Pluralism in Spatial Information Systems

KUCERA, Gail Langran (kucera@islandnet.com), Mercator Systems Ltd., 2936 Phyllis Street, Victoria, BC V8N 1Z1, Canada

Key Words: pluralism, versioning, multiresolution, multitemporal

An increasingly data-rich environment requires more than a simple monolithic representation of information. The need to extend an information system to model a more pluralistic world can occur because the application domain itself are pluralistic or because the available data are pluralistic. The following are some examples.

1. The application needs to track change over time, meaning that multitemporal versions of features or attributes are required.

2. Multiple data sources are available, but no single source can be considered to be definitive. One approach is to maintain data from all sources concurrently within the database, and create on-demand reconciled views using application software.

3. Data at multiple resolutions are available, but a lower-resolution data source might contain features not available on a higher-resolution data source.

4. Users need the ability to drill down and up, changing not just the resolution, but also the content of the data being analyzed. Rapid performance could require roll-ups of generalized data, or data from a lower-resolution source.

The most common and simple way to manage pluralistic information is to manage a monolithic database and reconcile any pluralism as part of the update process. New information that differs from existing information is evaluated, and if "better," it replaces the "worse" information. The monolithic database holds a single "best map" compiled by integrating available information.

A number of techniques are available to model a more pluralistic world within the database. Examples include:

- Time-bracketed non-persistent features

- Multitemporal linked lists of persistent feature versions

- "Same-as" networks that link different versions of different portions of the same feature

- Source goodness hierarchies to describe the relative reliability of data sources

- Feature goodness hierarchies that facilitate drill-down and drill-up within a data warehouse

- Various roll-ups of pluralistic data to present an authorized monolithic view.

This paper will draw examples from three projects that, combined, use all the above techniques. The first project resulted in an operational system to manage 100-plus years of information on the crown lands that encompass more than 90 percent of British Columbia. The second project involves ongoing Research and Development funded by the U.S. Army Topographic Engineering Center, and will result in a pluralistic spatial data warehouse to manage multisource, multiresolution information with linked "same-as" feature networks to support spatial drill-down/up, and generation of a "best map" at a requested scale. The third is a spatial data warehouse for Canadian Forest Service inventory information and forest management criteria and indicators.