Geology topics

Jason L. Shapiro

Publications and source records attributed to Jason L. Shapiro.

5 recordsLinked to original sources

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↗

USGS2019

Resource Assessment Economic Filter (RAEF)—A graphical user interface supporting implementation of simple engineering mine cost analyses of quantitative mineral resource assessment simulations

Economic evaluations of undiscovered mineral resources provide important context in which to consider the results of quantitative mineral resource assessments. The U.S. Geological Survey economic analysis method uses a simple engineering cost model approach developed by the U.S. Bureau of Mines that applies mine and mill engineering cost equations to simulated undiscovered deposits. The important characteristics of these deposits are derived from Monte Carlo simulations that combine probabilistic estimates of undiscovered deposits that might occur in a study area and a grade-tonnage model defined for a specific deposit type. This report describes the Resource Assessment Economic Filter (RAEF), a graphical user interface (GUI) tool that applies a set of mine cost equations to the deposits under consideration. RAEF, which is written in the open-source statistical programming language R, is an easy-to-use tool to apply user-defined mine, mill, and study area parameters to simulated deposits. For a given deposit type, it estimates the undiscovered resources that might be economic to extract. In addition, RAEF provides a series of graphical, tabular, and statistical summaries that document the results of the economic filter analysis.

Techniques and Methods↗

USGS2019

User’s guide for Assessment Tract Aggregation GUI (ATA GUI)—A graphical user interface for the AggtEx.fn R script

The U.S. Geological Survey three-part method for mineral resource assessments estimates numbers of undiscovered mineral deposits as probability distributions in geologically defined regions termed “permissive tracts.” This report describes a graphical user interface (GUI) script developed in open-source statistical software (R) that aggregates estimated undiscovered deposits of a given type from two or more permissive tracts using the AggtEx.fn R script. The AggtEx.fn R script aggregates undiscovered deposit estimates assuming independence, total dependence, or some degree of correlation among aggregated areas, given a user-specified correlation matrix. The script outputs three sets of aggregated estimates based on those three assumptions. The GUI script described in this report, Assessment Tract Aggregation GUI (ATA GUI), provides an easy-to-use tool that supports implementation of the AggtEx.fn R script, installation of the R packages needed to run the application, and creation of a combined input file from individual files generated by the MapMark4GUI software. Users can also use EMINERS output information by creating a file of output values following the MapMark4GUI output file format. The probabilistic estimates of aggregated undiscovered deposits produced by ATA GUI can be used as input for MapMark4GUI to estimate contained resources for the aggregated tracts. MapMark4GUI uses Monte Carlo simulation to combine undiscovered deposit estimates with tonnage and grade models to simulate undiscovered mineral resources for a region of interest. This simulation includes the amounts of commodities and rock that could be present within a permissive tract. This report includes instructions on installing and running the ATA GUI script and describes the input and output files used and created during the aggregation process.

Techniques and Methods↗

USGS2018

User’s guide for MapMark4GUI—A graphical user interface for the MapMark4 R package

MapMark4GUI is an R graphical user interface (GUI) developed by the U.S. Geological Survey to support user implementation of the MapMark4 R statistical software package. MapMark4 was developed by the U.S. Geological Survey to implement probability calculations for simulating undiscovered mineral resources in quantitative mineral resource assessments. The GUI provides an easy-to-use tool to input data, run simulations, and format output results for the MapMark4 package. The GUI is written and accessed in the R statistical programming language. This user’s guide includes instructions on installing and running MapMark4GUI and descriptions of the statistical output processes, output files, and test data files.

Techniques and Methods↗

USGS2016

Building unified geospatial data for land-change modeling—A case study in the area of Richmond, Virginia

An effort to build a unified collection of geospatial data for use in land-change modeling (LCM) led to new insights into the requirements and challenges of building an LCM data infrastructure. A case study of data compilation and unification for the Richmond, Va., Metropolitan Statistical Area (MSA) delineated the problems of combining and unifying heterogeneous data from many independent localities such as counties and cities. The study also produced conclusions and recommendations for use by the national LCM community, emphasizing the critical need for simple, practical data standards and conventions for use by localities. This report contributes an uncopyrighted core glossary and a much needed operational definition of data unification.

Virginia↗