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Multi-criteria evaluation (MCE) procedures are widely used in raster-based geographic information systems (GIS) to perform a variety of land use siting applications. Many of the criteria used in an MCE analysis are based on spatial relationships or situation characteristics. Situation factors measure the accessibility that each raster cell is to resources or land uses that generate spatial externalities for the activity being sited. This accessibility can be measured either in terms of distance to the nearest target cell containing resources or the overall level of resource availability as measured by a spatial interaction model. This paper examines the spatial structure of these situation factors to identify the set of critical target cells for which distance estimates are most sensitive. Critical target cells are especially important in the case of positive externalities in which an activity would be inclined to locate near these cells to use or consume the resources there. Critical target cells are useful for evaluating the utility of the final site selection with respect to resource/activity ratios.

Mathematical algorithms for placing labels upon maps have been used for the past thirty years. This paper presents a multi-criteria model for point feature annotation that examines the sometimes conflicting issues in the point labelling process. Based on a maximum labelling model, the primary goal is to place the maximum number of labels on the map, although secondary goals reflect other considerations such as point size, position preferences, and feature importance. By differentially weighting these separate factors, emphasis could be placed on each consideration in turn allowing the cartographer to assess the implications of each factor in the point annotation process. An application of this model to mountain peaks in Grand Teton National Park illustrates how it can assist a cartographer as a preliminary step in assessing the difficulty encountered in labelling point symbols as well as providing the cartographer with a choice of alternative scenarios upon which to base the final placement.