Abstract
AbstractInfrastructure interdependency analysis involves the synthesis of numerous datasets to arrive at an understanding of the pertinent connections within a complex, interrelated system. Geospatial data is beneficial for analyzing infrastructure interdependencies because it can be used to evaluate and visualize the myriad of component relationships that constitute critical infrastructures and key resources. However, various limitations in geospatial and nonspatial data have restricted the full utilization of Geographic information system and other visualization technologies in such analyses. The data requirements for analyzing interdependencies include ready access to a wide variety of critical infrastructure/key resource data that are geospatially validated and linked with nonspatial data to provide rich attributes that can be queried and visualized. Analysts have struggled long to find complete and accurate geospatial and nonspatial data. Their struggle is complicated further by the use of multiple data sources that differ in levels of completeness and accuracy. Analysis of interdependencies requires a high level of data validation and completeness, which can be difficult to discern from the currently available geospatial datasets. This technical article identifies key geospatial data pitfalls including accuracy and completeness, and offers solutions for improving and enhancing existing geospatial data for interdependency analysis.
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