UCGIS Research Areas
& Uncertainty in Spatial Data
Modified according to the discussion in the Uncertainty group (firstname.lastname@example.org)
After browsing through the UCGIS research priority nominations, we noticed
that there is a common concern among many of the member universities about
the need for more research in the area of errors and uncertainty in spatial
databases. We (at U.W.-Madison) felt that there are three basic sub-research-
areas on errors and uncertainty in spatial databases and attempted to group
the nominations on errors and uncertainty based on the three subareas. The
purpose of this effort is to prompt discussion among the nominators and to
facilitate any possible future collaborations.
We are not sure that we have put your nominations in the proper groups.
We would appreciate your comments on the designation of the groups and the
assignments of your nominations. Any other comments are also welcome.
We have also created an email list for the purpose of discussion.
Please send your comments to
- Conceptual Models for Representing Spatial Data
- Integrating Spatial Data Across Scales
- Error Propagation Through Models
In addition to the instrument (measurement) error, further error can
be introduced during the data collection process. This is due in part to
the inadequacy of the conceptual model for representing spatial
data. Efforts involve developing new models and/or extensions to existing
models for adequately capturing and representing spatial data to minimize
the discretization. The new models and/or extionsions should include adding
variance estimates as layers, to attributes in tables, to entire object
classes, to regions, and to entire data sets; adding variograms, correlograms,
and other descriptors of joint probabilities; adding misclassification
Oak Ridge Nat
Lab | OH
St U |
WI Madison | U N C
Data in spatial data bases come from a variety of sources (remote
sensing, terrain analysis, digitization of existing paper maps, etc.)
at multiple scales. These data have undergone different degrees of
generalization at both spatial and attributes levels. They may or may
not represent the scale at which natural processes occur. Thus
integration of these diverse data sets in the environmental analysis
process can introduce errors due to their scale incompatibility. A clear
conceptual framework defining the detailed relationships between
generalization and data quality is needed to guide the development of
methodologies (de)generalizing datasets to compatible scales for the
purpose of analysis.
WI Madison |
MI St U | U
CA Santa Barbara |
Data from different sources contain inherent errors which are
propagated when these layers are combined in a modeling process.
Error propagation should be an integral part of any environmental
modeling activity. This can ensure that results from environmental
analysis can be interpreted with a proper understanding of
uncertainty. Impact of uncertainty on decision making needs to be
assessed as well.
Diego St U |
U MN |