近年來由於政府實施無紙化及金融業者推行電子帳單的成效卓越，使得國內郵件的收寄量逐年下滑，郵局如何與民營業者競爭國內物流市場並達成盈餘目標，成為營運中不可忽視之因素。 傳統的郵局設點多依據公司規定與配合政府政策需求，甚少採用涉及複雜因素之區位分析進行選址。因此，如何有效且公正地評選郵局新設據點以提高收益，成為亟待解決之問題。 本研究目的在於提供高收益之郵局設點建議，我們提出一種評估中華郵政公司設點效益的方法，以國內郵局實際設點位置與相關空間資料來建置實驗模型。研究結果顯示，以本研究方法建立之預測模型可成功的提供中華郵政公司建議於何處新增據點可收最大功效。 我們首先蒐集中華郵政公司設點之鄰近區域資料，如競爭者設點數、人口因素、重要交通路口、郵件收寄量等。其次導入資料探勘技術分析影響郵件收寄量之因素，建立中華郵政公司設點收寄量預測模型。然後依照建立預測模型時所得到之區辨力分數，判斷採用何種資料探勘技術建立預測模型較適當。最後將所選定的預測模型套用於台北縣市各村里建物重心，透過環域資料分析以計算預估之收寄量，再整合各資料探勘技術之預測結果後推論出最佳設點建議。 實作中，以台北縣市資料來測試我們的方法。實驗數據顯示，我們的方法成功地找出十一個建議設點的村里，可提供給中華郵政公司作為高收益的設點建議。 / The amount of postal mail declines in recent years due to the efforts of paper-reduce policies implemented by the government, the industries, and the general publics. It becomes one of the important issues of the Chunghwa Post Company, to compete with other companies in domestic freight and mail services and to achieve the desired profits. Traditionally, the location of post offices were decided according to the government policies as well as the company regulations. The issues involved in the site selection analysis were seldom considered. Hence, developing an effective and fair mechanism to find the new post office locations that could improve the company’s surplus becomes an important problem to be solved. The purpose of this thesis is to provide recommendations to the post office site selection which will yield high profit to the company. We proposed a method to evaluate the effective profits that could be produced by a particular post office through the data mining techniques and the related GIS information. We first collect various data, such as neighborhood population, traffic flow, postal mail received at particular post office, competitor’s information, etc., and analyze these data using data mining techniques in order to establish prediction models. The most appropriate model was chosen to find the new post office sites. The Metropolitan Taipei area was chosen to illustrate our idea. The best sites for new post offices were selected through the buffering analysis as well as the data mining techniques. The experimental results show that our method can successfully find eleven locations which could generate most profit to Chunghwa Post Company if the new post offices were located in these places.

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.