Uncertainty in Spatial Data: Identification, Visualization and Utilization

描述（由申请人提供）：本提案是对PA-11-238《空间不确定性：数据、建模和通信》（R 01）的回应。我们的研究重点是记录，可视化和利用空间分析中的数据错误和不确定性信息。要素时 在进行空间聚集的过程中，没有记录通过这一过程引入的腐败。数据使用者并不知道给定数据集的误差和不确定性的大小。地理编码的个体受访者的健康结果通常需要在地理上或分类上进行汇总，以便在发布指数时保护隐私，例如，导出癌症率。正确解释健康结果的邻里水平的特点，需要的知识，以及利用个人的地理分布区域内的单位，再加上这些地理分布之间的区域协会。另一方面，随着数据质量信息变得越来越容易获得，现有的映射工具不能充分地包括数据质量信息。此外，数据用户往往忽视数据错误和不确定性信息，将空间数据和相关地图视为没有错误和不确定性。因此，在不考虑数据质量的情况下执行诸如地理聚类检测的分析。该建议针对这些特定的数据质量问题，具体目标如下：1）制定指数，以量化汇总误差的影响。我们将解决两个方面：分布区域内的单位，属性错误的影响，通过空间聚集的地理编码的个人。2)开发各种方法和工具，以直观显示抽样和空间汇总产生的属性误差。我们将加强我们目前的GIS数据质量可视化工具，修改现有的可视化框架，并引入工具，以支持新的图例设计和地图分类方法。3)引入空间统计方法，将误差和不确定性信息纳入全局和局部空间模式检测的分析中。我们将评估现有方法的可靠性，并提出新的方法来解释抽样，规格和测量误差。我们将纳入通过实现目标1而开发的聚合误差度量。忽视空间数据中的错误不利于制定有效的政策和作出正确的决定。我们建议的工作将加强未来的数据收集和处理工作，使用户能够考虑不同类型的错误信息，提高空间模式检测的可靠性，通过纳入数据质量信息，并将不确定性信息转化为地图和数据质量信息传达给用户。结果具有普遍适用性。

DESCRIPTION (provided by applicant): This proposal is a response to PA-11-238, Spatial Uncertainty: Data, Modeling and Communication (R01). Our research focuses on documenting, visualizing and utilizing data error and uncertainty information in spatial analysis. When features undergo spatial aggregation, corruptions introduced through the process are not documented. Data users are not aware of the magnitude of error in and uncertainty accompanying a given dataset. Health outcomes of geocoded individual respondents often require aggregation, either geographically or categorically, in order to preserve privacy when publishing indices, say, derived cancer rates. Properly explaining health outcomes by neighborhood-level characteristics requires knowledge as well as a utilization of the geographic distribution of individuals within areal units coupled with areal associations among these geographic distributions. On the other hand, as data quality information is becoming more readily available, existing mapping tools fail to sufficiently include data quality information. Also, data users often ignore data error and uncertainty information, treating spatial data and associated maps as error- and uncertainty-free. Thus, analyses, such as geographic cluster detection, are performed without considering the quality of data. This proposal addresses these particular data quality issues with the following specific aims: 1) formulate indices to quantify impacts of aggregation error. We would address two aspects: distributions of geocoded individuals within areal units, and impacts of attribute errors through spatial aggregation. 2) develop methods and tools to visualize attribute errors arising from sampling and spatial aggregation. We would enhance our current data quality visualization tools for a GIS, modify existing visualization frameworks, and introduce tools to support new legend designs and map classification methods. 3) introduce spatial statistical methods to incorporate error and uncertainty information into the analyses of global and local spatial pattern detection. We would evaluate the reliability of existing methods, and propose new methods to account for sampling, specification, and measurement error. We would incorporate the aggregation error measures developed through achieving our Aim 1. Ignoring error in spatial data is detrimental to the formulation of effective policies and the making of sound decisions. Our proposed work would enhance future data gathering and processing effort, enable users to consider different types of error information, improve the reliability of spatial pattern detection by incorporating data quality information, and translate uncertainty information into maps and communicate data quality information to users. Results have very general applicability.