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Michael P. Finn

Publications and source records attributed to Michael P. Finn.

24 records · Page 2Linked to original sources
USGS2006

User's Guide for MapIMG 2: Map Image Re-projection Software Package

BACKGROUND Scientists routinely accomplish small-scale geospatial modeling in the raster domain, using high-resolution datasets for large parts of continents and low-resolution to high-resolution datasets for the entire globe. Direct implementation of point-to-point transformation with appropriate functions yields the variety of projections available in commercial software packages, but implementation with data other than points requires specific adaptation of the transformation equations or prior preparation of the data to allow the transformation to succeed. It seems that some of these packages use the U.S. Geological Survey's (USGS) General Cartographic Transformation Package (GCTP) or similar point transformations without adaptation to the specific characteristics of raster data (Usery and others, 2003a). Usery and others (2003b) compiled and tabulated the accuracy of categorical areas in projected raster datasets of global extent. Based on the shortcomings identified in these studies, geographers and applications programmers at the USGS expanded and evolved a USGS software package, MapIMG, for raster map projection transformation (Finn and Trent, 2004). Daniel R. Steinwand of Science Applications International Corporation, National Center for Earth Resources Observation and Science, originally developed MapIMG for the USGS, basing it on GCTP. Through previous and continuing efforts at the USGS' National Geospatial Technical Operations Center, this program has been transformed from an application based on command line input into a software package based on a graphical user interface for Windows, Linux, and other UNIX machines.

Open-File Report↗
USGS2004

User's Guide for the MapImage Reprojection Software Package, Version 1.01

Scientists routinely accomplish small-scale geospatial modeling in the raster domain, using high-resolution datasets (such as 30-m data) for large parts of continents and low-resolution to high-resolution datasets for the entire globe. Recently, Usery and others (2003a) expanded on the previously limited empirical work with real geographic data by compiling and tabulating the accuracy of categorical areas in projected raster datasets of global extent. Geographers and applications programmers at the U.S. Geological Survey's (USGS) Mid-Continent Mapping Center (MCMC) undertook an effort to expand and evolve an internal USGS software package, MapImage, or mapimg, for raster map projection transformation (Usery and others, 2003a). Daniel R. Steinwand of Science Applications International Corporation, Earth Resources Observation Systems Data Center in Sioux Falls, S. Dak., originally developed mapimg for the USGS, basing it on the USGS's General Cartographic Transformation Package (GCTP). It operated as a command line program on the Unix operating system. Through efforts at MCMC, and in coordination with Mr. Steinwand, this program has been transformed from an application based on a command line into a software package based on a graphic user interface for Windows, Linux, and Unix machines. Usery and others (2003b) pointed out that many commercial software packages do not use exact projection equations and that even when exact projection equations are used, the software often results in error and sometimes does not complete the transformation for specific projections, at specific resampling resolutions, and for specific singularities. Direct implementation of point-to-point transformation with appropriate functions yields the variety of projections available in these software packages, but implementation with data other than points requires specific adaptation of the equations or prior preparation of the data to allow the transformation to succeed. Additional constraints apply to global raster data. It appears that some packages use the USGS's GCTP or similar point transformations without adaptation to the specific characteristics of raster data (Usery and others, 2003b). It is most common for programs to compute transformations of raster data in an inverse fashion. Such mapping can result in an erroneous position and replicate data or create pixels not in the original space. As Usery and others (2003a) indicated, mapimg performs a corresponding forward transformation to ensure the same location results from both methods. The primary benefit of this function is to mask cells outside the domain. MapImage 1.01 is now on the Web. You can download the User's Guide, source, and binaries from the following site: http://mcmcweb.er.usgs.gov/carto_research/projection/acc_proj_data.html

Open-File Report↗
USGS2004

A decision support system for map projections of small scale data

The use of commercial geographic information system software to process large raster datasets of terrain elevation, population, land cover, vegetation, soils, temperature, and rainfall requires both projection from spherical coordinates to plane coordinate systems and transformation from one plane system to another. Decision support systems deliver information resulting in knowledge that assists in policies, priorities, or processes. This paper presents an approach to handling the problems of raster dataset projection and transformation through the development of a Web-enabled decision support system to aid users of transformation processes with the selection of appropriate map projections based on data type, areal extent, location, and preservation properties.

Scientific Investigations Report↗
USGS2003

User's Guide for the Agricultural Non-Point Source (AGNPS) Pollution Model Data Generator

BACKGROUND Throughout this user guide, we refer to datasets that we used in conjunction with developing of this software for supporting cartographic research and producing the datasets to conduct research. However, this software can be used with these datasets or with more 'generic' versions of data of the appropriate type. For example, throughout the guide, we refer to national land cover data (NLCD) and digital elevation model (DEM) data from the U.S. Geological Survey (USGS) at a 30-m resolution, but any digital terrain model or land cover data at any appropriate resolution will produce results. Another key point to keep in mind is to use a consistent data resolution for all the datasets per model run. The U.S. Department of Agriculture (USDA) developed the Agricultural Nonpoint Source (AGNPS) pollution model of watershed hydrology in response to the complex problem of managing nonpoint sources of pollution. AGNPS simulates the behavior of runoff, sediment, and nutrient transport from watersheds that have agriculture as their prime use. The model operates on a cell basis and is a distributed parameter, event-based model. The model requires 22 input parameters. Output parameters are grouped primarily by hydrology, sediment, and chemical output (Young and others, 1995.) Elevation, land cover, and soil are the base data from which to extract the 22 input parameters required by the AGNPS. For automatic parameter extraction, follow the general process described in this guide of extraction from the geospatial data through the AGNPS Data Generator to generate input parameters required by the pollution model (Finn and others, 2002.)

Open-File Report↗