Collaborative Research: Spatial Cluster Detection Based on Contiguity
CCI Project Lead: Tony Grubesic
Project Leaders: Alan Murray (CCI), Tony Grubesic (CCI), Loni Tabb
About the Project
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.