Global and local spatial analysis for applied goegraphical research

对于加拿大人极为关切的许多问题，采取行动需要作出区位决定：检测和监测环境危害，以及有效应对流行病暴发，这只是大多数关键因素位于地理空间的几个例子。目前，管理决策得到了量化模型的支持；这些模型是潜在的可取工具，例如可以将疾病发生率与健康危害联系起来，从而提供了在不久的将来哪里最需要卫生保健服务的现实图景。不幸的是，目前的模型往往是不确定的，因此不可靠。在最好的情况下，不可靠的模型为决策者提供了他们需要管理的因素的现实但模糊的图景，导致无效的决策；在最坏的情况下，图景如此模糊以至于令人困惑，可能导致管理决策不仅无效，而且有害。这种不确定性源于地理因素的两个特性：物体在空间上变化不均匀，近物体比远物体更相似。这两个属性经常同时出现，而每个现有的建模方法只能解决其中的一个；因此，每个方法只能部分地清除图像的模糊性。更糟糕的是：每一种方法在试图解决一个不确定性来源时，都会加剧另一个来源，从而增加整体的不确定性。

For many issues that greatly concern Canadians, action requires locational decisions: detection and monitoring of environmental hazards, and efficient response to epidemic outbreaks, are but a few examples where most key factors are located in geographical space. Presently, management decisions are supported by quantitative models; these models are potentially desirable tools, which can link, for example, disease incidence to health hazards, thus providing a realistic picture of where health care services will be most needed in the near future. Unfortunately, current models are often uncertain, therefore unreliable. In the best cases, unreliable models provide decision makers with a realistic, but blurry picture of the factors they need to manage, leading to ineffective decisions; in the worst cases the picture is so blurry that it is confusing, and may lead to management decisions that are not just ineffective, but harmful. The uncertainty stems from two properties of geographical factors: things vary unevenly in space, and near things are more similar than distant things. These two properties often occur simultaneously, whereas each existing modeling method can only address one of them; therefore, each method only partially clears the blurriness of a picture. Worse: each method, in trying to address one source of uncertainty, can aggravate the other, increasing the overall uncertainty.