Fritz Charles E, Schuurman Nadine, Robertson Colin, Lear Scott
Department of Geography, Faculty of Environment, Simon Fraser University, Burnaby, BC, Canada.
Geospat Health. 2013 May;7(2):183-98. doi: 10.4081/gh.2013.79.
Spatial cluster analysis is a uniquely interdisciplinary endeavour, and so it is important to communicate and disseminate ideas, innovations, best practices and challenges across practitioners, applied epidemiology researchers and spatial statisticians. In this research we conducted a scoping review to systematically search peer-reviewed journal databases for research that has employed spatial cluster analysis methods on individual-level, address location, or x and y coordinate derived data. To illustrate the thematic issues raised by our results, methods were tested using a dataset where known clusters existed. Point pattern methods, spatial clustering and cluster detection tests, and a locally weighted spatial regression model were most commonly used for individual-level, address location data (n = 29). The spatial scan statistic was the most popular method for address location data (n = 19). Six themes were identified relating to the application of spatial cluster analysis methods and subsequent analyses, which we recommend researchers to consider; exploratory analysis, visualization, spatial resolution, aetiology, scale and spatial weights. It is our intention that researchers seeking direction for using spatial cluster analysis methods, consider the caveats and strengths of each approach, but also explore the numerous other methods available for this type of analysis. Applied spatial epidemiology researchers and practitioners should give special consideration to applying multiple tests to a dataset. Future research should focus on developing frameworks for selecting appropriate methods and the corresponding spatial weighting schemes.
空间聚类分析是一项独特的跨学科工作，因此在从业者、应用流行病学研究人员和空间统计学家之间交流和传播想法、创新、最佳实践和挑战非常重要。在本研究中，我们进行了一项范围综述，以系统地在同行评审期刊数据库中搜索对基于个体层面、地址位置或x和y坐标派生数据使用空间聚类分析方法的研究。为了说明我们的结果所提出的主题问题，我们使用一个存在已知聚类的数据集对方法进行了测试。点模式方法、空间聚类和聚类检测测试以及局部加权空间回归模型最常用于个体层面的地址位置数据（n = 29）。空间扫描统计量是地址位置数据中最常用的方法（n = 19）。我们确定了与空间聚类分析方法的应用及后续分析相关的六个主题，建议研究人员予以考虑；探索性分析、可视化、空间分辨率、病因学、尺度和空间权重。我们希望寻求使用空间聚类分析方法指导的研究人员，既要考虑每种方法的注意事项和优势，也要探索可用于此类分析的众多其他方法。应用空间流行病学研究人员和从业者应特别考虑对数据集应用多种测试。未来的研究应侧重于开发选择合适方法和相应空间加权方案的框架。
