Date of Graduation


Document Type


Degree Type



Eberly College of Arts and Sciences


Geology and Geography

Committee Chair

Trevor M. Harris.

Committee Co-Chair

Gergory Elmes

Committee Member

Timothy Warner


As an alternative to traditional graphical intuitive approaches (GIA), a semi-automated modeling approach (SMA) can more efficiently identify linear routes by using powerful iterative and automated methods. In this research, two case studies were investigated to examine critical issues relating to the accuracy and effectiveness of raster-defined algorithmic approaches to linear route location. The results illustrate that different shortest-path algorithms do not necessarily result in markedly different linear routes. However, differing results can occur when using different neighboring-cell links in the cell-based route network construction. Cell-based algorithmic approaches in both Arc/Info and IDRISI software generate very similar results which are comparable to linear modeling with greater than eight neighboring-cell links. Given a specific shortest-path algorithm and its route searching technique, the use of a finer spatial resolution only results in a narrower and smoother route corridor. Importantly, cost surface models can be generated to represent differing cumulative environmental 'costs' or impacts in which different perceptions of environmental cost can be simulated and evaluated.;Three different simulation techniques comprising Ordered Weighted Combination models (OWC), Dynamic Decision Space (DDS), and Gateway-based approaches, were used to address problems associated with concurrent and dynamic changes in multi-objective decision space. These approaches provide efficient and flexible simulation capability within a dynamic and changing decision space. When aggregation data models were used within a Gateway approach the match of resulting routes between GIA and SMA analyses is close. The effectiveness of SMA is greatly limited when confronted by extensive linear and impermeable barriers or where data is sparse. Overall, achieving consensus on environmental cost surface generation and criteria selection is a prerequisite for a successful SMA outcome. It is concluded that SMA has several positive advantages that certainly complement a GIA in linear route siting and spatial decision-making.