RE-THINKING BEST PRACTICES IN CARTOGRAPHIC DATA CAPTURE AND DATA MODELING
B.P. Buttenfield1, C. Frye2
1 - University of Colorado, Geography, Boulder CO USA
2 - ESRI, Redlands, California USA
Data capture of base cartographic features continues as a major activity in national mapping agencies, regional and local governments, and field science offices. Data modeling and data processing to create map products at a single scale and for a single purpose is both costly and labor-intensive. As a consequence, a goal in most organizations is to capture data on Sherman and Tobler’s (1957) Multiple Use convention, which introduces a degree of redundancy by creating multiple representations that serve multiple purposes. A single data representation cannot serve all audiences, or all types of use, or all mapping scales. The “general purpose” paper-based topographic map legacy is widely recognized. Redundancy creates problems for data updates; and linking multiple representations is often complicated. Managers hope to achieve a balance between flexibility and parsimony. Capturing data for a range of mapping scales and selection of mapping or other purposes must be informed by product requirements and the production or workflow requirements.
We argue for specifying parameters of use to “inform” data capture and modeling. To the extent that data is generalized for particular purposes that are loosely or tightly coupled to a particular resolution and mapping scale, specifying intended use in advance of data capture will likely reduce or ideally minimize subsequent processing. Robust metrics establishing data’s suitability for a given cartographic purpose have not yet been formalized, nor published in the literature. Captured cartographic data can’t be extended across an infinite range of mapping scales without applying some form of data modeling, changing attributes, feature geometry, or both. Compiling data is more expensive than modeling data, for any product. Thus the problem of multiple use is intensified for production of any database intended for multiple mapping scales and purposes.
Management scientists refer to the Iron Triangle whose vertices are resources, funding and time. We show how each Iron Triangle component impacts cartographic data modeling. We present examples from recent work practice to demonstrate where “informed” data capture can reduce or perhaps obviate the need for intensive data modeling. We present guidelines for best practices, including decisions about when new data capture is the preferred practice to re-align existing data in preparation for new map products. Finally we present implications for data sharing that incorporate best practice reports that delineate fitness for use, which can inform data partners about how to model shared data to a different scale or purpose.