Increasing recognition of the extent and speed of habitat fragmentation and loss, particularly in the urban fringe, is driving the need to qualitatively and quantitatively analyze regional landscape structure for decision support in land use planning and environmental policy implementation. In this thesis, a framework for addressing such needs is proposed by introducing an extension to the spatial data models currently in use for geographic spatial analysis. Existing approaches from the Geographic Information Systems (GIS) and Computer Aided Drawing (CAD) literature are reviewed and found wanting in the kinds of spatial analysis tasks required in this problem domain. A methodology is proposed to address a specific need for the rational design of a landscape scale conservation network. In this approach the problem is framed as a multi-objective combinatorial optimization problem. A prototype of this methodology is developed which serves as both a demonstration application of the optimization techniques and an implementation of parts of the proposed spatial data model on a real world problem with real world data. Specifically, a Non-dominated Sorting Genetic Algorithm (NSGA) is applied to the structured data to generate a set of Pareto-optimal solutions where each solution represents a different configuration of features on the landscape and the objective functions consist of metrics derived from principles of design suggested in the landscape ecology literature. A key theme in the work is translating between representations. Data is transformed from the attributed planar graphs of the source data, to these graphs' attributed dual (adjacency) graph, to an indexed string (character vector/hyperedge) and back to an attributed planar graph. Each representation is the most natural or useful for a particular stage of analysis. For example, the current configuration of ecological and social features is coded as colourings on the vertices of the dual graph of the source data which are available as planar graphs that cartographically represent area features. The prototype implementation of this framework was created and tested on a set of small, medium and large sized input data. The performance of the framework and its implementation are evaluated.