【正文】 isson Model All the models described above are based on data observed at discrete locations that are considered to be nonrandom, as defined by a regular or irregular lattice of location points. That is, the locations of the observations are considered to be fixed, and we evaluate the spatial randomness of the observation conditioning on the lattice. Hence, those are all versions of what are called discrete scan statistics. In a continuous scan statistics, observations may be located anywhere within a study area, such as a square or rectangle. The stochastic aspect of the data consists of these random spatial locations, and we are interested to see if there are any clusters that are unlikely to occur if the observations where independently and randomly distributed across the study area. Under the null hypothesis, the observations follow a homogeneous spatial Poisson process with constant intensity throughout the study area, with no observations falling outside the study area. Example: The data may consist of the location of bird nests in a square kilometer area of a forest. The interest may be to see whether the bird nests are randomly distributed spatially, or in other words, whether there are clusters of bird nests or whether they are located independently of each other. In SaTScan, the study area can be any collection of convex polygons, which are convex regions bounded by any number straight lines. Triangles, squares, rectangles, rhombuses, pentagons and hexagons are all examples of convex polygons. In the simplest case, there is only one convex polygon, but the study area can also be the union of multiple convex polygons. If the study area is not convex, divide it into multiple convex polygons and define each one separately. The study area does not need to be contiguous, and may for example consist of five different islands. The analysis is conditioned on the total number of observations in the data set. Hence, the scan statistic simply evaluates the spatial distribution of the observation, but not the number of observations. The likelihood function used as the test statistic is the same as for the Poisson model for the discrete scan statistic, where the expected number of cases is equal to the total number of observed observations, times the size of the scanning window, divided by the size of the total study area. As such, it is a special case of the variable window size scan statistic described by Kulldorff (1997). When the scanning window extends outside the study area, the expected count is still based on the full size of the circle, ignoring the fact that some parts of the circle have zero expected counts. This is to avoid strange noncircular clusters at the border of the study area. Since the analysis is based on Monte Carlo randomizations, the pvalues are automatically adjusted for these boundary effects. The reported expected counts are based on the full circle though, so the Obs/Exp ratios provided should be viewed as a lower bound on the true value whenever the circle extends outside the spatial study region. The continuous Poisson model can only be used for purely spatial data. It uses a circular scanning window of continuously varying radius up to a maximum specified by the user. Only circles centered on one of the observations are considered, as specified in the coordinates file. If the optional grid file is provided, the circles are instead centered on the coordinates specified in that file. The continuous Poisson model has not been implemented to be used with an elliptic window. 連續(xù)泊松模型所有的模型描述是基于上述數(shù)據(jù)觀察離散地點(diǎn)被認(rèn)為是隨機(jī)的，所確定的規(guī)則或不規(guī)則格點(diǎn)的位置。因此，這些都是什么版本被稱為離散掃描統(tǒng)計(jì)。隨機(jī)方面的數(shù)據(jù)由隨機(jī)空間位置，和我們有興趣，看看是否有任何群是不可能發(fā)生，如果意見(jiàn)是獨(dú)立隨機(jī)分布在研究區(qū)。例如：數(shù)據(jù)可能包括位置的鳥(niǎo)巢一平方公里面積的森林。在SaTS can，研究區(qū)可以是任何集合的凸多邊形，凸區(qū)域內(nèi)的任何數(shù)量的直線。在最簡(jiǎn)單的情況下，只能有一個(gè)凸多邊形，但研究區(qū)域也可以結(jié)合多個(gè)凸多邊形。研究領(lǐng)域不需要是連續(xù)的，例如可能由五個(gè)不同的島嶼。因此，掃描統(tǒng)計(jì)只是評(píng)估的空間分布的觀察，而不是數(shù)量的觀察。因此，它是一種特殊情況的變量窗口大小的掃描統(tǒng)計(jì)描述庫(kù)爾多夫（1997）。這是為了避免奇怪的非圓集群在邊境區(qū)的研究。報(bào)告預(yù)計(jì)數(shù)是根據(jù)全圓，所以地震/進(jìn)出口比率應(yīng)被視為一個(gè)下界的真實(shí)值時(shí)，圓外延伸區(qū)域空間研究。它使用一個(gè)圓形掃描窗口不斷變半徑達(dá)到最大由用戶指定。如果可選的網(wǎng)格文件提供，圓而為中心的坐標(biāo)指定的文件中。Few Cases Compared to Controls In a purely spatial analysis where there are few cases pared to controls, say less than 10 percent, the discrete Poisson model is a very good approximation to the Bernoulli model. The former can then be used also for 0/1 Bernoulli type data, and may be preferable as it has more options for various types of adjustments, including the ability to adjust for covariates specified in the case and population files. As an approximation for Bernoulli type data, the discrete Poisson model produces slightly conservative pvalues. 少數(shù)病例與對(duì)照組相比在一個(gè)純粹的空間分析，有少數(shù)病例與對(duì)照組相比，說(shuō)不到百分之10，離散泊松模型是一個(gè)很好的近似的伯努利模型。作為一個(gè)近似伯努利型數(shù)據(jù)，離散泊松模型產(chǎn)生稍微保守值。伯努利模型的運(yùn)行速度，使它的首選模式使用時(shí)，僅有2類。指數(shù)型模型的主要目的是為生存時(shí)間數(shù)據(jù)，但可用于任何數(shù)據(jù)在所有的意見(jiàn)是積極的。正常模型可用于連續(xù)數(shù)據(jù)，以積極和消極的價(jià)值觀。Normal versus Ordinal Model The normal model can be used for categorical data when there are very many categories. As such, it is sometimes a putationally faster alternative to the ordinal model. There is an important difference though. With the ordinal model, only the order of the observed values matters. For example, the results are the same for ordered values ‘1 – 2 – 3 – 4’ and ‘1 – 10 – 100 – 1000’. With the normal model, the results will be different, as they depend on the relative distance between the values used to define the categories. 正常與序模型正常模型可用于分類數(shù)據(jù)時(shí)，有非常多的類別。有一個(gè)重要的差異，雖然。例如，結(jié)果都是相同的命令值的1–2–3–4”和“1–10–100–1000 39。Discrete versus Homogenous Poisson Model Instead of using the homogeneous Poisson model, the data can be approximated by the discrete Poisson model by dividing the study area into many small pieces. For each piece, a single coordinates point is specified, the size of the piece is used to define the population at that location and the number of observations within that small piece of area is the number of cases in that location. As the number of pieces increases towards infinity, and hence, as their size decreases towards zero, the discrete Poisson model will be