Framework for prioritizing geospatial data processing tasks during extreme weather events

Abstract

In recent years, advanced geospatial technologies have been playing an increasingly important role in supporting critical decision makings in disaster response. One rising challenge to effectively use the growing volume of geospatial data sets is to rapidly process the data and to extract useful information. Unprocessed data are intangible and non-consumable, and often create the so-called “data-rich-but-information-poor” situation. To address this issue, this study proposed a Data Envelopment Analysis (DEA) based information salience framework to prioritize the sequence of the information processing tasks. The proposed model integrates the DEA efficiency score with a linguistic group decision process. For the input variables, computational complexity and intensity are selected to measure the difficulty in information processing. For the outputs, the performance of each processing tasks is evaluated based on the experts’ judgment on how the processing tasks satisfy the needs of decision makers. These needs are characterized by four classic disaster functions. A unique element of our proposed framework is that cone constraints are added to the DEA model based on the experts’ evaluation of the importance of the four disaster functions to model the dynamic information need. The proposed model was validated with a Hurricane Sandy based case study. The results indicate that the proposed framework is capable of prioritizing geospatial data processing tasks in a systematic manner and accelerating information extraction from disaster related geospatial data sets.