site selection strategies. We also used data mining techniques to construct the classification models of successful site selection. In addition, we analyzed and evaluated competition relations between the two leading chain coffee brands using the geographic information systems to obtain appropriate recommendations in new site selections.The shop rental information of Taipei City was used to explore and to evaluate the models recommended in our mechanism. The experimental results showed that the prediction through the classification models for site selections can achieve 70% of success rate. This indicates our mechanism effectively improve the successful rate of site selections. Moreover, the experimental results also show that the spatial analysis of site selections between the competitors is helpful in providing appropriate site selection strategies. en_US dc.description.tableofcontents 第一章 緒論 11.1 研究動機與目的 11.2 問題描述 31.3 研究貢獻 4第二章 文獻回顧與探討 62.1 連鎖咖啡店之經營 62.2 選址考慮因素 82.3 資料探勘 92.4 地理資訊系統 10第三章 選址之討論 113.1 系統流程 113.2 資料前處理 133.2.1 人口資料 143.2.2 經濟活動資料 143.2.3 空間關係資料 143.3 選址關鍵因素選取 153.3.1 相關係數 163.3.2 購買力指數 173.3.3 零售飽和指數 173.3.4 人口特性 183.3.5 經濟活動 213.3.6 空間關係 24第四章 分類模型與實例探討 464.1 選址分類模型建構 464.2 實例探討與設點評估 524.3 結果分析 55第五章 結論與未來研究 655.1 研究結論 655.2 未來研究 67參考文獻 68 zh_TW dc.format.extent 938525 bytes - dc.format.mimetype application/pdf - dc.language.iso en_US - dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#G0098971011 en_US dc.subject (關鍵詞) 選址分析 zh_TW dc.subject (關鍵詞) 地理資訊系統 zh_TW dc.subject (關鍵詞) 資料探勘 zh_TW dc.subject (關鍵詞) site selection analysis en_US dc.subject (關鍵詞) geographical information system en_US dc.subject (關鍵詞) data mining en_US dc.title (題名) 地理資訊系統及資料探勘技術在連鎖咖啡店設點之分析與研究 zh_TW dc.title (題名) Coffee shop location analysis using GIS and data mining techniques en_US dc.type (資料類型) thesis en dc.relation.reference (參考文獻) [1] 許愷，「台灣地區連鎖店及連鎖系統的發展與改進」，中國文化大學企管所碩士論文，民國70年6月。[2] 詹益盺，「咖啡連鎖店之市場區隔與定位研究－以中市星巴克與85°C為實証分析」，朝陽科技大學企業管理系碩士論文，民國96年7月。[3] 陳坤宏，「台北市民商業空間選擇行為與空間結構關係之研究」，中國文化大學實業計劃(工學組)研究所碩士論文，民國73年6月。[4] 李孟熹，「連鎖店管理-實務操演手冊」，科技圖書股份有限公司，民國87年。[5] 蔡界勝，「餐飲管理與經營」，五南圖書出版公司，民國85年。[6] 曾憲雄、蔡秀滿、蘇東興、曾秋蓉和王慶堯，「資料探勘Data Mining」，台北，旗標出版社，民國94年。[7] 周天穎、周學政，「Arc View 透視 3.X」，松崗電腦圖書資料股份有限公司，民國89年。[8] 陳章旺，「零售營銷:實戰的觀點」，北京大學出版社，民國97年1月。[9] Ankerst, M., Elsen, C., Ester, M. and Kriegel, H.-P., “Visual classification: An interactive approach to decision tree construction,” In Proc. 1999 Int. Conf. Knowledge Discovery and Data Mining (KDD’99), pp.392–396, San Diego, CA, Aug. 1999.[10] Bell, Simon J., ”Image and consumer attraction to intraurban retail areas: An environmental psychology,” Journal of Retailing and Consumer Services, vol. 6, pp.67-78, 1999.[11] Berry, M. J. A. and Linoff, G. S., “Data Mining Technique Techniques: For Marketing, Sales and Customer Support,” New York: John Wiley and Sons Inc., 1997.[12] Berson, A., Smith, S. and Thearling, K., “Building Data Mining Application for CRM,” New York, McGraw-Hill., 2001.[13] Chang, Kang-tsung, “Introduction to Geographic Information Systems, 3rd ed,” New York: McGraw-Hill., 2006.[14] Christensen, C. M., “The Innovator’s Dilemma: When New Technology Causes Great Firms to Fail,” Harvard Business School Press, 1997.[15] Han, J., Kamber, M., ”Data Mining: Concepts and Techniques,” Morgan Kaufmann Publishers, San Franscisco, 2000.[16] Kotler, P., “Marketing Management: Analysis, Planning, Implementation, and Control,” 10th ed, Pearson Canada, 2000.[17] Levy, M. and Weitz, B.A., “Retail Locations,” Retailing Management 6th ed, Irwin/McGraw-Hill, pp.184-205, 2007.[18] Mitchell, Tom M., “Machine Learning,” McGraw-Hill., 1997.[19] Nelson, R.L., “The Selection of Retail Location,” NY, 1958.[20] Quinlan, J. R., “Induction of Deciscion Trees,” Machine Learning, vol. 1, pp.81-106, 1979.[21] Quinlan, J. R., “C4.5: Programs for Machine Learning,” Morgan Kaufmann Publishers, San Mateo, 1993.[22] Quinlan, J. R., “Decision Trees and Decisionmaking,” IEEE Transactions on Systems. Man. and Cybernetics, vol. 20, no. 2, pp.339-346, 1990.[23] Star, J., Estes, J., “Geographic Information Systems: An Introduction,” Prentice-Hall, Inc., 1990.[24] Winston, P., "Learning by Building Identification Trees," in P. Winston, Artificial Intelligence, Addison-Wesley Publishing Company, pp.423-442, 1992.[25] Yeung, Raymond W., “A First Course in Information Theory,” Kluwer Academic/Plenum Publishers, 2002.[26] ArcGIS, http://www.esri.com/products/index.html[27] Weka, http://www.cs.waikato.ac.nz/ml/weka/[28] Correlation, http://zh.wikipedia.org/wiki/%E7%9B%B8%E9%97%9C [29] Buying Power Index, http://wiki.mbalib.com/zh-tw/%E8%B4%AD%E4%B zh_TW
