Uncertainty in Spatial Data: Identification, Visualization and Utilization

空间数据的不确定性：识别、可视化和利用

• 批准号: 8916168
• 负责人: Daniel A Griffith
• 金额: $ 27.19万
• 依托单位: UNIVERSITY OF TEXAS DALLAS
• 依托单位国家: 美国
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-09-01 至 2017-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8916168
• 关键词: Accounting; Address; Characteristics; Classification; Communication; Coupled; Data; Data Quality; Data Set; Detection; Drug Formulations; Ecological Bias; Environment; Epidemiology; Equipment and supply inventories; Future; Geographic Distribution; Geographic Information Systems; Health; Imagery; Individual; Knowledge; Location; Malignant Neoplasms; Maps; Measurement; Measures; Methods; Modeling; Neighborhoods; Outcome; Pattern; Policy Making; Privacy; Process; Publishing; Reader; Research; Resolution; Respondent; Sampling; Socioeconomic Status; Statistical Methods; Techniques; Translating; Uncertainty; Visual; Work; data modeling; design; improved; indexing; population health; response; sound; tool

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.

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(R01)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误差和不确定性信息。当要素 在进行空间聚合时，通过该过程引入的腐败没有记录在案。数据用户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绝对上进行汇总，以便在发布指数时保护隐私，例如推导出的癌症比率。通过邻里层面的特征正确解释健康结果需要了解和利用区域单位内个人的地理分布，以及这些地理分布之间的区域关联。另一方面，随着数据质量信息变得更加容易获得，现有的绘图工具不能充分包括数据质量信息。此外，数据用户经常忽略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集群检测之类的分析。这项建议针对这些特定的数据质量问题，具体目的如下：1)制定指数来量化聚合误差的影响。我们将解决两个方面：地理编码的个体在区域单元内的分布，以及属性误差通过空间聚集的影响。2)开发可视化采样和空间聚集产生的属性误差的方法和工具。我们将增强现有的用于地理信息系统的数据质量可视化工具，修改现有的可视化框架，并引入支持新的图例设计和地图分类方法的工具。3)引入空间统计方法，将误差和不确定性信息融入到全局和局部空间模式检测分析中。我们将评估现有方法的可靠性，并提出新的方法来说明抽样、规格和测量误差。我们将纳入通过实现我们的目标1.忽视空间数据中的误差不利于制定有效的政策和作出正确的决策。我们提出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，通过纳入数据质量信息来提高空间模式检测的可靠性，并将不确定性信息转换为地图并向用户传达数据质量信息。结果具有非常普遍的适用性。