This study presents a GIS-based approach to efficiently assess sinkhole susceptibility based on their morphological and contextual attributes derived from GIS and imagery data. Using a 14-km2 karst area in Nixa, Missouri as the study area, we first applied a sequence of GIS operations to extract sinks (i.e., topographic depressions), from bare-ground digital terrain models. We then derived three types of sink attributes from various GIS and imagery data, including four morphological attributes related to sink size, shape, depth, and terrain ruggedness; three imagery attributes denoting the impervious surface percentage, vegetation growth condition, and seasonal water conditions of sinks; and seven contextual attributes related to land use, population density, neighborhood sink density, hydrological flow, groundwater yield, bedrock depth, and historical sinkhole records. Sinks were ranked by each of the 14 attributes and assigned corresponding susceptibility scores, then combined by specified weights. The results identified high-priority sinkholes for mitigation activities or for further field-based inspection. The proposed computerized approach for sinkhole susceptibility ranking can be effectively used as a first-stage sinkhole examination to maximize the use of limited resources for further comprehensive investigation.