Cluster Detection Capabilities of the Average Nearest Neighbor Ratio and Ripley's K Function on Areal Data: an Empirical Assessment
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Spatial clustering detection methods are widely used in many fields of research including epidemiology, ecology, biology, physics, and sociology. In these fields, areal data is often of interest; such data may result from spatial aggregation (e.g. the number disease cases in a county) or may be inherent attributes of the areal unit as a whole (e.g. the habitat suitability of conserved land parcel). This study aims to assess the performance of two spatial clustering detection methods on areal data: the average nearest neighbor (ANN) ratio and Ripley's K function. These methods are designed for point process data, but their ease of implementation in GIS software and the lack of analogous methods for areal data have contributed to their use for areal data. Despite the popularity of applying these methods to areal data, little research has explored their properties in the areal data context. In this paper we conduct a simulation study to evaluate the performance of each method for areal data under different types of spatial dependence and different areal structures. The results shows that the empirical type I error rates are inflated for the ANN ratio and Ripley's K function, rendering the methods unreliable for areal data.
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