Simulation Algorithms for Spatial Pattern Recognition

描述（由申请人提供）： 该SBIR项目正在开发中性空间模型的规范、构建和模拟方法和软件，并在概率模式识别框架内应用这些中性模型。研究结果将使流行病学家、环境科学家和图像分析师能够更准确地识别空间数据中的模式，方法是消除由不切实际的零假设（如“完全空间随机性”）造成的假阳性偏见。该项目将实现5个目标： 1.进行需求分析，以指定要合并到软件中的中性模型和功能。 2.开发和测试软件原型，以评估所提出的模型的可行性。 3.提出中性模型的拓扑结构，并制定策略来生成它们，并进行敏感性分析，以调查隐含假设（即空间自相关或非均匀风险）和实现数量对测试结果的影响。 4.将中性模型纳入第一个商业软件包，允许在空间统计检验中使用用户指定的备择假设。 5.应用软件和方法来展示该方法及其对暴露和健康风险评估的独特益处。 该项目的可行性已在一期工程中得到论证。第二阶段项目将实现第三至第五个目标。这些技术、科学和商业创新将彻底改变我们识别、记录和评估空间模式相对于中性模型的概率的能力，中性模型结合了现实的局部、空间和多变量依赖关系。本提案中的中性模型和方法首次使评估聚类或边界分析结果对零假设规范的敏感性成为可能。

DESCRIPTION (provided by applicant): This SBIR project is developing methods and software for the specification, construction and simulation of neutral spatial models, and for applying these neutral models within the framework of probabilistic pattern recognition. Results will allow epidemiologists, environmental scientists and image analysts across a broad range of commercial disciplines to more accurately identify patterns in spatial data by removing the bias towards false positives that is caused by unrealistic null hypotheses such as "complete spatial randomness" (CSR). This project will accomplish 5 aims: 1. Conduct a requirements analysis to specify the neutral models and functionality to incorporate in the software. 2. Develop and test a software prototype to evaluate feasibility of the proposed models. 3. Propose a topology of neutral models and develop strategies to generate them and to conduct sensitivity analysis for investigating the impact of implicit assumptions (i.e. spatial autocorrelation or non-uniform risk) and number of realizations on test results. 4. Incorporate the neutral models in the first commercially established software package that allows for user-specified alternate hypothesis in spatial statistical tests. 5. Apply the software and methods to demonstrate the approach and its unique benefits for exposure and health risk assessment. Feasibility of this project was demonstrated in the Phase I. This Phase II project will accomplish aims three through five. These technologic, scientific and commercial innovations will revolutionize our ability to identify, document and assess the probability of spatial patterns relative to neutral models that incorporate realistic local, spatial and multivariate dependencies. The neutral models and methods in this proposal make possible, for the first time ever, evaluation of the sensitivity of the results of cluster or boundary analyses to specification of the null hypothesis.