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USGS2024

Semi-automated methods to develop a unified geographic information system dataset

Geospatial data describing the topography, natural features, human-built features, and land uses of a particular area or region can come from independent data providers and, therefore, vary in format, data encoding, and geographic coverage. Because of the complexity of the processes and procedures required for unifying these heterogeneous data into a dataset with consistent format, encoding, and coverage, fully automated procedures for data unification do not exist. However, a combination of manual and automated procedures—semi-automated methods—can substantially reduce the time required for data unification while improving accuracy. This report presents three semi-automated data-unification methods in detail. Although these methods are not new in principle, their details are the result of original development work, and they serve as examples that can be reused, adapted, or generalized to provide head starts to future data-unification projects. The format of this report can be used and refined to encourage the publication of future reports and more widespread sharing of semi-automated methods.

Techniques and Methods↗
USGS2009

Estimating Prediction Uncertainty from Geographical Information System Raster Processing: A User's Manual for the Raster Error Propagation Tool (REPTool)

The U.S. Geological Survey Raster Error Propagation Tool (REPTool) is a custom tool for use with the Environmental System Research Institute (ESRI) ArcGIS Desktop application to estimate error propagation and prediction uncertainty in raster processing operations and geospatial modeling. REPTool is designed to introduce concepts of error and uncertainty in geospatial data and modeling and provide users of ArcGIS Desktop a geoprocessing tool and methodology to consider how error affects geospatial model output. Similar to other geoprocessing tools available in ArcGIS Desktop, REPTool can be run from a dialog window, from the ArcMap command line, or from a Python script. REPTool consists of public-domain, Python-based packages that implement Latin Hypercube Sampling within a probabilistic framework to track error propagation in geospatial models and quantitatively estimate the uncertainty of the model output. Users may specify error for each input raster or model coefficient represented in the geospatial model. The error for the input rasters may be specified as either spatially invariant or spatially variable across the spatial domain. Users may specify model output as a distribution of uncertainty for each raster cell. REPTool uses the Relative Variance Contribution method to quantify the relative error contribution from the two primary components in the geospatial model - errors in the model input data and coefficients of the model variables. REPTool is appropriate for many types of geospatial processing operations, modeling applications, and related research questions, including applications that consider spatially invariant or spatially variable error in geospatial data.

Techniques and Methods↗
USGS1981

Some thoughts on cartographic and geographic information systems for the 1980's

The U.S. Geological Survey is adopting computer techniques to meet the expanding need for cartographic base category data. Digital methods are becoming increasingly important in the mapmaking process, and the demand is growing for physical, social, and economic data. Recognizing these emerging needs, the National Mapping Division began, several years ago, an active program to develop advanced digital methods to support cartographic and geographic data processing. An integrated digital cartographic database would meet the anticipated needs. Such a database would contain data from various sources, and could provide a variety of standard and customized map and digital data file products. This cartographic database soon will be technologically feasible. The present trends in the economics of cartographic and geographic data handling and the growing needs for integrated physical, social, and economic data make such a database virtually mandatory.

Pecora VII Symposium↗