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（例如疾病或犯罪）发生率较高的地区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消极后果。然而，精确识别重要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的不完善相关的问题经常混合在一起，为开发可靠和健壮的解决方案方法创造了一个有点混乱的环境。因此，虽然没有单一的“最佳”空间聚类方法来识别高风险区域，但几种技术，包括空间扫描统计，仍然流行并广泛用于地理，流行病学和犯罪学中识别热点。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，为识别不规则形状的空间集群提供更准确和精确的框架，用于热点检测。具体而言，本研究将开发更严格的基于相邻性和相对相邻性的空间聚类检测方法，以识别具有最大统计显著性的聚类，同时定量跟踪其地理结构。此外，将开发一套创新的诊断方法，以更好地识别错误识别，例如遗漏高风险单位或在检测到的集群中包含额外的非重要单位。目标是将这些已开发的方法用于在广泛的空间尺度和应用领域中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聚类方法，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证据，以帮助了解有关酒类销售点及其与暴力和发病率的关系的地方和区域公共政策和公共卫生问题。该项目的成果还支持了两个主要大都市地区（俄亥俄州辛辛那提市和宾夕法尼亚州费城）社会和经济上被剥夺权利的弱势群体和地区。发表的研究成果和参与的主要国际会议，结合网站，论坛，并由德雷克塞尔和亚利桑那州立大学主办的赞助活动，将使项目成果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.