A scoping review of spatial cluster analysis techniques for point-event data

Abstract

Abstract. 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.

Detection of major climatic and environmental predictors of liver fluke exposure risk in Ireland using spatial cluster analysis

“…The use of geographical information systems (GIS) and geospatial tools and the incorporation of spatial statistics aiming to detect high-risk regions of a disease can contribute to a better understanding of the geographical pattern, transmission, and potential risk factors of fasciolosis (Weisent et al, 2011). Spatial cluster analysis belongs to this category of tools that try to detect hotspots (clusters) or regions presenting higher density of spatio-temporal disease occurrence (Fritz et al, 2013).…”

Cluster analysis of fasciolosis in dairy cow herds in Munster province of Ireland and detection of major climatic and environmental predictors of the exposure risk

“…The use of spatial statistics with geographical information systems (GIS) and other geospatial tools in order to detect high-risk regions can expand the current knowledge on the transmission, geographical pattern and potential risk factors of fasciolosis (Weisent et al, 2011). Spatial cluster analysis is based on such tools to identify hotspots or areas with an unusual increase in disease events grouped together in space and time (Fritz et al, 2013). There are only two studies that have used cluster detection techniques to identify highrisk regions for fasciolosis, i.e.…”

Spatial distribution of individuals with symptoms of depression in a periurban area in Lima: an example from Peru

“…Then, spatial clustering was assessed using SaTScan. The purely spatial (no temporal variable included) binomial model for the spatial scan statistic was selected to detect clustering of high and low prevalence (27). This iteratively tests for statistically significant clustering using a circular ‘scanning window’.…”