GIS and Spatial Statistical Methods for Determining Sinkhole Potential

Urban development is rapidly occurring in the karstic terrain of the Frederick Valley, Maryland, USA. Sinkholes are an inherent feature of the landscape; they present hazards and engineering challenges to the development of residential, commercial and industrial infrastructure. Here, a new approach is presented for assessing susceptibility of sinkhole occurrence. A sinkhole dataset and geologic map was obtained from the Maryland Geological Survey (Brezinski, 2004). Clustering and kernel density analyses were performed on the sinkhole location data to assess interdependence of sinkhole locations. External factors relating to sinkhole development were examined separately using Geographically Weighted Regression (GWR) and ordinary linear regression. These factors included geologic structure (folds axes and faults), rock type, proximity to impervious surfaces and surface drainage patterns. In order to determine the relative likelihood of sinkhole formation, the results of the cluster analysis, density analysis, and regression analyses resulted in map layers that could be weighted and overlain to create a sinkhole susceptibility map. Cluster analysis is important for analyzing sinkhole development for two reasons: 1) human activities tend to fill an empty space by creating a more uniform pattern than random, 2) the eye tends to “se