People's daily lives can be described as colorful, but they can likewise fall into a cyclical situation. Each person's daily journey between home and destination may be repeated through a number of urban spaces that are not necessarily interesting, but more or less in raising the frequency of visual fatigue. The relatively fast pace seen in the central city life and the energy of fighting for life can cause some pressure psychologically, and anxiety and irritation can follow. Although there is no shortage of larger parks or squares and other recreational areas in the city for people to relax, specific places and times do not cater to everyone all the time. The core idea of this project is to explore more possibilities of urban space to give people additional help in mental health by increasing the frequency of relaxation in daily life. Spatial interventions do not have to be huge or shaped. Perhaps a street light at night can provide a brief moment of solace and warmth. The possibilities of using urban spaces and the characteristics they possess have never been fully explored, some unnoticed corners, or how different spaces can make interesting connections with each other.

當台灣一旦進行較大型的公職人員或民意代表選舉時，各個新聞媒體都會報導出其所獲得的民意調查結果，可見得媒體、民眾、政黨以及候選人，都會將民調當成很好的指標工具。 當我們獲知所公佈的民意調查資料時，都會看到報導幾乎抽樣樣本都在1000個左右，來說明其抽樣誤差約在3%。但是本文所探討的是，我們是否需要每次固定在1000個左右的樣本數，或許我們可以更快就可以看出結果，早一點讓決策者多一些時間做出決策，不但省下了多餘的時間，也可以節省相當多的金錢。 在本文中以民國八十七年的由全國意向民意調查中心所做的台北市長選舉意向調查為例，分別在五月和六月的兩次的訪問中，我們可以很清楚明顯的看出，當我們進行到六百個樣本數時，我們所訪問的資料已經出現相當穩定的結果，即使訪問更多的樣本數的樣本，對於結果的差異幾乎沒有影響，所以在此兩個例子中，我們可以在訪問過程中，就有足夠很好的證據去停止再抽樣訪問，可以和訪問完成一千多個樣本時才作決策時比較，我們可以省下三、四百個樣本數的訪問時間來思考做決策，也省下了三、四百個樣本數的成本來做其他的工作；若是並無法能夠獲得明確的結果，也是會做到原先設定的固定樣本數訪問法，一樣無法獲得相當好的結論，也是必須再繼續抽樣，才能夠看出更明顯的結果。 本文所探討的內容，並非每次訪問過程都是只要像例子中，只需六百個樣本左右即可，這是需視各個不同訪問的個案，也許訪問中各問項變動性太大，那麼就需要更多的樣本數來做觀測。

For the last several decades, sequential change point problems have been studied in both the theoretical area (sequential analysis) and the application area (industrial SPC). In the conventional application, the baseline process is assumed to be stationary, and the shift pattern is a step function that is sustained after the shift. However, in biosurveillance, the underlying assumptions of problems are more complicated. This thesis investigates several issues in biosurveillance such as non-homogeneous populations, spatiotemporal surveillance methods, and correlated structures in regional data. The first part of the thesis discusses popular surveillance methods in sequential change point problems and off-line problems based on count data. For sequential change point problems, the CUSUM and the EWMA have been used in healthcare and public health surveillance to detect increases in the rates of diseases or symptoms. On the other hand, for off-line problems, scan statistics are widely used. In this chapter, we link the method for off-line problems to those for sequential change point problems. We investigate three methods--the CUSUM, the EWMA, and scan statistics--and compare them by conditional expected delay (CED). The second part of the thesis pertains to the on-line monitoring problem of detecting a change in the mean of Poisson count data with a non-homogeneous population size. The most common detection schemes are based on generalized likelihood ratio statistics, known as an optimal method under Lodern's criteria. We propose alternative detection schemes based on the weighted likelihood ratios and the adaptive threshold method, which perform better than generalized likelihood ratio statistics in an increasing population. The properties of these three detection schemes are investigated by both a theoretical approach and numerical simulation. The third part of the thesis investigates spatiotemporal surveillance based on likelihood ratios. This chapter proposes a general framework for spatiotemporal surveillance based on likelihood ratio statistics over time windows. We show that the CUSUM and other popular likelihood ratio statistics are the special cases under such a general framework. We compare the efficiency of these surveillance methods in spatiotemporal cases for detecting clusters of incidence using both Monte Carlo simulations and a real example. The fourth part proposes multivariate surveillance methods based on likelihood ratio tests in the presence of spatial correlations. By taking advantage of spatial correlations, the proposed methods can perform better than existing surveillance methods by providing the faster and more accurate detection. We illustrate the application of these methods with a breast cancer case in New Hampshire when observations are spatially correlated.