Hurricane Disaster Relief

Project Title:

Rethinking Representation in Discrete Spatial Modeling: Theoretical Developments and a Computational Study of Hurricane Disaster Relief

Funded by:

The National Science Foundation (BCS-0550330)

Project Overview:

Geographical representation insofar as how physical and human phenomena are constituted symbolically and stored digitally is an important concern for spatial modeling and decision making using geographic information systems (GIS). Prior research has shown that representation-related error substantially impacts planning and modeling scenarios in a variety of contexts. For example, when using discrete spatial models of network flow to efficiently distribute goods, route shipments and allocate services, aggregation is a key representation issue that adversely affects model outputs and therefore compromises strategic planning and analysis. Unfortunately, aggregation is central to many existing discrete spatial model structures, which suggests that they need to be reevaluated and new techniques should be developed. The research will design new spatial models that reduce prominent sources of representation-related error in critical decision making situations. The general approach is to explore whether existing spatial models can be rethought to take advantage of more detailed and disaggregate spatial representations of geographical phenomena. The context in which these models are tested and developed is hurricane disaster relief planning. In this research, a series of computational GIS-based simulation experiments will be formulated and executed where relief goods and services are to be distributed to affected locations in Florida. Simulations will be designed in conjunction with new spatial model development. The simulations will model the distribution of relief goods and services in scenarios that take into account the extent of hurricane damage, the availability of governmental/relief agency resources, and other situational characteristics. Performance of new spatial models will be assessed by comparing their outputs to results obtained using existing approaches.

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