An Optimization Approach to Constraint-Based Generalization in a Commodity GIS Framework

The task of generalization of existing spatial data for cartographic production can be expressed as optimizing both the amount of information to be presented, and the legibility/usability of the final map, while conserving data accuracy, geographic characteristics, and aesthetical quality. This paper provides an overview of a research project underway presently at ESRI to implement an optimization approach to constraint-based generalization within a commodity GIS (ArcGIS). In this approach, a set of rules are defined, one for each constraint. Each rule contains a satisfaction function, measuring the degree of violation of the constraint, and one or more actions which should improve the situation if the constraint is violated. An Optimizer kernel then has the responsibility of evaluating local and global satisfaction, and applying actions to appropriate features to improve the situation. In real generalization scenarios, it is often not possible to avoid some violation of constraints, and the goal of the Optimizer is to maximize the overall satisfaction.