Web-based multi-criteria evaluation of spatial trade-offs between environmental and economic implication from hydraulic fracturing in a shale gas region in Ohio

Planning of shale gas infrastructure and drilling sites for hydraulic fracturing has important spatial implications. The evaluation of conflicting and competing objectives requires an explicit consideration of multiple criteria as they have important environmental and economic implications. This study presents a web-based multi-criteria spatial decision support system (SDSS) prototype with a flexible and user-friendly interface that could provide educational or decision-making capabilities with respect to hydraulic fracturing site-selection in eastern Ohio. One of the main features of this spatial decision support system is to emphasize potential trade-offs between important factors of environmental and economic implications from hydraulic fracturing activities using a weighted linear combination (WLC) method. WLC is a simple approach that integrates users' preferences into an overall assessment and offers a rationale for trade-offs between decision criteria and objectives. In the prototype, the GIS-enabled analytical components allow spontaneous visualization of available alternatives on maps which provide value-added features for decision support processes and derivation of final decision maps. The SDSS prototype exhibits a straightforward decision-making procedure with easy-to-use web interface and facilitates non-expert participation capabilities. It comprises of a mapping module, decision-making tool, group decision data statistics, and social media sharing tools. The system architecture combines a variety of closely related components using Silverlight, ArcGIS API for Silverlight, ArcGIS Server, and ArcSDE for SQL Server software. During the decision-making process, users are guided through a logical flow of successively presented forms and standardized criteria maps to generate visualization of trade-off scenarios and alternative solutions tailored to their personal preferences. Finally, the results and the preferences from all users are graphed for visualization and subsequent decision-making making.