Collaborative Research: Spatial Cluster Detection Based on Contiguity

合作研究：基于连续性的空间聚类检测

• 批准号: 1154316
• 负责人: Tony Grubesic
• 金额: $ 17.95万
• 依托单位: Drexel University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2012
• 资助国家: 美国
• 起止时间: 2012-04-01 至 2015-03-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1154316&HistoricalAwards=false
• 关键词: Collaborative; Research; Spatial; Cluster; Detection

The identification of spatial clusters is an important and critical task in many scientific fields. Areas which exhibit a raised incidence of some phenomenon (e.g. disease or crime) are often targeted for increased intervention efforts, such as additional public health safeguards, increased allocations of human resources, or modification to existing public policies to deter negative outcomes. However, the ability to precisely identify significant spatial clusters continues to be challenging. Problems associated with imperfections in spatial data, geographic scale, cluster shape and size, and temporal dynamics often co-mingle to create a somewhat chaotic environment for developing reliable and robust solution approaches. Therefore, while there is no single "best" spatial clustering approach for identifying areas of elevated risk, several techniques, including spatial scan statistics, remain popular and widely used in geography, epidemiology, and criminology for identifying hot spots. This project will develop cutting-edge mathematical and statistical approaches combined with exploratory spatial data analysis techniques to provide a more accurate and precise framework for identifying irregularly shaped spatial clusters for hot spot detection. Specifically, this research will develop more rigorous contiguity and relative contiguity-based spatial cluster detection approaches for identifying clusters with maximum statistical significance while quantitatively tracking their geographic structure. In addition, a suite of innovative diagnostics will be developed to better recognize errors of misidentification, such as missing high-risk units or including extra non-significant units in the detected clusters. The goal is to bring these developed methods to bear on the problem of identifying and assessing spatial clusters over a wide range of spatial scales and application areas.Building upon preliminary research, this team is poised to develop the next generation of spatial clustering approaches and make major advancements to the STEM fields of applied mathematics, operations research, epidemiology, and geographic information science. Further, the substantive components of this project will generate new empirical evidence to help inform local and regional public policy and public health issues regarding alcohol outlets and their relationship to violence and morbidity. Results of this project also support vulnerable populations and places that are socially and economically disenfranchised in two major metropolitan areas (Cincinnati, OH and Philadelphia, PA). Published research and participation in major international conferences, in combination with websites, forums, and sponsored activities hosted by both Drexel and ASU will enable effective dissemination of project results to a wide audience.

在许多科学领域，空间簇的识别是一项重要且至关重要的任务。 表现出某种现象（例如疾病或犯罪）发病率提高的领域通常是针对增加干预工作的目标，例如额外的公共卫生保障措施，增加人力资源的拨款或对现有公共政策的修改以阻止负面结果。 但是，精确识别重要的空间簇的能力仍然具有挑战性。 与空间数据，地理量表，群集形状和大小以及时间动力学相关的缺陷相关的问题通常会共同融合，以创建一个有些混乱的环境，以开发可靠且可靠的解决方案方法。 因此，尽管没有单一的“最佳”空间聚类方法来识别风险升高的领域，但包括空间扫描统计在内的几种技术仍然流行并且广泛用于地理，流行病学和犯罪学来识别热点。 该项目将开发最先进的数学和统计方法与探索性空间数据分析技术相结合，以提供更准确，更精确的框架，用于识别不规则形状的空间簇以进行热点检测。 具体而言，这项研究将开发出更严格的连续性和基于相对连续性的空间群集检测方法，以识别具有最大统计意义的簇，同时定量跟踪其地理结构。 此外，将开发一套创新的诊断套件，以更好地识别错误识别的错误，例如缺少高风险单元或在被检测的簇中包括额外的不显着单位。 The goal is to bring these developed methods to bear on the problem of identifying and assessing spatial clusters over a wide range of spatial scales and application areas.Building upon preliminary research, this team is poised to develop the next generation of spatial clustering approaches and make major advancements to the STEM fields of applied mathematics, operations research, epidemiology, and geographic information science. 此外，该项目的实质性组成部分将产生新的经验证据，以帮助告知当地和区域公共政策以及有关酒精渠道及其与暴力和发病率的关系的公共卫生问题。 该项目的结果还支持在两个主要都会区（俄亥俄州辛辛那提市和宾夕法尼亚州费城）在社会和经济上被剥夺社会和经济上剥夺特权的脆弱人群和地方。 发表研究和参与主要国际会议，结合Drexel和ASU主持的网站，论坛和赞助活动的结合，将使项目结果有效地传播给广泛的受众。