因為疾病空間模式通常會與環境中的危險因子有很強烈的關聯性，因此流行病學家與社會大眾都對疾病的空間模式感到興趣。舉例來說，空間群聚就是一項非常受到重視的疾病空間模式，在眾多的空間群聚檢定方法種，Kulldorff和 Nagarwalla在1995年提出的空間掃描統計量是相當受到廣泛應用的方法，雖然這個統計方法可以檢定初空間資料的異質性，但是卻沒有辦法區隔這些異質性是來自於整體空間資料的相關性或是局部的空間群聚。在本篇論文中，我們將分別提出計次型的統計方法與貝氏統計方法兩種類型的空間群聚檢定方法來處理這樣的問題，其中計次型的統計方法為一兩階段的統計方法，首先採用EM演算法來估計空間自相關，並根據估計的結果與掃描窗格在偵測空間群聚；另一方面，貝氏方法則考慮加入群聚的中心位置及半徑作為事前的機率分布，進而透過MCMC的方法來計算出後驗分布的結果。除此之外，北卡羅來納的嬰兒猝死症和台灣老年人口癌症死亡資料將被用來示範與評價不同群聚檢定方法的差異與效果。

蔬菜的供給彈性非常小，收成之後，不僅產量會決定售價的高低，同類蔬 菜之間的替代效果，對於價格變化也有很大的影響力。因此若能事先預測 同類蔬菜未來的價格變化，即可計劃各類蔬菜的生產量。在本篇論文中， 我們試著將時空數列應用在非空間系統的經濟領域上。以臺灣地區三種常 見的蔬菜為例，分別以時空數列的 STARMA 模式與單變量 ARIMA 時間數 列，利用蔬菜批發價格建立模式，並比較其短期預測效果。最後，就價格 變動與產銷策略之關係進行討論。 / The supply elasticity of vegetables is so small. Once the production has been known, it would reflect on the price as soon as possible. And at the same time, the substitute effect between the vegetables has also great influence on the change of the price. However, if we could forecast the variation of the vegetables price,then the production-marketing strategy would be planned in advance. In this paper, we apply the spatial time series analysis on the field of economic, which is included in the non-spatial system. An investigate about the price variation for three kinds of vegetables in Taiwan.And the comparison of short-term forecasting performance for the STARMA model and traditional ARIMA model are also made. Finally, we discuss in detail about the relationship between the change of vegetable price and production-marketing strategy.

不動產價格的影響因素可按影響範圍區分為三大類，分別為影響整體不動產市場的「總體環境因素」，對一定範圍內不動產產生價格影響的「區域環境因素」，及對於單一不動產價格有所影響的「房屋個體因素」。其中，區域環境因素為影響個別不動產價格之首要因素，不動產之價格會受到所屬區域之政治、經濟、自然、社會等因素影響，「公共建設因素」為重要之區域環境之一，包含公共設施水準及其配置狀態。影響個別不動產價格之次要因素為「房屋個體因素」，可再次細分為三大影響因素如下：房屋本身所具有的特徵因素，即建築物之內部結構；房屋的建築方式，住宅類型等與全棟房屋有關的因素；與房屋鄰近地區環境有關的因素。而集合式與獨立式住宅因分屬不同房屋類型，即上述房屋價格形成因素中「房屋之建築方式」。實際交易上，獨立式住宅多半以「整棟建物」作為交易計算單位，對於坐落之基地權利持分通常為全部，而集合式住宅係以「樓層」、「戶」作為交易之計算單位，所有之基地持分與其他住戶共同持有，基於上述差異，過去研究多將建築方式視為影響房屋價格的條件之一，並據此分類次市場，因此較少有研究同時探討二者在空間分布上所具有的區位差異，及購屋者對於環境的偏好是否有所不同。且過去文獻多半以使用傳統迴歸模型為主要分析方法。但傳統迴歸分析所使用最小平方法迴歸模型，經常會產生殘差項存在有空間自相關的問題，及空間本身所存在之空間異質性偏誤，即空間不穩定性。因此 本文以台中市都會區內之住家使用房屋為樣本，依特徵價格理論將獨立式住宅與集合式住宅視為差異化商品，其內外特徵納入變數，使用GeoDa軟體進行空間自相關分析，並使用ArcGIS軟體中的地理加權迴歸模組（GWR）進行迴歸分析，藉以探討不同類型房屋所偏好之外部特徵，瞭解不同空間環境對房屋價格之影響及台中市都會區空間發展型態，並驗證其於規劃建設產生的空間不穩定性。 研究結果顯示，台中市建立之重大市政建設及土地開發計畫會影響集合式住宅與獨立式住宅之地價熱點分布，其共同之房價熱點均座落於高地價市地重劃區及重大市政建設分布位置，而獨立式住宅之房價熱點，進一步分布於與高地價市重劃區鄰近之市地重劃區；在購屋者對周圍設施偏好方面，集合式住宅購屋者對於國中小學、大學、重大市政建設、市場、公園均有顯著偏好，惟獨立式住宅購屋者對於大學、重大市政建設、公園有顯著偏好，對於國中小學、市場有不偏好情形，顯示不同類型住宅對於公共設施之偏好不完全相同；集合式住宅與獨立式住宅之房屋特徵屬性呈現空間不穩定性，分析結果顯示，上述二種住宅類型，對於本研究所有公共設施距離特徵屬性均呈現空間不穩定、非均質性的結果，顯示不同類型住宅均會與彼此具有相依性，並形成各區域間的異質性。 / Locational characteristics are the determinants of house prices. While former research have examined the effects of proximity to resources and facilities have on residential property values, and the change of the importance as located regions or submarkets vary, the effects of different types of houses are rarely compared due to their dissimilarity in ways of building and ownership. Do house price effects of the same facility alter when properties are situated in different submarkets? Further, the issues of spatial non-stationarity are usually overlooked by previous studies. By using transaction data of two common types of residential houses in Taichung City, we found house price hot spots of both detached houses and condos in regions with major constructions and development plans. Apart from the mutual hot spots found in high land price redevelopment zones, we also discovery hot spots of detached houses in areas in proximity to these redevelopment zones. As for desirable facilities for home buyers, neighborhood schools, universities, major constructions, local markets and parks were found to have an notable price impact on condos, whereas only universities, major constructions and parks in vicinity of in detached houses can we found significant price effects, suggesting the differences in the preference of consumers in distinct regions. Also, spatial dependence and heterogeneity are verified in both types of houses, making the entire market area spatial non-stationary.

當我們利用Shewhart管制圖(Shewhart control chart)或累積和管制圖(Cumulative-sum chart. CUSUM chart)來偵測製程時,通常假設製品係獨立取自一個服從均數μ和標準差為σ的獨立常態分配的管制下進行。但是若產品特性值呈現自相關時,這類管制圖就可能發生誤導的結果。本文利用時間序列模式來解決具相關變數的管制圖問題。並考慮利用非線性時間序列模式及特別原因管制圖(special-cause control chart)來檢視台灣經濟景氣指標是否處於控制中的狀態。並討論特別原因管制圖的連串長度分佈(run length distribution)。在最後的實例分析中,介紹自動控制的觀念。 / Traditionally, in the quality control process, such as: Shewhart control chart or CUSUM chart, it is assumed that the observation process follows an i.i.d normal distribution. If the assumption for independence fails, that is when the process exhibits type of autocorrelation, we need to find a more reliable decision method. In this paper, we will apply the time series analysis and structure changed concept to slove the serial correlation problem. The idea of automatic control can be applied in the explanation of this nonlinear process. Finally, a time series about the monitoring indicators of Taiwan is discussed in detail as an example.

區域發展差異現象一直以來為國家政策所關注，而近年來台灣地區失業率有逐漸上升的趨勢，各縣市之表現亦大相逕庭，顯示各地區存在失業差異現象。過去研究較少以空間觀點觀察失業相關議題，此外，關於區域差異因素對失業率之影響鮮少納入政府規劃因素。因此，本研究以空間自相關分析方法檢測失業是否具有空間相關性及聚集性，並應用長期追蹤資料（panel data）迴歸模型，以人口、產業、所得、都市化程度及政府規劃因素，分析台灣22縣市1988至2008近二十年來各區域差異因素對失業率之影響，藉由實證結果提出相關都市及產業政策之建議。實證結果發現，台灣失業分佈具有一定程度的空間相關性，且高低失業率在各縣市間亦有聚集現象。再者，依固定效果模型實證結果發現人口數、工業及服務業就業者百分比、都市化程度、工業區面積百分比與失業率間呈現顯著正向關係；經濟發展支出百分比與失業率呈現顯著負向關係；區域固定效果，即排除自變數影響下，各縣市本身區域特質對失業率之影響，結果顯示台北縣及桃園縣之係數為負向，南投縣、嘉義縣、台東縣與花蓮縣之係數為正向；時間固定效果方面，大部分年度皆具顯著性，且係數有由負轉正之趨勢，代表特定時間衝擊會對失業率造成影響。 / Differences in regional development have been a focus on national policies. Recently, there is a increasing trend in the unemployment rate in Taiwan, and it also differs from cities and counties, indicating there exists differences in regional unemployment. Previous research rarely combined unemployment issues with spatial perspective. In addition, the effect of regional discrepant factors on the unemployment rate rarely take government planning factors into account. Therefore, this study uses spatial autocorrelation analysis to detect whether unemployment has spatial correlation and aggregation, and applies panel data regression model with population, industry, income, the degree of urbanization, and government planning factors to analyze the effect of regional discrepant factors on the unemployment rate in Taiwan's 22 cities and counties from 1988 to 2008. According to the empirical results, we come up with some urban and industrial policy proposals. Empirical results indicate that the distribution of unemployment in Taiwan has a certain degree of spatial correlation, and high or low unemployment rate also has aggregation among cities and counties. Furthermore, according to the results of the fixed effects model, population, the percentage of industrial and service sector employment, the degree of urbanization, and the percentage of industrial area show a significant positive relationship with unemployment rate. The percentage of expenditures for economic development shows a significant negative relationship with unemployment rate. Region-specific fixed effect, which exclude the influence of independent variables, is the effect of regional characteristics of counties and cities on the unemployment rate. This result shows the coefficient of Taipei County and Taoyuan County is negative, and the coefficient of Nantou County, Chiayi County, Taitung County and Hualien County is positive. As for time-specific fixed effect, almost all years are significant, and the coefficient has the trend from negative to positive, indicating that a particular time impact will affect the unemployment rate.

近年來，由於人類對自然現象、社會現象或經濟現象的認知意識逐漸產生多元化的研判與詮釋，也因此致使人類思維數據化的概念已逐漸廣泛的被應用，對數據分析已從傳統以單一數值或平均值的分析作法，演變為考量多元化數值的分析作為。有鑑於此，在數據資料具備「模糊性」特質的現今，藉由模糊區間的演算方法，進一步探討之間的關係。 傳統的統計分析，對於兩變數間線性關係的強度判斷，一般是藉由皮爾森相關係數（Pearson’s Correlation Coefficient）的方法予以衡量，同時也可以經由係數的正、負符號判斷變數間的關係方向。然而，在現實生活中無論是環境資料或社會經濟資料等，均可能以模糊的資料型態被蒐集，如果當資料型態係屬於模糊性質時，將無法透過皮爾森相關係數的方法計算。 因此，本研究欲研擬一個較簡而易懂的方法，計算模糊區間資料的相關係數，據以呈現兩組模糊區間資料的相互影響程度。此外，若時間性之模糊區間資料被蒐集之際，我們亦提出利用中心點與長度之模糊自相關係數（ACF with the Fuzzy Data of Center and Length；簡稱CLACF）及模糊區間資料之自相關函數(ACF with Fuzzy Interval Data；簡稱FIACF)的方法，探討時間性模糊資料的自相關係數予以衡量。 / The classical Pearson’s correlation coefficient has been widely adopted in various fields of application. However, when the data are composed of fuzzy interval values, it is not feasible to use such a traditional approach to evaluate the correlation coefficient. In this study, we propose the specific calculation of fuzzy interval correlation coefficient with fuzzy interval data to measure the relationship between various stocks. In addition, in time series analysis, the auto-correlation function (ACF) can evaluate the effect of stationary for time series data. However, as the fuzzy interval data could be occurred, then the classical time series analysis will be not applied. In this paper, we proposed two approaches, ACF with the fuzzy data of center and length (CLACF) and ACF with fuzzy interval data (FIACF), to calculate the auto-correlation coefficient for fuzzy interval data, and use the scheme of Mote Carlo simulation to illustrate the effect of evaluation methods. Finally, we offer empirical study to indentify the performance of CLACF and FIACF which may measure the effect of lagged period of fuzzy interval data for daily price (low, high) of the Centralized Securities Trading Market and the result show that the effect of evaluation lagged period via CLACF and FIACF may response the effect more easily than classical evaluation of ACF for the close price of Centralized Securities Trading Market.

高速公路系統建設可提升區域與區域之間的可及性與易行性，同時達到節省旅行時間和成本、高速公路交流道沿線地區土地價值上升等效果。此外，高速公路系統建設對於周圍地區人口、產業可產生相當程度之影響，例如大量的人口遷移、區域交通影響衝擊、社會和經濟層面產生重大變化等，進而改變國家及區域發展方向。於此情況下，合宜地評估及分析高速公路系統建設開發對國家及區域發展之影響，將有助於其推展並降低對地區之不利影響。 本研究希望以我國從1976至2010年來，共九條高速公路系統 建設為研究主軸，利用台灣城際運輸系統需求模式(TMD2008)之旅行時間資料、內政部戶口普查及工商普查資料為基礎，並運用事前事後比較(before and after analysis)與空間自相關分析(Spatial Autocorrelation analysis)等分析方法對高速公路系統建設所造成區域人口、產業之空間變動進行評估，分析高速公路系統建設對於區域人口、產業及交通可及性之空間分布變化影響。最後則建構高速公路可及性對區域人口及產業空間影響模型，並針對歷年來高速公路系統建設對國家及區域發展的影響進行實證分析。 / The benefits of highway infrastructure investments include promoted inter-regional accessibility and mobility, saved travel time and cost and increased land value in freeway interchange areas. In sum, highway infrastructure investments play an important role in national and regional development. It contributes to the population re-distribution, the growth of economic activities, and more importantly, the land use change. Therefore, how to effectively evaluate the impact of highway investments on national and regional development is an important research topic. The analytical results in this study can be applied to increase the accuracy of population and economic projection, and to manage transportation policy decisions. Between 1976 and 2010, there were nine highway systems had been built in Taiwan. This study emphasizes on the impact of highway investments on the spatial distribution of population and industry, and regional accessibility change. In this paper, we utilize before and after analysis and Spatial Autocorrelation analysis with Geographic Information System to analyze demographic data, economic data and TDM2008 database in order to perform the spatial analysis of population and industry re-distribution along highway constructions. Finally, a spatial gravity model is built to verify and describe the related spatial impacts so that several influential factors can be identified by this empirical study.

最近，金融科技(FinTech)和行動金融服務，吸引越來越多的目光。新的創新金融科技服務，改變了金融服務的消費行為。行動網路的發展使人們能夠隨時隨地的享受行動銀行的服務已經是個不爭的事實。然而，由於無線網路先天的特性以及行動裝置的移動性，使行動金融的服務品質受到網路不穩定的影響。而且，隨著Bank 3.0時代的來臨，將會有大量的使用者同時使用行動金融服務，特別是在股市開盤以及重大訊息揭露的時候。因著大量使用者瞬間湧入，以及無線網路不穩定的影響，交易系統的效能很可能會時好時壞，所以無法滿足即時金融市場的需求。 本論文中，我們提出「行動銀行訊息即服務」的框架，使系統能夠很容易的水平擴充，並且能夠輕易的實現雙向通訊和雙向交易等多項行動金融服務。為了達到最少成本追求最大利益的目的，我們發展了能夠適應突發流量以及網路不穩定性的任務分配演算法，使得不用增加額外硬體成本的前提下改善系統效能。然後，為了實驗欲模擬大量行動裝置的使用者，我們觀察真實網路的特性並發明了網路延遲自相關模型來驗證我們提出的任務分配演算法。結果顯示，透過此任務分配演算法，確實能夠有效改善系統資源管理的能力。最後，本研究將系統佈署於真實網路環境當中，並且發現進行同樣實驗的結果與採用網路延遲自相關模型的實驗結果一致。因此，本研究間接驗證了網路自相關模型的正確性，以及證明本任務分配演算法，在突發流量和網路不穩定的即時行動金融服務環境下，能有效降低系統響應時間。 / FinTech (financial technology) and mobile financial services are getting more and more attention recently. New innovative FinTech services change the consumption behavior for financial services. It is an indisputable fact development of the mobile Internet allows people to enjoy mobile banking everywhere and anytime. However, due to the nature of wireless networking and the mobility of the mobile device, the quality of mobile financial service will be affected by network instability. Moreover, with the coming of Bank 3.0, a huge amount of users would be in the mobile service of finance simultaneously, especially when the instances of the stock market opening or disclosure of highly important financial message. As the result of bursty traffic and network instability, the performance of transaction system is up and down, making it tough to satisfy the demand of real-time financial markets. In this thesis, we propose a “Mobile Banking Messaging as a Service Framework” that can easily scale out and fulfill functions comprising Bilateral Communication, Bilateral Trading, and many other mobile financial services. To pursuit of the greatest benefit along with investment of the least resources, we develop the task assignment algorithm which can adapt the system to bursty traffic and unstable networks to improve performance for free. Then, in order to simulate a large number of mobile users, we observe the characteristic of real-world network delay and propose a network delay autocorrelation model to verify our task assignment algorithm. The results of experiment show that we could actually use our task assignment algorithm to improve the ability of the system to manage resource. Finally, we deploy our system in a real-world network delay environment and find that the results obtained in the real condition are the same with our simulation results. Therefore, this research can indirectly verify the correctness of the network delay autocorrelation model, and prove that our task assignment algorithm can effectively reduce the system response time for real-time mobile financial service system under bursty traffic and unstable networks.

近年來由於地理資訊系統（GIS）的快速發展發，空間資料分析開始受到重視並在社會科學領域中逐漸扮演重要的角色。雖然一般的統計方法已在傳統資料分析上發展已久，然而它們卻不能有效地說明空間性資料，並且無法充分處理空間相依或空間異質性問題。一般而言，空間資料分析主要有兩個分派:模型導向學派與資料導向學派。本文研究目的在於應用空間統計方法合理且充分地評估房地產價值，研究方法包含地理統計(克利金和共克利金)、地理加權迴歸與空間特徵價格模型等，並且以台中市不動產資料進行實證探究。這項新的研究技術在不動產評價領域中將可提供更好的解析能力，使其在評價過程中或是不動產投資決策時，成為一個更強而有力的分析工具。 / In recent years, spatial data analysis has received significant awareness and played an important role in social science because of the rapid development of Geographic Information System (GIS). Although classic statistical methods are attractive in traditional data analysis, they cannot be executed seriously for spatial data. Standard statistical techniques didn’t sufficiently deal with spatial dependence or spatial heterogeneity issues. Generally, the model-driven method and the data-driven method are mainly the two branches of the spatial data analysis. The purpose of this paper is to apply spatial statistics methods including geostatistical methods (kriging and cokiging), geographically weighted regression, and spatial hedonic price models to real estate analysis. It seems to be completely reasonable and sufficient. The real estate data in Taichung city (Taiwan) is used to carry out our exploration. These techniques give better insight in the field of real estate assessment. They can apply a good instrument in mass appraisal and decision concerning real estate investment.