Published Article/Report
- Myers, W. L., M. McKenney-Easterling, K. Hychka, B. Griscom, J. A. Bishop, A. Bayard, G. L. Rocco, R. P. Brooks, G. Constantz, G. P. Patil and C. Taillie. 2006. Contextual clustering for configuring collaborative conservation of watersheds in the Mid-Atlantic Highlands. Environmental and Ecological Statistics 13 (4): 391-407.
The environmental purpose is to characterize watersheds in a region regarding vulnerability and resiliency relative to present and potential degradation of water quality due to human impact based on available spatial information and multidisciplinary expertise. Available information is of six general types as (1) physical and topographic conformation, (2) soil factors, (3) climatic factors, (4) hydrologic characteristics, (5) land-cover/land-use, and (6) prior records of sampling at selected locations for water quality and biological indicators. The strategy is first to develop cluster-based classes of watersheds that are expected to have similar responses to anthropogenic stressors, without using indicators of landscape condition that are directly influenced by local human activity. Watersheds in these classes can then be analyzed for degree of human influence as indicated by land-cover/land-use demographics. More sparse data on water quality and biological indicators at stream sampling locations provide a basis for determining the degradation response to human-induced stressors in each class along with potential for remediation. Focus in this paper is on the first task of cluster-based classification.
Statistical adaptation comes in combining empirical objectivity of clustering with interdisciplinary environmental expertise, such that the trajectory of investigation arises from team expertise while the formulation is shaped statistically. Expertise enters initially in recognizing subsets of available descriptors that characterize different aspects of the watershed context needing to be explored separately rather than being completely confounded. Reduction of redundancy among available descriptors and removal of outliers are preliminary concerns. Clustering then proceeds through a series of phases using the sets of variables individually and in selected combinations. Contingency of composite clustering relative to separately clustered sets is examined via special cross tabulations in order to elucidate interactions between sets of variables. The spatial nature of the investigation contributes the major contextual capability for exercising team expertise through visualization using geographic information systems (GIS) that enhances and integrates insights from clustering, particularly with regard to spatial distribution of cluster membership.