Multi-Objective Models that Address Spatial Uncertainty
58th Annual North American Meetings of the Regional Science Association International
November 9-12, 2011
Session: Location and Spatial Modeling F: Friday Nov 11
Ran Wei, Arizona State University
Alan Murray, Arizona State University
Methods to address geographic data uncertainty are critically important. Spatial analytical approaches are generally limited in their ability to recognize and account for most aspects of uncertainty in spatial data. We propose two multi-objective models that account for spatial uncertainty in a dispersion optimization. This approach enables one to consider model results in terms of relative certainty. Empirical results demonstrate the applicability and usefulness of the developed models.