Collaborative Research: Spatial Cluster Detection Based on Contiguity

空间集群的识别是许多科学领域的一项重要而关键的任务。某些现象(如疾病或犯罪)发病率上升的领域往往是加强干预努力的目标，例如增加公共卫生保障、增加人力资源分配或修改现有公共政策以阻止负面结果。然而，准确识别重大空间集群的能力仍然具有挑战性。与空间数据、地理规模、集群形状和大小以及时间动态相关的问题往往交织在一起，为开发可靠和健壮的解决办法创造了某种程度上的混乱环境。因此，虽然没有单一的“最佳”空间聚集方法来识别高危区域，但包括空间扫描统计在内的几种技术仍然很受欢迎，并在地理学、流行病学和犯罪学中被广泛用于识别热点。该项目将开发尖端的数学和统计方法，结合探索性空间数据分析技术，为确定不规则形状的空间集群以进行热点探测提供更准确和精确的框架。具体地说，这项研究将开发更严格的邻接性和基于相对邻接性的空间集群检测方法，以识别具有最大统计意义的集群，同时定量跟踪其地理结构。此外，还将开发一套创新的诊断方法，以更好地识别误识错误，如遗漏高风险单位或在检测到的集群中包括额外的不重要单位。目标是将这些开发的方法应用于在广泛的空间尺度和应用领域识别和评估空间集群的问题。在初步研究的基础上，该团队准备开发下一代空间集群方法，并在应用数学、运筹学、流行病学和地理信息科学等STEM领域取得重大进展。此外，该项目的实质性组成部分将产生新的经验性证据，以帮助了解当地和区域的公共政策和公共卫生问题，这些问题涉及饮酒渠道及其与暴力和发病率的关系。该项目的成果还支持两个主要大都市地区(俄亥俄州辛辛那提和宾夕法尼亚州费城)的弱势人口和被剥夺社会和经济权利的地方。出版的研究和参与重大国际会议，结合德雷克塞尔和亚利桑那州立大学主办的网站、论坛和赞助活动，将使项目成果能够有效地传播给广泛的受众。

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.