A Decision Support System for Map 

Projections of Small Scale Data 

 

 

Scientific Investigations Report 2004-5297 

U.S. Department of the Interior 

U.S. Geological Survey
A Decision Support System for Map Projections of Small Scale Data 

Michael P. Finn, E. Lynn Usery, Stephan T. Posch1

, and Jeong Chang Seong 

 

U.S. Geological Survey 

Rolla, Missouri 

Northern Michigan University 

Marquette, Michigan 

Phone: 573-308-3931; Fax: 573-308-3652 

mfinn@usgs.gov

KEY WORDS Decision Support, Transformation, Map Projection, Cartography, 

GIS 

ABSTRACT 

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. 

INTRODUCTION

Many models of global natural and anthropogenic change depend on data 

formatted as raster cells (or matrices of pixel values, or grid cells). 

Research has shown that errors from mathematical projection are of such 

magnitude that they may jeopardize model results (Steinwand and others 

1995; Tobler and others 1995; Usery and others 2003a). There is little 

guidance for users of Geographic Information System (GIS) and other 

software packages that offer the capability to project these regional 

and global raster datasets (Usery et al, 2003b). Since numerous 

projections exist within each software package (each with its own set of 

parameters), and since there is an ability to customize each projection, 

many GIS users may not have the knowledge to select a projection 

appropriate to the area of the world, data type, or particular problem 

being examined. Most may not know if there are inherent errors in 

commercial software packages for projection transformation, if an error 

 

1

 Now with Dynetics, Inc., Huntsville, AL. 
has occurred, or if their model is generating erroneous results because 

of the inaccurate transformation of data. 

A Decision Support System (DSS) delivers information that can assist in 

policies, priorities, or processes. The challenge in conducting 

scientific research is ensuring that results are used to enhance 

societal decision making (Lee and Birk, 2003). DSSs can vary from a more 

pure scientific Earth-systems type to a more community-participation 

type, but many DSSs contain a strong geospatial component. Armstrong and 

others (1986) refer to Spatial DSSs (SDSS) as programs that assist 

decision makers in evaluating alternative solutions to spatial problems 

through the integration of software such as GIS. 

The U.S. Bureau of Reclamation’s Agricultural Water Resources Decision 

Support – Evapotranspiration Toolbox system is a good example of a DSS 

with a strong spatial component. It enables operations managers to make 

better decisions. The system couples both a GIS land use layer and 

evaporation estimates for each raster cell of a project area to an 

environmental model (U.S. Bureau of Reclamation, 2004). Other good 

examples of DSSs containing strong spatial components are participatory 

GISs that utilize GIS data to allow groups to deliberate criteria that 

describes problems at various scales (Jankowski and Nyerges, 2001). 

More than a decade ago, some work on DSS-like tools specifically for map 

projections was investigated by Nyerges and Jankowski (1989) and Smith 

and Snyder (1989). Nyerges and Jankowski presented a specification of a 

knowledge base used in an expert system for map projection selection and 

focused on what knowledge should exist rather than how it should be 

represented. Smith and Snyder developed a computer software package 

called the Expert Map Projection Selection System, that uses objectoriented structures and artificial–intelligence programming methods. 

Their system is for use in designing thematic maps and other customized 

map products generally constructed within a GIS environment. 

DISCUSSION 

DESIGN & IMPLEMENTATION 

We designed a DSS for map projections of small-scale datasets. The 

design and implementation is a Web-enabled DSS for map projection 

selection. Usery and others (2002) demonstrated an initial development 

of a DSS to guide users in the selection of an appropriate map 

projection. We have expanded this system for selecting an optimum 

projection considering various factors, such as pixel size, areal 

extent, number of categories, spatial pattern of categories, resampling 

methods, and error correction methods, although not all features 

currently are implemented (U.S. Geological Survey, 2004). 

INTERACTIVE INTERFACE 

The DSS poses questions interactively. The choices selected by the user 

determine the path in a decision tree to a solution. The design includes 

an interactive interface to solicit information from the user and guides 

him to the selection of an appropriate projection (or alternate 

projections) based on his responses (figure 1). Although the interface 
is rather intuitive and straightforward, Posch and Finn (2003) provide a 

user’s guide for assistance. 

Figure 1. Interactive Interface for DSS. 

AERIAL EXTENT AND CHARACTERISTIC TO PRESERVE 

In recommending a map projection for small-scale regional data, the 

system considers geographic location as well as directional extent. The 

primary input to the DSS is the area to map. This is posed as a choice 

among global, continental, or regional area of coverage (figure 2). The 

next input is the preservation of shape, area, or simply a compromise. 

This drives the decision to a conformal or equal-area projection, or a 

compromise between the two (figure 3). We included a compromise 

selection such as the Robinson projection, which is neither conformal 

nor equal area, yet has nice visual appeal because of its utility for 

data display instead of its utility for analyses. 
Figure 2. Drop Down Menu for Type of Area. 

Figure 3. Drop Down Menu for What to Preserve. 

The system handles continents based on their geographic location and 

directional extent. For example, the DSS handles continents with greater 

longitudinal range (Asia, Australia, and Europe) differently than those 

with greater latitudinal range (Africa, North America, South America) or 
polar location (Antarctica). It recommends a projection in real time 

factoring in the area of the continent and the geometric characteristic 

to preserve. For regional cases, which generally are smaller (largerscale mapping) than a continent, the DSS allows the user to 

interactively define an area of interest, while also allowing the choice 

of conformal or equal-area projection. Like the continental selection 

page, it recommends a projection in real time. The interface for 

regional area selection is shown in figure 4. 

Figure 4. Regional Area Selection Interface. 

DECISION TREE 

The DSS relies on conventional theory for the selection of projections 

(Maling, 1973; Voxland, 1978; Snyder, 1987; Snyder and Voxland, 1989), 

and the design utilizes a specific decision tree that handles regional 

and global raster projection (figure 5 a-d). We supplemented these 

selection criteria with results from research performed by Usery and 

others (2003a, 2003b). 
Figure 5a. Decision Tree; Level 0, Root. 

Figure 5b. Decision Tree; Level 1, Global. 

Figure 5c. Decision Tree; Level 1, Continental. 
Figure 5d. Decision Tree; Level 1, Regional. 
FURTHER REFINEMENTS 

Refinements planned for the current prototype DSS include other user 

choices for raster versus vector data model, and thematic versus 

continuous data type. The prototype currently is in testing phase. The 

objective is to develop a DSS that will include an integrated, Web-based 

map projections tutorial, somewhat similar to the design described by 

Jankowski and Nyerges (1989) and with an eye towards using the agentbased framework for integrating spatial data into an SDSS developed by 

Sengupta and Bennett (2003). Further implementation of the DSS will 

expand the user input to include data type, volume or resolution, and 

other appropriate options. In addition, this DSS will be refined to 

include recommendations of appropriate projection parameters to coincide 

with the suggested projection, such as the standard parallels or central 

meridian. 

CONCLUSIONS AND RECOMMENDATIONS

This study has implemented a DSS to help users select an appropriate 

projection based on factors that commonly cause problems, including 

location of regional area, extent direction, characteristics to be 

preserved, and data type. Future work will improve and expand the DSS to 

include a complete tutorial, and examine the capabilities for displaying 

raster data of varying resolutions and recommending appropriate 

projection parameters. 

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