GEOLOGY-GIS DATA MODEL IMPROVEMENTS AT ARCGIS 9.2

Bedrock and surficial geologic maps and supporting information provide the foundation for studies of ecosystems, earth history, groundwater, geomorphology, soils, and environmental hazards such as fire history, landslides, and rockfall potential. Geologic maps describe the underlying physical conditions of many natural systems and are an integral component of the physical science inventories stipulated by the National Park Service (NPS) in its Natural Resources Inventory and Monitoring (I&M) Guideline. The NPS has identified GIS and digital cartographic products as fundamental resource management tools. There are few geologists employed at parks, thus these tools are particularly important to the National Park Service to aid resource managers in using geologic data for park management decisions.

Colorado State University, in partnership with the NPS, developed the NPS Geologic Resources Evaluation (GRE) Geology-GIS Geodatabase Data Model, which is now the NPS GRE Program standard for producing geologic-GIS data from paper maps and existing digital products as defined by I&M and GRE guidelines. The data model utilizes functionality inherent in the ESRI geodatabase format to maintain attributes and relationships present in the existing paper map or digital data. Recent changes to ArcGIS software have introduced new avenues for maintaining these map relationships and improving efficiency. Methods of improving the data model that incorporate this new functionality are currently being researched. In an effort to improve efficiency in maintaining the data model, the number of feature classes in the data model could be decreased by grouping similar feature classes. In addition, utilizing ArcGIS representations would help to standardize the appearance of geologic features by attaching the symbology to data model feature classes. Finally, new functionality in the geodatabase, such as multi-user capability and the elimination of a spatial domain requirement for feature datasets and topology, could lead to improvements in conversion and data capture processes.