title: The Volatility of Data Space: Topology Oriented Sensitivity Analysis.
abstract: Despite the difference among specific methods, existing Sensitivity Analysis (SA) technologies are all value-based, that is, the uncertainties in the model input and output are quantified as changes of values. This paradigm provides only limited insight into the nature of models and the modeled systems. In addition to the value of data, a potentially richer information about the model lies in the topological difference between pre-model data space and post-model data space. This paper introduces an innovative SA method called Topology Oriented Sensitivity Analysis, which defines sensitivity as the volatility of data space. It extends SA into a deeper level that lies in the topology of data.
authors.affiliation: Department of Construction Science, Texas A&M University, Francis Hall 334, TAMU, College Station, TX 77840, United States of America.
authors.forename: Jing
authors.initials: J
authors.lastname: Du
authors.affiliation: Department of Geography, Michigan State University, 673 Auditorium Rd, Room 121, East Lansing, MI 48824, United States of America.
authors.forename: Arika
authors.initials: A
authors.lastname: Ligmann-Zielinska
mesh_terms: D000465:Algorithms; D003198:Computer Simulation; D003627:Data Interpretation, Statistical; D008962:Models, Theoretical; D062206:Spatial Analysis