A Cost-Benefit Analysis of 

The National Map

By David Halsing, Kevin Theissen, and Richard Bernknopf

Circular 1271

U.S. Department of the Interior

U.S. Geological Survey
U.S. Department of the Interior

Gale A. Norton, Secretary

U.S. Geological Survey

Charles G. Groat, Director

U.S. Geological Survey, Reston, Virginia: 2004

For sale by U.S. Geological Survey Information Services

Box 25286, Denver Federal Center

Denver, CO 80225

This report and any updates to it are available online at:

http://pubs.usgs.gov/circular/c1271/

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Any use of trade, product, or firm names in this publication is for descriptive purposes only and does not imply 

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Cataloging-in-publication data are on file with the Library of Congress (URL http://www.loc.gov/).

Produced in the Western Region, Menlo Park, California

Manuscript approved for publication, May 5, 2004

Text edited by James W. Hendley II

Layout by Susan Mayfield
 iii

Executive Summary

The Geography Discipline of the U.S. Geological Survey (USGS) has conducted this 

cost-benefi t analysis (CBA) of The National Map. This analysis is an evaluation 

of the proposed Geography Discipline initiative to provide the Nation with a 

mechanism to access current and consistent digital geospatial data. This CBA is a 

supporting document to accompany the Exhibit 300 Capital Asset Plan and Business 

Case of The National Map Reengineering Program. 

The framework for estimating the benefi ts is based on expected improvements in 

processing information to perform any of the possible applications of spatial data. 

This analysis does not attempt to determine the benefi ts and costs of performing 

geospatial-data applications. Rather, it estimates the change in the differences 

between those benefi ts and costs with The National Map and the current 

situation without it. The estimates of total costs and benefi ts of The National Map

were based on the projected implementation time, development and maintenance 

costs, rates of data inclusion and integration, expected usage levels over time, and 

a benefi ts estimation model. 

The National Map provides data that are current, integrated, consistent, complete, 

and more accessible in order to decrease the cost of implementing spatial-data 

applications and (or) improve the outcome of those applications. The effi ciency 

gains in per-application improvements are greater than the cost to develop and 

maintain The National Map, meaning that the program would bring a positive net 

benefi t to the Nation. The average improvement in the net benefi t of performing 

a spatial data application was multiplied by a simulated number of application 

implementations across the country. The numbers of users, existing applications, 

and rates of application implementation increase over time as The National Map

is developed and accessed by spatial data users around the country.

Results from the “most likely” estimates of model parameters and data inputs 

indicate that, over its 30-year projected lifespan, The National Map will bring a 

net present value (NPV) of benefi ts of $2.05 billion in 2001 dollars. The average time 

until the initial investments (the break-even period) are recovered is 14 years. Table 

ES-1 shows a running total of NPV in each year of the simulation model. In year 14, 

The National Map fi rst shows a positive NPV, and so the table is highlighted in 

gray after that point. Figure ES-1 is a graph of the total benefi t and total cost curves 

of a single model run over time. The curves cross in year 14, when the project 

breaks even. A sensitivity analysis of the input variables illustrated that these 

results of the NPV of The National Map are quite robust. Figure ES-2 plots the 

mean NPV results from 60 different scenarios, each consisting of fi fty 30-year runs. 

The error bars represent a two-standard-deviation range around each mean.

The analysis that follows contains the details of the cost-benefi t analysis, the 

framework for evaluating economic benefi ts, a computational simulation tool, and a 

sensitivity analysis of model variables and values.

Table ES-1. Yearly results for mean net 

present value of baseline scenario. 

[The cumulative mean net of benefits and costs are 

presented, with negative net present value (NPV) 

in parentheses. These are mean values because 

30 model runs were simulated to capture model 

variance. In year 14, the NPV becomes positive, 

and gains accumulate thereafter (in gray). The final 

mean and standard deviation NPV are included at 

lower right]
iv 

Mean Net Present Values of Scenarios 

-1

0

1

2

3

4

5

6

7

8

9

02468

1012141618202224262830323436384042444648505254565860

Scenario Number

NPV (Billions of 2001 dollars)

Figure ES-1. Total cost and total benefit curves of The National Map. A graph of the accumulating benefits (blue) and costs (red) of The National Map over 30 years. The break-even point is where the curves 

cross (year 14). The kink in the cost curve is the change from development costs to maintenance costs 

only. Note the slow upward climb of the benefit curve until the project nears completion, when most of 

the country’s spatial-data users have had time to see valuable data, adopt The National Map as their data 

source for existing tasks, and innovate new applications for spatial data. The area above the cost curve 

and below the benefit curve between years 14 and 30 represents the net present value (NPV).

Figure ES-2. Sensitivity analysis results; mean net present value by scenario. The 60 

different scenarios were each run 50 times. Results from each scenario are plotted here, 

as the mean net present value (NPV, yellow diamond) and the two-standard-deviation 

range (approximately the 95-percent confidence interval; red bars). Note that not all 

scenarios reach a positive value.
 v

Acknowledgments

Several people made critical contributions to this project. Dr. Anne Wein, a volunteer 

operations research analyst, provided critical insights into formulating the mathematics of 

the theoretical framework. Amy Mathie contributed considerable time and energy to the 

early phases of defi ning and scoping this analysis. Members of the Program Management 

Section—including Dale Russell, Bill Coronel, John Fisher, and Janet Goodman—assisted us in 

arranging and conducting the interviews of GIS managers and contractors, from which we were 

able to derive much of our data. Dr. Stephen Gillespie generously reworked survey numbers for 

inclusion into our modeling. He also gave signifi cant feedback and helpful criticism to both the 

theoretical and computational model development. We are deeply indebted to two technical 

peer reviewers, Carl Shapiro and Donald Bieniewicz, whose extremely detailed comments and 

insights greatly improved the fi nal analysis and report. Finally, we appreciate the assistance 

given by Linda Hicklin and Joan Sziede in scoping the project.
vi 
 vii

Contents

Executive Summary ....................................................................................... iii

Acknowledgments ....................................................................................... v

Introduction ................................................................................................ 1

1. Literature Review ....................................................................................... 1

2. Framework for Cost-Benefit Analysis of The National Map ....................................... 3

3. Data and Modeling Methods, Results, and Sensitivity Analysis ................................. 8

4. Results and Sensitivity Analysis ..................................................................... 10

5. Discussion ............................................................................................. 14

References Cited .......................................................................................... 14

Appendices

 A. Formal Economic Theory ..................................................................... 17

 B. Details of Data Gathering and Synthesis ................................................... 18

 C. STELLA Software and NB-Sim Model Details ................................................ 26

 D. Sensitivity Analysis .............................................................................. 37

Tables

 ES-1. Yearly results for mean net present value of baseline scenario ........................... iii

 1. Mean net present values (NPV) of baseline scenario, by year ........................... 11

 B1. Calculating the value of an application ...................................................... 19

 B2. Applications and their estimated values (where available) .............................. 21

 B3. Alphabetical list of county-level GIS applications .......................................... 25

 C1. Variables in the theoretical and simulation models and their baseline values ......... 27

 C2. Default NB-Sim model inputs and their sources ............................................. 28

 D1. Sensitivity analysis scenarios and results ................................................... 39

Figures

 ES-1. Total cost and total benefit curves of The National Map ................................. iv

 ES-2. Sensitivity analysis results; mean net present value by scenario ........................ iv

 1. Values of spatial data over time ............................................................... 4

 2. Possible future values of spatial data over time ............................................. 4

 3. Variation in net present value resulting from 50 runs of the baseline scenario ......... 12

 4. The total cost and total benefit curves for a single run of the baseline scenario ...... 12

 C1. The user interface in the computational model, NB-Sim ................................. 29

 C2-C8. Diagrams of underlying dynamics of the NB-Sim model ................................. 30–35

 C9. Graphical input window for NB-Sim ......................................................... 36

 D1. Sensitivity analysis results; mean net present value by scenario ........................ 38
viii 
A Cost-Benefit Analysis 1

A Cost-Benefit Analysis of The National Map

By David Halsing, Kevin Theissen, and Richard Bernknopf 

Introduction

History and Purpose

The Office of Management and Budget requires an 

Exhibit 300 Capital Asset Plan and Business Case (hereafter referred to as the “Exhibit 300”) for the U.S. Geological 

Survey Geography Discipline’s effort, The National Map. The 

Exhibit 300 requires submission of a cost-benefit analysis 

(CBA) of each proposed program. Western Geographic Science Center staff conducted this CBA in support of the Exhibit 

300. Work on this CBA was begun in October 2002 and was 

completed in July 2003.

Organization of the Report

This report is organized in the following manner. Section 1 is a review of the literature involving the use and value 

of spatial data and Geographic Information Systems (GIS). 

Section 2 presents the conceptual framework that underlies the 

benefits-estimation model at the heart of this CBA. Section 3 

explains the data gathering methods and outlines the computational model developed to simulate the spread of spatial-data 

use and the benefits that would ensue. Section 4 contains 

primary results and provides an overview of the sensitivityanalysis methods and results. Section 5 is a discussion of the 

major results and implications for The National Map program. 

In addition to the main report, several appendices have been 

compiled to provide the details of the analytical approach. 

These describe the economic theory (appendix A), the data 

gathering steps (appendix B), the model calculations and 

baseline parameters (appendix C), and the exact inputs for the 

sensitivity analysis (appendix D). 

Fitting this Analysis into the Exhibit 300 Report

This analysis was initiated in support of an Exhibit 300 

Report for the Office of Management and Budget that initially 

addressed the entire program known as The National Map. The 

Reengineering Program is essentially the information-technology (IT) component of the larger vision of The National Map. 

A more specific Exhibit 300 was created to solely address this 

Reengineering Program. This report accompanies that more 

specific Exhibit 300. Despite the narrower subject matter of the 

new Exhibit 300 Report, this cost-benefit analysis covers the 

entire program that will eventually produce The National Map. 

There is a reason for this apparent discontinuity. The 

scope of the analysis was determined on the basis of the 

inseparability of the IT component from the other necessary 

components of The National Map, that is, the problem of joint 

costs. The IT portion is absolutely necessary to produce the 

benefits of The National Map but on its own is insufficient to 

do so. The Reengineering Program carries a clear and definable share of the cost of developing the larger National Map, 

yet it is impossible to evaluate the proportion of total net 

benefits that will arise from only this IT component. 

A method employing a simulation approach was developed to evaluate all components of The National Map, instead 

of solely the IT component. It is suggested that the Exhibit 

300 Reports for other required components of The National 

Map include results from this CBA, recognizing that, while 

they each bring their own definable share of the program 

costs, the benefits of The National Map arise only from their 

inseparable contributions.

1. Literature Review

The collection, integration, and distribution of digital 

spatial data at the scale and extent of the USGS effort called 

The National Map is a relatively new occurrence. As such, the 

literature on direct analogs of The National Map is comparatively rare. Studies were conducted for the British Ordnance 

Survey (OXERA, 1999) and the Australia-New Zealand Land 

Information Council (Price Waterhouse, 1995), but these are 

not exact comparisons. Instead, much of the available literature on spatial data focuses on the use of Geographic Information Systems (GIS) as compared to using paper maps or no 

spatial data at all. For example, the two studies mentioned 

above lean heavily on subjective valuations of GIS/no-GIS 

alternatives. Nevertheless, these types of studies constitute 

the bulk of the literature upon which this cost-benefit analysis 

necessarily relied.

Early in the growth of GIS, the need to measure the economic feasibility of GIS implementation was recognized and 

documented in the literature. Most of the economic studies 

take a CBA approach and were conducted internally by local, 
2 The National Map

regional, or national governments and (or) by hired consultants (see for example, Center for Technology in Government, 

1995; Ledbetter and others, 1997; Hardwick and Fox, 1999; 

Montgomery County, 1999; Price Waterhouse, 1995; OXERA, 

1999). These case studies and project assessments are sometimes scorned in the literature for being anecdotal, but they 

serve as important “building blocks” to our understanding of 

the costs and benefits of The National Map.

The costs of implementing GIS in an organization are well 

understood and are not difficult to quantify by most accounts 

(see Korte, 1996, for an example of GIS implementation costs). 

Costs include data, equipment, software, and other supplies, 

training, operating costs, systems development, and system 

maintenance. These costs can be substantial (that is, millions of 

dollars annually) particularly the initial costs for equipment and 

later costs of data maintenance and systems development.

Several different types of GIS benefits are discussed in 

the literature. Most of these benefits fall under two broad categories termed efficiency and effectiveness benefits (Prisley and 

Mead, 1987; Gillespie, 1994). Efficiency benefits (often called 

“cost-savings” or “costs-avoided” benefits) are those that arise 

when the addition of a GIS reduces the costs to perform existing tasks in an organization. This is most often achieved by 

reducing the amount of time needed (or even the necessity) to 

perform a particular task. For example, once topographic maps 

are in digital format and can be distributed electronically there 

is less need for staff time to produce, maintain, and distribute 

paper maps. In several published studies efficiency benefits 

are directly measured in terms of the number of staff hours/

salary that are saved by the addition of a GIS (see for example, 

Korte, 1996; Baltimore County, 2001). Effectiveness benefits 

(sometimes called “value-added” benefits) are those that arise 

from improvements to existing tasks or the addition of new 

tasks that could not be performed prior to the implementation 

of the GIS. For example, land use/land cover maps that are 

updated monthly in a GIS database could improve environmental decision-making by visualizing seasonal and interannual trends and patterns that were not apparent in a series of 

paper maps that were updated every 15 to 20 years.

Quantifying the full benefits of a GIS is a difficult task. 

Although it can be relatively straightforward to calculate some 

of the efficiency benefits directly, effectiveness benefits are 

very difficult to estimate and require a more rigorous analysis 

than is found in a traditional CBA (Dickinson and Calkins, 

1988; Gillespie, 2000). Accordingly, some of the economic 

evaluations of GIS in the literature make a quantitative 

estimate of the efficiency benefits that a GIS can potentially 

bring to an organization. However, only a few studies have 

made quantitative estimates of any of the effectiveness benefits 

(see for example, Bernknopf and others, 1993; McInnis and 

Blundell, 1998), and we are aware of no comprehensive study 

of the effectiveness benefits that GIS brings to an organization. Instead, effectiveness benefits are typically treated as an 

important, qualitative “bonus” that can be added to the more 

easily quantified cost-savings resulting from the implementation of a GIS (see for example, Hardwick and Fox, 1999).

In the past it appears that this methodology has been 

accepted because the efficiency benefits (cost savings) alone 

are usually sufficient to give a positive net benefit and justify 

investment in the GIS. Even when this is not the case it is 

simply assumed that the full benefits of a GIS will outweigh 

the costs—and most economic studies of GIS (whether rigorous or not) conclude that positive net benefits arise from the 

implementation of GIS technology in an organization1

.

Estimation of the effectiveness benefits of a GIS is potentially a time-consuming and expensive process. This generally 

involves developing a metric for outcomes of many tasks that 

are, by their nature, non-market goods. One study found that 

a city in Florida used a computer-aided GIS to reroute their 

ambulance dispatch system and trimmed the response time by 

1 minute per call (referenced in OXERA, 1999). To put a dollar value on that reduced response time is significantly more 

difficult and generally requires a separate study. For example, 

an analysis of the ambulance routing program developed an 

estimate of $150,000 in savings per year.

Without some measurement of these benefits, however, a 

CBA will greatly underestimate the full benefits that will arise 

from the implementation of a GIS. Early studies of the economic value of geologic information by Bernknopf and others 

(1988a,b) examined effectiveness benefits in losses avoided 

from landslide hazards and showed the potentially large benefit 

that can be derived from geospatial data. Bernknopf and others 

(1993) later developed an economic model to estimate the value 

of geologic map information. They converted a geologic map of 

Loudon County, Virginia, into digital format and estimated the 

benefits that would accrue from an improved mapping resolution for two applications—the siting of a county landfill and the 

selection of an appropriate transportation corridor. They found 

that significant benefits arise from the improved resolution of 

data from an updated version (1992) of the Loudon County 

geologic map originally made in 1963. The benefits, estimated 

at $1.28 to 3.50 million, were measured in avoided costs of 

landslides and property value loss.

Gillespie (1997, 2000) later developed a model for users 

to make a relatively quick estimate of the full benefits of a 

GIS. The authors of one study (McInnis and Blundell, 1998) 

used the model to estimate the benefits arising from a small 

number of applications, and they speculate that the model is a 

useful tool to be considered for more complete analyses. The 

main strength of the model is that it offers a method to estimate effectiveness benefits in an inexpensive manner. Yet, the 

authors also noted that the selection of some of the values for 

the variables in the model can be subjective and that the model 

is overly sensitive to some of these values. 

In addition to efficiency and effectiveness benefits, the rates 

of usage and diffusion of publicly available geospatial information are crucial to an understanding of the benefits arising from 

The National Map, but these are also poorly understood quanti1

An exception to this is the study of Bond (2000) who notes that whereas 

GIS has great potential it has primarily been used for operational tasks that 

have not produced a positive net benefit.
A Cost-Benefit Analysis 3

ties. The rate of diffusion of information and communication 

technologies is often modeled as an S-shaped curve, in which 

slow diffusion of information occurs prior to a period of rapid 

diffusion, which is in turn followed by a long, leveling-off period 

(Rogers, 1962; Hwang, 2002). Knowing the slope and start 

time of the rapid diffusion of information and communication 

technologies is critical to determining its benefits, but predicting these two pieces of information is difficult if not impossible. 

Often, the diffusion of technology turns out to be slower than 

models and other predictive tools might suggest. For this reason, 

some researchers recommend caution in the use of diffusion 

models as predictive tools (see for example, Lennstrand, 1998). 

There is clearly a need for further theoretical and academic study of the benefits arising from geospatial information and the implementation of GIS in an organization. 

The development and implementation of The National Map

provides an excellent opportunity to help address this need. In 

the present study, we have developed a model that can be used 

to estimate the benefits that arise from The National Map, and 

we report the results of our analysis.

2. Framework for Cost-Benefit Analysis 

of The National Map

Introduction

This section contains the methodology for benefits estimation. The method makes clear distinctions between several 

important aspects of the use of spatial data. Specifically, it:

• Distinguishes the costs and benefits of spatial data 

itself from those of the applications of the data,

• Compares the state of the world with The National 

Map from that without it,

• Recognizes that uses of spatial data are likely to 

increase over time, in part as a function of The 

National Map, and

• Accounts for variation in the baseline ability of customers to use The National Map data.

The purpose of this policy evaluation is to assess the 

costs and benefits of The National Map and to estimate its 

net present benefit to society. A positive net present benefit 

would mean that the costs of developing and maintaining The 

National Map are less than the benefits society reaps from 

its existence. These benefits come from many sources that 

involve the use of spatial data in a variety of public and private 

applications. These benefits include:

• Lower costs of developing and populating a GIS. 

• Less redundant data collection.

• Reduced private purchase of data.

• Lower costs of carrying out tasks that require spatial data.

• Reduced staff time because of data currency, availability, consistency, completeness, and other factors.

• Improved quality of decisions made with the data.

• Increased quality of data (for example, current, seamless, consistent).

• Uniform availability of descriptive metadata.

• Instantaneous, constant, and widespread data availability.

• Increased use of spatial data.

• Greater numbers of entities using spatial data in existing applications.

• New applications will develop as a function of better 

data availability.

The key policy questions are: 

 (1) Does The National Map increase the efficiency and (or) 

effectiveness of using spatial data?

 (2) Does The National Map increase the frequency of spatial 

data use in existing applications? 

 (3) Does The National Map allow some who do not currently use spatial data to do so?

 (4) Does The National Map foster the development of innovative applications for spatial data?

 (5) Will these contributions of increasing the type, volume, 

and efficiency of spatial-data utilization justify the 

increased cost of the program?

Basis for Comparison

The Exhibit 300 Report for The National Map outlines 

three alternative policy proposals: 

• Alternative 1—A fully implemented version of The 

National Map is built over 10 years at an annual cost of 

$25 million per year over 2001 funding levels.

• Alternative 2—A version of The National Map is built 

on the basis of 2001 funding levels by diverting other 

USGS Geography Discipline funding into the program. 

The description of alternative 2 states that in this case, 

no guarantees could be made about how long it would 

take to develop The National Map, the degree to which 

it would be completed, how consistent its data would 

be, and so on.

• Alternative 3—No attempt to build The National Map

is made. The USGS Geography Discipline goes about 

“business as usual” as it was in 2001, the year before 

work began on The National Map.
4 The National Map

The basis for this cost-benefit analysis, therefore, is to 

compare the differences in the states of the world that would 

result from achieving alternative 1 versus alternative 3. As 

such, it considers only the difference between the costs and the 

benefits of The National Map program, assuming that all other 

aspects of the Geography Discipline remain the same. We 

believe that this comparison adequately addresses the states 

of the world with and without The National Map, which is the 

main goal of a cost-benefit analysis. However, it is important 

to develop fuller descriptions of alternatives 2 and 3 and to 

include discussions of other alternatives to a fully implemented National Map. 

Alternatives to The National Map

This analysis assumes that alternative 3 would see no 

significant changes to the net present value to society of the 

USGS Geography Discipline, either in terms of type, quality, 

distribution, or usage levels of spatial data. This constant value 

is represented in figure 1 by the dashed horizontal line. Conversely, the solid curved line represents alternative 1. The dip 

in NPV in early implementation years arises from the added 

cost of developing The National Map, but the gains thereafter cause the value to society of the Geography Discipline to 

break even and then increase. The dashed curved line represents alternative 2, which lies somewhere between the other 

two. We did not analyze alternative 2, and its shape, trajectory, 

and value as represented here are purely speculative.

In reality, however, the horizontal line for the status-quo, 

alternative 3, world is not accurate. The value of the Geography Discipline’s products would more accurately be expected 

to decrease over time. The Topographic Map Series, a signature USGS product, is 23 years old on average, and getting 

older, due to inadequate funding and staffing levels. Thus the 

maps become more out-of-date and less accurate each year. 

Further, users of spatial information are increasingly turning 

toward digital technologies like GIS and satellite imagery, all 

of which force the USGS to “go digital” or become obsolete. 

Finally, the Federal Geographic Data Committee (FGDC) is 

producing the Geospatial One-Stop (GOS), a standardized 

digital clearinghouse and web portal that has the goal of unifying all Federal data under one electronic banner. Failing to 

participate in GOS would make USGS spatial data and maps 

less relevant than they are today. 

In figure 2 the NPV line for alternative 3 has been 

drawn with a downward curve to capture this decrease in 

value over time. In the interest of parsimony, we did not 

explicitly model this source of benefit from The National 

Map, this “obsolescence avoided,” in our cost-benefit analysis. We conservatively assumed no loss of value in the nonNational Map world.

Other alternatives for ameliorating the obsolescence problem do exist, however. The National Atlas is a freely available 

on-line database and viewer of USGS spatial data holdings 

(see http://www.nationalatlas.gov). Work on the National Atlas 

began in 1997 with the recognition of the need to transition 

to digital, on-line data service. It has some elements that are 

similar to what The National Map would ultimately provide, 

including a map viewer with user-selectable extent and layers, 

and some of its data is being incorporated into The National 

Map. However, like other USGS data products, there is insufficient funding to keep it current and accurate, and its decline 

in value is assumed to proceed along with the topographic 

Figure 1. Values of spatial data over time. Heuristic diagram 

showing trajectories of value of U.S. Geological Survey Geography Discipline data over time under 3 alternatives proposed in the 

Exhibit 300 Report.

time

2001

NPV

alternative 3

alternative 1

2015

alternative 2

Figure 2. Possible future values of spatial data over time. Heuristic diagram illustrating trajectories of value of U.S. 

Geological Survey Geography Discipline data over time under various management strategies.

time

2001

NPV

alternative 3

alternative 1

2015

Increased funding, National Atlas, and others

State/local gov’t and (or) private collaboration + FGDC

compliance
A Cost-Benefit Analysis 5

map series. Though certainly people are using data from the 

National Atlas2

, its utility is assumed to decline as it ages.

There is also the possibility of collaborations between 

private entities and (or) other government (including state, 

local, or other federal) efforts to self-organize into something 

like The National Map. This is certainly possible, and the 

FGDC has been working to develop the National Spatial Data 

Infrastructure (NDSI) to encourage this dynamic. However, we 

assumed that these other attempts would be likely to take longer to develop, achieve less complete coverage, and could fail 

to deliver the free-access that is essential to the public-good 

component of The National Map. For example, FGDC’s NSDI 

effort began in 1990 and is not yet complete. Other examples 

of collaborative efforts include the Open GIS Consortium, 

the National States Geographic Information Council, and the 

National Association of Counties.

It therefore seems a valid assumption to conclude that, 

although substitutes for The National Map could eventually 

exist, they would not be likely to bring as much value to society as would the rapid and complete implementation of The 

National Map through alternative 1 funding. 

Assumptions

Our efforts to model the institutional dynamics that 

address these questions require several broad simplifying 

assumptions. The list below states a few of these assumptions 

before the theoretical model is described (further discussion 

of these and other modeling assumptions can be found in the 

relevant chapters and appendices):

 (1) For the purposes of this analysis, The National Map

program consists of the fully implemented National 

Map, which is alternative 1 from the original Exhibit 300 

Report. We analyze the costs and benefits of the differences between that program and what existed prior to its 

inception, which is alternative 3. Although the Geography Discipline of the U.S. Geological Survey has already 

begun to develop The National Map with no additional 

funding, as described in alternative 2, we compare the 

“with” and “without” cases.

 (2) We assume that The National Map can be built and maintained as described in the Exhibit 300 Report and other 

vision documents. We also assume that whatever budget 

increase is received to produce The National Map will 

allow it to be completed over some time period that is 

dependent on the level of funding. Should little increased 

funding be available, it will delay the completion of The 

National Map but not prevent it.

 (3) The sources of the benefits in this analysis come from 

the eight primary layers of geospatial data as described in 

The National Map vision documents. There are likely to 

be other layers of spatial data and products added to The 

National Map over time, but this analysis does not consider those because they are unknown at the present time.

 (4) The value of digital spatial data comes from its use in a 

variety of “applications.” The net benefit of implementing an application can be improved with better and more 

readily accessible spatial data. That is, having newer, 

better, more plentiful, or more available data allows 

spatially dependent applications to be implemented more 

efficiently, more effectively, or both. The National Map 

would provide this better data, and can be credited with 

some of the resulting improvements of spatial data applications. If data from The National Map is not used in an 

application, no benefit can be credited to it.

 (5) The various applications of digital spatial data can be 

grouped into three categories, or “tiers,” on the basis of 

how complex they are and how much technical sophistication is required to perform them:

 (a) Tier 3 applications are the simplest and are those 

done with paper maps and (or) simple GIS overlays.

 (b) Tier 1 applications are the most complex uses, involving spatial statistics and mathematical modeling.

 (c) Tier 2 applications are moderately complex applications 

and lie between tiers 1 and 3 in terms of complexity.

 (6) The National Map will have 3 types of users/customers 

that are allocated to tiers of capability to utilize the 

data layers it provides. Each tier contains users that are 

described as being in one of three “states of nature.” 

These states of nature reflect the user’s current level of 

facility with GIS and digital spatial data:

 (a) Tier 1 users are the most sophisticated users and have 

access to applications from all three tiers.

 (b) Tier 2 users are moderately sophisticated users and 

have access to tier 2 and tier 3 applications.

 (c) Tier 3 users are the least sophisticated users and can 

only implement tier 3 applications.

 (7) Tier 1 users have the greatest number of applications 

in which they can currently make use of digital spatial 

data, because they can make use of applications from the 

lower two tiers. They have the potential to receive the 

highest total (as opposed to marginal) benefit from using 

this data. Tier 3 users have the fewest number of available applications because they can only implement tier 3 

applications. Tier 2 users lie between these two extremes, 

making use of tier 2 and tier 3 applications.

 (8) The spatial unit of analysis is the county. This does 

not mean we examine only county government uses of 

spatial data. Rather, it means that we use counties as the 

unit of averaging and aggregating uses of digital spatial 

data. While recognizing that not all uses of The National 

Map are at the county scale, in order to build a feasible 

model, we make the simplifying assumption that they 

are. Applications with the spatial extent similar to a 

2 The National Atlas web site satisfies an average of 3.2 million requests for 

services each month (Donald J. Bieniewicz, written commun., 2003).
6 The National Map

county are likely to use some of the base-map information The National Map will provide, whereas city uses 

might require data more specific than that. Also, the large 

number of counties (3078) in the United States provides 

a large number of units over which to integrate. Where 

possible, we scale other uses of data—including private as well as public—up or down to the county level. 

Federal uses of spatial data are handled separately in the 

computational model, as will be discussed more in sections 3 and 4.

Together, Assumptions 5 through 7 illustrate several key 

points. Tier 1 data users are able to make use of digital spatial 

data in many more applications than tier 3 users are. This adds 

more benefits to The National Map per tier 1 user than per tier 

3 user. This is especially true of the novel applications that are 

likely to grow out of a data source as complete and current as 

The National Map. 

A part of the USGS goal is to assist the transition of spatial data users from tier 2 and 3 into tier 1 by providing data 

and lowering the barriers to using digital spatial data. This is 

one of the important pure public good aspects of The National 

Map. Solely providing a tier 3 county with a GIS database is 

something close to a direct subsidy, offsetting their direct costs 

of developing and populating their GIS database. Benefits to 

society do derive from this. If, however, by providing the data 

layers in The National Map, the USGS also helps a county 

develop into an efficient user of spatial data in a variety of 

more sophisticated applications, thereby allowing innovative new applications to be developed and used, then it is an 

example of the Federal Government providing a public good 

that brings greater positive net benefits to all.

Below, we derive a quantitative framework for enumerating and estimating these benefit streams and how their rates 

of accrual determine the net benefit of The National Map. The 

next section explains the major steps of the model.

The Economic Model for Estimating Benefits

Values Come from Applications

The benefits of The National Map come from the value of 

information as its data is used to permit, facilitate, or improve 

some public or private decision or process. These benefits 

either can be in terms of obviating the need for users to generate, populate, or operate their own GIS database (cost savings; 

efficiency gains) or through making it faster or more effective 

to carry out tasks and projects (value added; effectiveness 

gains). Other value-added benefits could come from development of all-new uses of spatial data that are currently impossible or inefficient because of a lack of consistency, currency, 

or completeness of spatial data. 

Because the benefits of The National Map are based on 

expected improvements in processing information for applications of spatial data, the willingness to pay for the program is 

based on its utility as a provider of that data. The value added 

of The National Map originates from improvements in projected implementation time, reduced development and maintenance costs, faster rates of data inclusion and integration, 

and increased expected usage levels over time for geospatial 

applications. This is a derived demand for the on-line distributed database of The National Map and assumes that data are 

current, integrated, consistent, complete, and more accessible 

to produce the desired outcome of an application. Appendix A 

contains a derivation of the economic foundation of a derived 

demand for The National Map and provides the theoretical 

support for the benefits estimation procedure. 

The benefits of an application are the net present value 

of a partner’s ability to improve a decision’s effectiveness or 

efficiency with the use of spatial information or to use spatial 

data in a way that was not previously feasible. Throughout this 

document, “application” means a single type of use for a set of 

digital spatial data provided by The National Map. Examples 

of applications include:

• Creating an emergency evacuation plan.

• Designating critical habitat for an endangered species.

• Conducting property tax assessments.

• Researching land-cover change and deforestation.

• Verifying insurance claims.

Applications include any use of spatial data, whether by 

government or the private sector, by individuals or organizations. However, conducting the same application annually 

for 10 years does not create a list of 10 applications. Instead, 

it signifies there are 10 implementations of a single application over time. Similarly, an application conceptualized and 

enacted in one city, then copied by 6 others does not mean 

there were 7 applications. It means there were 7 implementations of a single application. 

The value (or net benefits) of The National Map program 

is equal to the benefits of the information it provides minus 

the cost of the program, as is stated in equation (1) below. In 

this analysis, the program is the on-line distributed database as 

described in the original Exhibit 300 Report. Cost, represented 

by a sum of discounted cash flow of costs, CTNM, is based on 

estimates of implementation and development costs.

The BTNM encompasses the discounted benefits that come 

only from the parts of the larger National Map program that 

partners, customers, and the general public see on their computers, can download and use in some set of applications. 

In equation (1), NBTNM represents the net benefit of The 

National Map, which is synonymous with the value of information in The National Map, and −CTNM and BTNM are the total 

costs and benefits of The National Map.

."4.-#4.-"4.- 	

Here, costs are only the costs of The National Map, not 

of the applications in which it is used. Application costs are 

addressed directly in other portions of the model. The benefits 

are also written as the benefits of The National Map, because 
A Cost-Benefit Analysis 7

they refer to the benefits of The National Map program, not 

of the applications themselves. Again, these benefits of The 

National Map must come from its data being used in an application. An application implemented by a local entity using 

only its internal data derives no value from The National Map.

Deriving a Net Present Benefit

The benefit of The National Map, BTNM , is a discounted 

summation of the changes in net benefits of the applications. 

Initially, assume that all applications for The National Map

data already exist and are known. Taking into account the tier 

structure, note that some applications require more sophistication than others to implement and that users may not have 

the technical capacity or human expertise to implement all 

existing applications. In our model, tier 3 users may not use 

tier 1 or 2 applications, whereas tier 1 users may draw from 

any of the three tiers of applications. Tier 2 users may use 

tier 2 or 3 applications. To avoid confusion with the subscript 

t for time, in the equations, s refers to user tier state. The unit 

of time is a year and is an interval during which an application may occur.

Define vjst as the net benefit of The National Map data 

as used in an application j by user s at time t. The net benefits 

come from a number of different sources, but generally fit 

into the efficiency and effectiveness benefits described in the 

literature review. At least some of those gains must be rightly 

attributed to The National Map if it is to have any value to 

society. Those improvements (which can be either gains or 

savings) can be written:

NBjst(TNM) are the net benefits of an application occurring in a 

single place at a specific time with The National Map data. 

Similarly, NBjst(SQ) are the net benefits of a single application in the “status quo,” without The National Map. The 

difference between them is the value of The National Map

in that application, vjst. Expanding the previous equation 

illustrates how this value is derived.

Depending on the specific application and the place it is 

being implemented, having The National Map available can 

either reduce the costs of the application (lower Cjst(TNM) — an 

efficiency gain) or increase the benefits that society, a firm, 

a researcher, or an individual can reap from the application 

(higher Bjst(TNM) — an effectiveness gain). Either of these raises 

net benefits of the application (NBjst(TNM)) by some amount. 

Then, by subtracting the original, status-quo net benefits 

[ NBjst(SQ) = (Bjst(SQ) – Cjst(SQ) ) ] of the same application, a value 

can be derived for the incremental value of The National Map

in that application as implemented by that user in that place.

Implicit in equations (2) and (3) are several assumptions. 

First, we assume that spatial data applications affected by The 

National Map are independent of each other. That is, the availability of The National Map data might increase the frequency 

or prevalence of application implementations, but does so 

in an absolute sense and not at the expense of other types of 

applications. Further, there is an assumption that there is no 

significant difference between public and private applications. 

This assumption means that aggregating both of these types 

of applications at the county scale is a fair simplification of an 

issue that certainly could be a concern but which is not possible to address at this level of analysis. The issue is that we 

are considering a social investment and that the cost-benefit 

analysis should be attempting to measure social benefits.

Ideally, it would be possible to make an exhaustive search 

for a large number of applications that had been improved 

with data from The National Map pilot projects and determine 

the difference in net benefits of those applications thus recognized. Those observations would then be used to extrapolate 

the net benefits that could be reaped across the entire country 

by a fully implemented National Map. Those benefits would 

be weighed against the costs of developing The National Map

and a net benefit for the program can be estimated.

However, the current pilot projects of The National Map 

are too new to lend insight or to provide dollar values for 

evaluation of the on-line distribution system in a set of applications. Instead, we have had to rely on non-National Map data 

holdings and delivery systems that are partial analogs of what 

The National Map will eventually be. There are not enough of 

these to do a “count” of total benefits. Instead, we have developed an “average improvement in net benefit per application.” 

Let: Jst = the total number of existing applications available to 

users in each tier, s, at each time, t. These values are expected 

to grow over time as innovation of new uses for spatial data 

proceeds. The average improvement in net benefit of an application in a tier at a given time is the summed net benefits of all 

appropriate applications divided by the number of applications:

However, within each tier, not all applications are needed 

or useful in every location. Also, there are different repetition 

intervals for each application. This means that a different subset of the set of existing applications, Jst, is implemented each 

tier-year. A different set of implementations obviously creates 

a different average change in net benefit of applications. 

Let:⎯v

*

st = the average net benefit of an implemented application in a tier, s, at time t.

The value of ⎯v

*

st changes each year depending on 

specific implementations, accounted for in this model by wst, 

discussed below. We assume that this variability is normally 

distributed with mean ⎯vst and standard ⎯vst/4 deviation . In the 

simulation model, the exact value for this average change in 

net benefits is drawn randomly from a normal distribution for 

each model year. See section 3 for more information.
8 The National Map

To derive the number and type of application implementations, wst , that occur in each tier, s, during each time period, 

t, the following definitions are required:

Kst = the number of data users in each tier, s, at each time, t. 

These values are likely to change over time as counties 

advance upward through the tiers.

Pst = the proportion of applications available to users in each 

tier that are actually implemented in a given time period. 

This is a function of the needs of individual users—not 

all counties need all applications (snow removal routing, 

for example). It is also related to the relevant time interval 

between iterations of an application—some applications 

occur annually; others every five years. It is also a function of the cumulative diffusion of existing applications. 

The exact subset of specific applications varies each year 

and is randomly generated in the computational model as a 

proportion of total existing applications. The next section 

of this report contains more details on how this is derived.

Thus, wst = Jst * Kst * Pst is the tier-specific number of 

applications performed in a single time period. 

CTNM(t) is the cost of The National Map in time period t. 

The time horizon of interest is T. The term (1/(1 + r)

t

) is used 

to discount the benefits and costs occurring at some future 

point, t, by the discount rate, r (according to Federal Government standards), to bring them into present value terms. The 

equation that summarizes all of above discussion and represents 

an expression for the net present benefit of The National Map is:

."4.- 11VST 
TST	
#4.-T	


4

T

 

 R	T S



 	

The increases in the available pool of applications (innovation), the number of places each one occurs (diffusion), and 

the change in number of users in each tier does not appear 

explicitly in the equations above. However, the simulation 

model we constructed (and which is described in a later section of this report) does have inputs for the rates of diffusion 

(into the calculation of Pst), innovation of applications, and for 

changes in the tier-user profile. 

In general, we expect transitions from one state or tier 

to take place faster in those counties and other data usage 

areas that are active partners with USGS in The National Map

program, but the general transition pattern should remain the 

same for all users. For example, after a tier 1 user begins using 

spatial data, The National Map provides a means to share J

applications among K users, in part because it provides much 

of the data needed to implement the application. Part of the 

plan for The National Map is also to compile and distribute 

a “library” of applications implemented by users across the 

country and lists of the data needed to drive them. This is 

another reason why The National Map will increase rates of 

diffusion, innovation, and advancement.

On completion of this report, the actual values for many 

model parameters, values, and rates of change are estimates 

and extrapolations. This means that, in the short term, our 

model and results are closer to simulation than actual prediction. We have made guesses at the evolution of the program 

by selecting reasonable and conservative values for each 

of the variables in the above equations. As The National 

Map is implemented, we can substitute real values derived 

from observation into the cost-benefit framework, modify 

the simulation model, and narrow the range of uncertainties 

around its projections. With the current level of institutional 

knowledge and experience, we use the model to predict reasonable outcomes. 

3. Data and Modeling Methods, 

Results, and Sensitivity Analysis

With the analytical framework in place, the next step 

was to develop a system and a method with which to quantify and account for the changes in the variables over time 

as The National Map evolves. This section first explains the 

analogs between the framework and the simulation model. It 

then provides an overview of the simulation model, outlines 

our data collection methods, and closes with a description of 

the baseline results, including a sensitivity analysis. There are 

additional details on these steps in a series of appendices and 

so they are treated briefly here. 

Putting Theory Into Practice

It was first necessary to adapt the framework contained 

in Section 2 into a workable tool for analyzing the costs and 

benefits. Recall that, by providing data that are current, integrated, 

consistent, complete and more accessible, The National Map

would decrease the cost of implementing spatial data applications 

and (or) improve the outcome of those applications. The sum of 

the efficiency and effectiveness gains minus the cost to develop 

and maintain The National Map would be the net benefit of The 

National Map. The initial approach to the analysis was to:

• Develop a long list of applications of spatial data.

• Sort the applications into the three tiers according to 

their complexity.

• Distribute each county in the United States into one of 

the three tiers, on the basis of the capacity they have to 

perform applications of varying complexity.

• Estimate the net benefits (the benefits minus costs) of 

performing each application of spatial data in the status 

quo world (that is, where no National Map exists).

• Estimate the net benefits of performing that same application in the case where The National Map exists and 

can provide data to assist or facilitate the application.

• The difference between the net benefits in each of these 

cases is the incremental value of The National Map
A Cost-Benefit Analysis 9

data, distribution system, and application library to that 

application.

• Compile these incremental values across space and 

time, estimating how often each application will occur 

and in how many places, and discount to present value.

• Take the costs to develop, implement, and maintain 

The National Map over the relevant time horizon and 

discount to present value.

• The difference between these totals is the net present 

value of The National Map, and represents its value to 

society over the time horizon of interest.

Several modifications from this initial method were 

required. First, because neither The National Map nor any 

completed pilot projects existed, we could not make a direct 

comparison of the “with” and “without” cases. Instead, we had 

to find examples of organizations that had made transitions 

similar to those The National Map will ultimately facilitate. 

This included changing from paper maps to a digital geographic information system (GIS), moving from an internal 

GIS to one distributed freely on the World-Wide Web, going 

beyond simple overlays and visual analysis to more complex 

spatial modeling, or increasing the data update cycle. All of 

these transitions will be enabled or made more accessible to 

local spatial data holders and users as The National Map is 

implemented. It was our assumption that empirical data from 

places that had made these transitions would be representative 

of others that could take place around the country after The 

National Map develops.

Even that modification was not enough, however, to 

follow the steps above. Despite significant time and effort in 

reviewing the literature and conducting interviews with city, 

county, and state GIS coordinators—as well as with National 

Map pilot project leads, Geography Discipline liaisons, and 

contractors who provide the USGS with data and products—

getting reliable numbers for the costs and benefits of implementing spatial data applications before and after one of those 

transitions was extremely difficult.3

Many operating budgets were not set up to track costs 

by project. In other situations, the project costs were well 

known, but the portion of those costs borne by the spatial 

data (acquisition, manipulation, or analysis) was not known. 

Further, it was difficult to get credible figures on how often 

or widespread an application was implemented. Often, the 

post-transition costs could be estimated for a certain use of 

spatial data, but the organization had failed to keep records 

of the pre-transition costs.4

 Finally, as difficult as measuring 

the changes in the cost side of these transitions, there is a 

greater problem with attributing the beneficial outcomes of 

an application to the spatial data input. There is little consensus about measuring these “effectiveness” benefits (see the 

Literature Review and appendix B sections of this report).

Despite these limitations, we found enough examples of 

changes that addressed either the reduced costs (efficiency) or 

increased benefits (effectiveness) of making one or more of 

The National Map-related transitions. From these examples, we 

extrapolated a “mean increase in net benefits per application 

implemented.” For brevity’s sake, we often refer to it as “NB of 

an application,” so the “improvement” and “implemented” portions are implied rather than stated. The small number of examples we found necessitated this “averaging” approach. Details 

of the derivation of the exact dollar amount are in appendix B, 

and for the reasons explained there, we used an improvement of 

only $1,000 gain per application implementation. 

The Computational Simulation Model: NB-Sim

We built a computational simulation model to estimate 

how many application implementations occurred in a given 

year, from which a total benefit of The National Map was calculated. Here, we give only a brief summary of the computational 

model, called “NB-Sim.” It is described in detail in appendix C. 

NB-Sim begins with the distribution of all counties in 

the United States into one of three tiers of complexity, as 

described in section 2. Estimates of this apportionment were 

derived by Gillespie (2003, written commun.), who estimated 

that 65 percent of counties were in tier 3, 30 percent were 

in tier 2, and only 5 percent had the capacity to do the most 

complicated tier 1 applications. These numbers came from his 

interpretation of the Federal Geographic Data Commission 

Survey (Gillespie, 1999) responses. Then, once The National 

Map program is underway, NB-Sim uses an annual implementation rate to determine a rate of transition of these counties 

from non-National Map-users to National Map users. 

Once this transition is made, an individual county can 

begin using data provided by The National Map in a range of 

applications. It is important to remember that “county” as used 

here does not mean “county government” but rather refers to 

all the users within that county. This includes municipal, academic, and private users, and also state government applications that are scaled to the county level. Federal Government 

use of data is treated separately.

Initially, there are a small number of applications available to users in each tier (less than 25 total), but as the number 

of users grows, the number of new applications grows as well. 

The model has an intrinsic rate of innovation of 2 percent per 

user per year. Each user in each tier can potentially utilize all 

the applications that exist at each point in time. However, there 

is a lag time as the applications need to “diffuse” around the 

Nation. No adoption of technology happens instantaneously or 

completely, so the model accounts for that lag with an internal 

variable for “cumulative diffusion rate.” There is a second 

variable to account for something we termed a “need limitation.” This captures the fact that not all applications are needed 

in every county (for example, there is no need for a hurricane 

3 Details of these interviews and literature searches are provided in appendix 

B and in the Literature Review section of the main body of this report.

4 Other literature addressing ways of measuring these “efficiency” gains are 

discussed in the Literature Review section and appendix B of this report.
10 The National Map

evacuation routing system in Wyoming). Nor are all applications implemented each and every year. So the need limitation adjusts the total number of implemented applications 

downward by randomly selecting a fractional number from a 

distribution ranging between zero and one but which is heavily 

weighted toward zero. The draws average out to about 10 to 15 

percent of available applications. 

One other transition that occurs is that the users of The 

National Map data can advance upward through this tier 

system, assisted in large part by the lower cost of data, and the 

ease of using data that are consistent, complete, vertically and 

horizontally integrated, and furnished with reliable metadata. 

This tier advancement shift was assumed to be slow—only 1 

percent per year—but it brings substantial benefits because tier 

2 and tier 1 users have access to many more applications then 

tier 3 users do. Each user tier can make use of the applications 

from the tiers below it, but none can make use of tiers that are 

above it. They also have faster diffusion rates, meaning that 

they get access to those applications sooner.

Thus far, there are a number of “application uses” for 

each tier of county-scale users in each year. A similar process 

happens in a fourth tier, representing Federal applications of 

spatial data. Each of these application uses is multiplied by 

the mean improvement in net benefits of an application, as 

described above. Importantly, the exact value for this variable is not $1,000 every year. Rather, $1,000 is the center 

of a normal distribution around which the program draws a 

random value. The standard deviation is set at one-fourth of 

whatever value is input for the mean. This allows variation 

in the average improvement in net benefit of an application. It is possible that, on rare occasions, this average will 

be a negative value. In any case, this randomness produces 

a different value for per-application improvement for each 

year in the model run.5

 These figures are multiplied by the 

number of implemented applications; the product represents 

the value-in-use of data from The National Map in each tier 

and year. These products are summed in each year, then converted to present dollars by the Office of Management and 

Budget’s suggested rate of 3.2 percent per year (for 30-year 

lifespan projects).

On the cost side, the best estimates of the funding levels 

required to build and maintain The National Map over its 

expected 30-year lifespan are the key inputs. The exact numbers are unknown at the present time, but the Implementation 

Team for The National Map approved the selection of a $25 

million annual cost for the 10-year development period and 

$5 million per year for annual maintenance over the entire 30 

years of the project. The development funding includes the 

start-up costs to form the partnerships, buy or create more current data, and so on. The lower figure for maintenance can be 

thought of as the fixed annual costs, including salaries, regular 

upgrades to computer equipment, facilitating partners’ efforts 

to update the data, and so on.

This analysis assumes that the major goal of the USGS in 

building The National Map—that of being the integrator and 

server of data, rather than the original creator—can and will be 

realized. We assume it will take 10 years to achieve this goal, 

during which time, the costs of the program to the USGS will 

be much greater than after it, when partners will bear the bulk 

of the costs (presumably a much lower total, though). Thus, 

the annual cost of The National Map in the NB-Sim model is 

the sum of development and maintenance costs for 10 years 

and then is only the maintenance cost. Each year’s cost is 

discounted to present value as well. 

In each year, therefore, there are values for the total benefits and total costs of The National Map in present value dollars. The model subtracts the total costs from the total benefits 

to determine the net benefits of The National Map.

4. Results and Sensitivity Analysis

Simulation Results

We ran the NB-Sim model with the best available estimates for the parameters and model inputs described above. 

Again, full details on the values and rates of change are available in appendix C; this is a broad overview. Each model run 

simulated benefits and costs for 30 years and then reported 

the net benefit for each year and the final total. By examining results from a run, it was possible to determine the year in 

which the net benefit switched from negative to positive; this 

would be the break-even time.

The base year for dollar comparison was chosen to 

be 2001, because that was the year The National Map was 

announced and work on it was begun. Even though this analysis took place in 2002 and 2003, the comparison is between 

the status quo, pre-The National Map world and the fully 

implemented The National Map. The last time there was a preNational Map world was 2001. We are currently living in an 

“alternative 2 world,” where there is no additional funding for 

The National Map, but work towards developing it has begun 

with whatever internal redirection of funds is possible. This 

“unfunded implementation” is not the main focus of this study 

and does not fit into the “with versus without” framework of 

a cost-benefit analysis, though we did attempt to simulate it in 

one of the scenarios in the sensitivity analysis discussed below.

Because of several random components in the model, any 

single run had the potential to generate exceedingly high or 

low values for a number of variables. To reduce this possibility, while maintaining variability in the system and avoiding a 

falsely deterministic model, the baseline parameters were run 

50 times, each for 30 years. We took the mean and standard 

deviation of both the net present value of benefits and the 

break-even time. This procedure was followed for the baseline 

results and for the sensitivity analysis of variables.

Results from the “most likely” estimates of model parameters and data inputs indicate that, over its 30-year projected 

5 The center for the normal distribution for the Federal uses of spatial data is 

$10,000, because they are so much broader in scope.
A Cost-Benefit Analysis 11

lifespan, The National Map could have a net present value 

(NPV) of benefits of $2.05 billion dollars, with a standard 

deviation of $490 million dollars. The average time until the 

initial investments (the break-even period) are recovered is 14 

years. The standard deviation around the payback period is 1.9 

years. Under the baseline parameters, The National Map was 

credited with a positive NPV in all 50 of the simulation runs. 

In other scenarios included in the sensitivity analysis—discussed below—that was not always the case. 

Table 1 lists the yearly mean NPV from 50 simulation 

runs, as well as the ending average NPV and standard deviation around it. Note the transition from negative to positive 

NPV in year 14 (marked with gray background). Figure 3 

presents the final NPV of each of the 50 runs, showing the 

variability around results from a single set of inputs. Figure 4 

is a graph of the total cost (in red) and total benefit (in blue) 

curves over time. The sharp kink in the cost curve is caused 

by the switch from funding development and maintenance to 

only spending on maintenance. The benefit curve shows slow 

growth initially, and then curves up sharply as The National 

Map approaches completion. Break even follows just a few 

years thereafter at the point the curves cross. 

The slow growth in early years may seem surprising 

unless the dynamics of the simulation model are examined. 

The National Map takes time to build and populate with data, 

meaning that in the early years, very few county-level users 

see data that is relevant to them and can make the transition 

from a non-National Map county to a National Map user. 

Once there, they make use of only a small fraction of the small 

number of applications we assume exist and are National 

Map-enabled. The innovation of new applications is dependent 

on how many counties have made that initial transition, and so 

grows slowly also. At the outset, most of the users are in tier 

3, meaning that they have access to the smallest number of 

initial applications. Users at all tier levels have had little time 

to adopt and implement many of the existing applications, 

because the cumulative diffusion fraction is still low. All of 

these lead to a very small number of application implementations in the early years of the program.

As The National Map nears completion in year 10, many 

things change. Assuming adequate years of funding were provided for development, by year 10, all counties have become 

users of The National Map data and distribution system. Innovation of new applications is at its peak and continues at a high 

level for many years. The cumulative diffusion has reached its 

maximum in each tier, meaning users have access to almost 

all of the existing applications. Further, the tier advancement 

trend has proceeded, and many users have shifted upward to 

become tier 1 and 2 users instead of tier 3. The result of all 

these changes is that more applications exist, and a great many 

more application implementations are taking place each year. 

In one 50-run simulation, for example, the number of implementations rose from just 65 in year three to 1,700 in year 6 to 

21,000 in year 9. This increasing rate of implementation continues through the entire 30-year run. Again, though, in any 

single model run, the random draw for implementation rate 

causes more interannual variability than what probably occurs 

in reality, but taking the average of many runs shows the more 

consistent trend that is expected.

Sensitivity Analysis

The input data and rates of change used in this simulation 

model are a combination of numbers from literature, program 

implementation plans, extrapolations from interviews and 

empirical data points, and simplifying assumptions. All of these 

estimates are extremely conservative, and we believe we are, if 

Table 1. Mean net present values (NPV) of 

baseline scenario, by year. 

[The change from negative to positive NPV takes 

place in year 14, shaded in gray. The final mean and 

standard deviation for NPV at the bottom of the table]
12 The National Map

Figure 3. Variation in net present value resulting from 50 runs of the baseline scenario. 

The baseline scenario was run 50 times through NB-Sim, each time generating a different 

net present value (NPV) because of several random modeling components. Here, the NPV 

of each run is plotted against the run number to show the range of variation in the baseline.

Figure 4. The total cost and total benefit curves for a single run of the baseline scenario. A graph 

of the accumulating benefits (blue) and costs (red) of The National Map over 30 years. The breakeven point is where the curves cross (year 14). The kink in the cost curve is the change from development costs to maintenance costs only. Note the slow upward climb of the benefit curve until 

the project nears completion, when most of the country’s spatial-data users have had time to see 

valuable data, adopt The National Map as their data source for existing tasks, and innovate new 

applications for spatial data. The area above the cost curve and below the benefit curve, between 

years 14 and 30 represents the net present value (NPV).

"ASELINE3CENARIO
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A Cost-Benefit Analysis 13

anything, underestimating the value to society of The National 

Map. However, it would be unwise to say that these results 

are more prediction than simulation. The NB-Sim model is an 

attempt to capture all the flows of changes in the streams of 

benefits and costs as a result of transitions The National Map is 

likely to bring and to populate them with reasonable numbers. 

It seemed important to go beyond merely submitting a “best 

guess” estimate of results and instead provide a tool to examine 

scenarios and input values more pessimistic and (or) more optimistic than the baseline values we used. To address this issue, 

we conducted a sensitivity analysis of the values and variables 

in the baseline scenario. A full discussion, analysis, and table 

of results are included in appendix D, but here, we provide an 

overview of the methods and results. 

In the sensitivity analysis, each set of inputs was 

dubbed a “scenario,” regardless of whether only one variable was changed from its baseline value or whether there 

were wholesale revisions. Each scenario was given a number 

and a short description, most of which only describe those 

inputs that are different from the baseline. As in the baseline 

case, each scenario consists of 50 runs with the same input 

values, to account for the randomness of many model parameters. The 60 scenarios were grouped into two groups of 30. 

The 30 scenarios in each group were identical except for the 

annual development cost, which changed from $25 million 

to $50 million per year. The other changes ranged between 

more conservative than the baseline and more optimistic. The 

conservative/ pessimistic changes were made to examine how 

low specific values could get and still allow The National Map

to make economic sense. The optimistic cases were developed 

to see how large the benefits to society could get if the input 

estimates were even a little bit too low.

The bullets below highlight the most important insights 

from an examination of the simulation results:

• With a 10-year development time, $25 million per 

year to build and $5 million per year to maintain The 

National Map produce positive net benefits that are 

robust to large numbers of even more conservative 

changes.

• The following changes, enacted individually, do not 

dramatically reduce the NPV:

• Eliminating tier advancement.

• Eliminating all Federal use of spatial data.

• Reducing the initial number of applications to 1 per tier.

• Designating all counties as initially in tier 3.

• Distributing the counties equally across all three tiers.

• The following changes, enacted individually, reduce 

NPV substantially, but still provide large positive net 

benefits to society:

• Reducing cumulative diffusion to one-half of baseline at all points in time.

• Cutting intrinsic innovation rate by one-half, down 

to 1 percent.

• Lowering the increase in net benefits per application 

use to $500 instead of $1,000 (and $5000 for Federal 

applications instead of $10,000).

• The following optimistic changes, enacted individually, 

increase NPV:

• Increasing innovation rate to 5 percent brings a dramatic gain to more than $5 billion.

• Making diffusion immediate and complete, rather than 

gradual and partial, increases NPV to more than $3 

billion; merely doubling its rate brings similar gains.

• Doubling the per-application improvement in net 

benefit of an application (that is, making it $2,000 

per use and $20,000 for Federal applications) 

roughly doubles the NPV to more than $4 billion.

• Shifting initial tier distribution entirely to tier 1 or 2 

brings substantial improvements over baseline; tier 1 

more so than tier 2.

• Increasing the value of either normal or emergency 

Federal applications does not bring substantial gains; 

there simply are not enough application implementations to make these changes stand out.

• When combining pessimistic changes, each one 

reduces NPV further, but even an unrealistically 

“worst-case” conservative scenario—no tier advancement, cutting net benefit gain per application use in 

half, making innovation rate 1 percent per user-year, 

and setting initial numbers of applications to one per 

tier—still brings a positive, though small, NPV of 

about $100 million.

• Assuming a constant total budget for development, but 

speeding up development to 5 years brings significant 

gains in NPV; conversely, slowing it to 20 years cuts 

NPV in half to about $1 billion.

• Failing to implement The National Map fully—that 

is, funding it for less than the time it is estimated to 

require—cuts the NPV severely.

• Building The National Map through Alternative 2—no 

additional funding—does bring a positive NPV, but it 

is very small, only $22 million over 30 years.

Importantly, the NB-Sim model is not only useful for 

sensitivity analysis; this tool also provides a method for 

evaluating The National Map program development as it 

proceeds. As better observations and empirical data are 

collected, the input numbers and rates of change can be 

improved so that they more nearly represent reality. Moreover, it can assist the Implementation Team in simulating the 
14 The National Map

outcomes of various program development choices as they 

arise. In this use, it is as much a decision-support system as it 

is a cost-benefit analysis.

5. Discussion

This policy analysis was conducted in support of the 

USGS Geography Discipline’s effort to develop The National 

Map. The research goal was to develop a system with which to 

estimate and analyze the costs involved in building, maintaining, and distributing The National Map and the various benefit 

streams expected from its existence. We tried to capture all the 

major transitions and dynamics in the amount, type, and value 

of spatial data uses that The National Map can enable and to 

put dollar values on the benefits that can result from those 

changes. Comparing those benefits and costs, we sought to 

estimate a net benefit to society that could result from developing The National Map.

We developed an analytical framework based in economic theory to model the various benefit and cost streams 

over time. The underlying idea was that by reducing the investment of time or money required to complete a certain project 

or task (an “application”), The National Map could reduce the 

cost of implementing that application. Further, by providing 

data that were current and consistent across the Nation, the 

outcome of that program or task could be improved, resulting in an improved benefit of implementing it. Finally, these 

gains would likely stimulate innovation of new uses for spatial 

data and facilitate their rate of adoption by others. Together, 

these effects would produce a change in net benefit each time 

an application was implemented. Even if those changes were 

small for any one project, iterating them over time and space 

would ultimately bring substantial benefits to society that 

would be directly attributable to The National Map. 

However, finding or developing data to populate that 

economic model was not an easy task. The existing literature 

on the value of spatial data as used in real-world applications 

is not very extensive. Further, even that work which does exist 

does not analyze projects that are directly comparable to The 

National Map. Most previous work investigates the change in 

values when moving from paper maps or analog spatial data 

to a GIS or digital data. Those results had to be modified and 

adapted to address the question of the value of an improved 

data set and distribution system that will be provided by The 

National Map.

Because of these limitations, as well as the time and 

resources available to conduct the analysis, a full accounting 

of the likely costs and benefits of The National Map was not 

feasible. Instead, we developed a computational simulation 

model called “NB-Sim” into which the best estimates could 

be entered. The model simulates ranges of results that can be 

expected from each set of inputs. It allows rapid adjustment of 

the inputs so that more pessimistic or more optimistic beliefs 

can be simulated, and so that new results can be generated if 

more input data or evidence becomes available.

 Using extremely conservative assumptions for initial 

values and rates of change, the expected NPV of benefits of a 

fully implemented version of The National Map are just over 

$2 billion (in 2001 dollars) in a projected product lifespan of 30 

years. The standard deviation around the NPV is $490 million, 

meaning the net benefits are most likely to fall between $1.07 

and 3.03 billion, the range covered by two standard deviations 

(approximately the 95-percent confidence interval). Despite 

significant outlays over 10 years to develop The National Map, 

the project breaks even in year 14, shortly after its completion. 

The sensitivity analysis we conducted showed that these 

baseline results are robust to even dramatic changes in one or 

more of the input values. Several model parameters could be 

doubled, halved, or eliminated without bringing substantial 

changes. However, the results are quite sensitive to changes in 

certain other variables. The most critical variables were the:

• Average change in the net benefit of an application 

implementation as a result of data from The National 

Map.

• Rate of innovation of new applications.

• Amount of cumulative diffusion of data from The 

National Map and of the new and existing applications 

those data can inform.

This policy analysis resulted in a rigorous framework 

with which to evaluate the expected net benefits to society of 

a distributed database of digital spatial data that will be The 

National Map. Confidence in these expectations is limited by 

the small quantity of rigorous analytical work that puts credible numbers on the value in use of digital spatial data and the 

frequency and pervasiveness of applications using those data. 

The fields of geography, information technology, and spatial 

statistics would all benefit greatly from further research into 

these sorts of issues. 

We recommend that the USGS take the lead in conducting prospective studies of the type, quantity, costs, and 

benefits of using digital spatial information. This would be 

a most opportune time to develop an empirical study in a 

“pre-National Map” situation of how data was used, what 

those uses cost, and what benefits they brought. Then, as The 

National Map develops and starts serving data, follow-up studies could measure changes to those cost and benefit profiles. 

This would allow the inclusion of more credible numbers into 

the simulation model, thus simultaneously providing project 

tracking and evaluation, decision support for future National 

Map implementation steps, and evidence of the continued 

importance of Federal spatial-data programs.

References Cited

Alexander, Timothy M., 2002, Measuring the value of geospatial information—a critical and doable challenge: Washington D.C., TMA Associates, [unpublished].
A Cost-Benefit Analysis 15

Alexander, Timothy M. and Schmidt, Nicholas, 2002, The one 

percent factor: Imaging Notes, November/December 2002.

Baltimore County Office of Information Technology, 2001, 

Analysis of impact of GIS to Baltimore County: [online at 

http://aria.arizona.edu/slg/GISCostBenefit.pdf].

Boardman, A.E., Greenberg, D.H., Vining, A.R. and Weimer, 

D.L., 1996, Cost-Benefit Analysis—Concepts and Practice: 

Prentice-Hall, Upper Saddle River, NJ.

Bernknopf, R.L., Campbell, R.H., Brookshire, D.S., Shapiro, 

C.D., 1988a, A probabilistic approach to landslide hazard 

mapping in Cincinnati, Ohio, with applications for economic evaluation: Bulletin of the Association of Engineering Geologists v. 25, p. 39-56.

Bernknopf, R.L., Brookshire, D.S., Campbell, R.H., Shapiro, 

C.D., 1988b, An economic and geographic appraisal of a 

spatial natural hazard risk—a study of landslide mitigation 

rules: Environment and Planning v. 20, p. 621-631.

Bernknopf, R.L., Brookshire, D.S., Soller, D.R., McKee, M.J., 

Sutter, J.F., Matti, J.C., Campbell, R.H., 1993, Societal 

value of geologic maps: U.S. Geological Survey Circular 

1111.

Bond, D., 2000, GIS and spatial analysis in urban and regional 

research: Cities and Regions, GIS Special November 2000 

[online at http://www.car.busmgt.ulst.ac. uk/papers/gis.pdf].

Center for Technology in Government, 1995, Sharing the 

costs, sharing the benefits—the NYS GIS cooperative project: New York State Department of Environmental Conservation Center for Technology in Government Project Report 

95-4.

Croswell, Peter, Fries, J.T., Lambert, S.C., 2003, Status and 

potential role of state, tribal, regional, and local public entities in The National Map program: PlanGraphics, Inc. and 

Kentucky Governor’s Office for Technology.

Dickinson, H.J., Calkins, H.W., 1989, The economic evaluation of implementing a GIS: International Journal of Geographical Information Systems, v. 2, p. 307-327.

Federal Geographic Data Committee, 2004, National Spatial 

Data Infrastructure Page: [online at http://www.fgdc.gov/

nsdi/nsdi.html].

Fries, J.T., Metcalf, Annie, Swain, Lisa, Vincent, Andrew, 

2002. Executive summary of the Ohio spatial data cost-benefit analysis: PlanGraphics, Inc., and Booz Allen Hamilton, 

Inc., Frankfort, Ky and McLean, Va.

Gillespie, Stephen R., 1994, Measuring the benefits of GIS 

use—Two transportation case studies: URISA Journal, v. 6, 

p. 62-67.

Gillespie, Stephen R., 1997, A model approach to estimating 

GIS benefits: U.S. Geological Survey [unpublished].

Gillespie, Stephen R., 2000, An empirical approach to estimating GIS benefits: URISA Journal, v. 12, p. 7-13.

Hardwick, P., Fox, B., 1999, Study of potential benefits of Geographic Information Systems for large fire incident management, prepared for USDA Forest Service Region 5, Fire and 

Aviation Management: Pacific Median Resources, [online at 

http://www.fs.fed.us/fire/planning/nist/GIS_study.pdf].

Hwang, G-H, Diffusion of information and communication of 

technologies and changes in skills: Sussex University Science and Technology Research (SPRU) Paper no. 48, 25 p. 

Johnson, D.D., Craig, W.J., 1997, GIS in Dakota County—

benefits add up: Minnesota Counties, 5 p.

Korte, G., 1996, Weighing GIS benefits with financial analysis: GIS World, v. 7, p. 48-52.

Ledbetter, M., Hunt, C., Anderson, D., 1997, Blueprints for a 

citywide GIS: GIS World, v. 11, p. 62-65.

Lennstrand, B., 1998, Diffusion of information and communication technology to households—How come it goes 

so slowly when it goes fast? Twelfth Biennial Information 

Technology Conference, Stockholm, Sweden, p. 1-16. 

Lerner, N.B., 1994, Quantifying GIS benefits—a bottom-up 

approach that builds management confidence: URISA journal, p. 53-59.

McInnis, L., Blundell, S., 1998, Analysis of Geographic Information Systems (GIS) implementations in state and county 

governments of Montana: report prepared for The Montana 

Geographic Information Council [online at http://www.

nysgis.state.ny.us/montana/montana.htm].

Montgomery County Council’s Management and Fiscal Policy 

Committee, 1999, Geographic Information System cost/

benefit assessment report: Montgomery County, Md [online 

at http://www.mc-mncppc.org/gis/gisbenefits.pdf].

National Research Council of the National Academies, 2003, 

Weaving a National Map: Washington, D.C., National 

Academies Press.

Oxford Economic Research Associates Ltd. (OXERA), 1999. 

The economic contribution of the ordinance survey of Great 

Britain: OXERA, 50 p. [online at http://www.ordsvy.gov.

uk/literatu/external/index.htm]

Pistorese, T.A., 1994, Actual benefits realized through implementation of a GIS: AM/FM, 409-417.

Price-Waterhouse, 1995, Australian land and geographic data 

infrastructure benefits study: Price-Waterhouse Economic 

Studies and Strategies Unit, study conducted for the Australia-New Zealand Land Information Council [unpublished].

Prisley, S., Mead, R., 1987, Cost benefit analysis for Geographic Information Systems. Proceedings of GIS ’87—
16 The National Map

American Congress on Surveying and Mapping: American Society for Photogrammetry and Remote Sensing, 

Bethesda, Md, p. 29-37.

Rogers, E.M., 1962, Diffusion of innovations: The Free 

Press.

Sears, Garry, 2001. Geospatial Data Policy Study, prepared for 

GeoConnections Policy Advisory Node: KPMG Consulting, 

Ottawa, Ontario, Canada.

Somers, Rebecca, 1999, Framework Data Survey Preliminary 

Report: Federal Geographic Data Committee [online at 

http://www.fgdc.gov/framework/survey _results/readme.

html].

Tulloch, David and Fuld, Jennifer, 2001, Exploring countylevel production of framework data—analysis of the national 

framework data survey: URISA Journal, v. 13, no. 2.

United States Office of Management and Budget, 1992 

(revised 2003), Circular A-94—Guidance and discount rates 

for benefit-cost analysis of Federal programs.

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economic analysis, phase two: Reston, Va.

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government perspective: International Journal of GIS, v. 8, 

p. 545-565.
A Cost-Benefit Analysis 17

Appendix A. Formal Economic Theory

The National Map is an on-line database and provider of geospatial information that is a pure public good. As a public 

good, the intent of The National Map is to not exclude anyone from the benefits of its use and to supply geospatial information 

at virtually no cost. As shown in equation 1 in section 2, the net benefits of The National Map are specified as the value-in-use 

of the information available. The value of information is a function of the spatial and temporal applications of digital spatial 

data and is measured in terms of applications (that is, cost savings, value added, and (or) new uses). The willingness to pay for 

applications using The National Map is assumed to be increasing at a decreasing rate:

D6/)4.-	

DS AND D

6/)4.-	

DS  !	

where s is The National Map user-tiers, and VOITNM is the value in use of the geospatial data that is dependent on the tier of the 

user and the availability of The National Map:

 6/)4.-FS4.-	 !	

where s = g(TNM)

where TNM refers to The National Map program.

Note that the order of progression of tier advancement is tier 3 ‡ tier 2 ‡ tier 1, and that an increase in tier sophistication 

does in fact result in a tier name with a lower number (for example, a tier 2 county becomes a tier 1 county, thus increasing its 

capability but lowering the numeral in its name). 

The National Map is specified in both terms in equation A2 as (1) the increased efficiency from using The National Map in current applications (as a function of s = g(TNM) and (2) the innovative applications that arise from being a National Map partner f(TNM).

The willingness to pay for The National Map is a derived demand. To find the marginal willingness to pay for The National 

Map, totally differentiate VOI into its components:

D6/)4.-	 FSDSF4.-D4.-	 !	

D6/)4.-	

D4.-	  F

S D4.-	

D4.-	  !	 DS

D4.-	

 F

4.- D4.-	

D4.-	

The term ds

d(TNM) represents the advancement rate for The National Map users to change tiers. 

The term ∂(VOITNM)

∂(TNM) represents the fraction of the expected applications’ benefi ts associated with the implementation of The 

National Map and represents the derived demand for The National Map.

We assume that increased collaboration with the USGS—as in data-sharing partnerships—in The National Map increases 

the capability and facility with spatial data more quickly than a nonpartner data user. A partnership is defined as an acceptance 

of standards, data sharing, and so on. 
18 The National Map

Appendix B. Details of Data Gathering and Synthesis

Estimating Change in Net Benefit of Implementing an Application

In our analysis we have derived an average value of 

improved net benefit per application of spatial data from The 

National Map. This value was calculated using 40 available 

application (usage) values found in the literature (n = 36) 

and from phone surveys (n = 4). Most often, the available 

values were given in terms of an organization’s cost savings 

per year to perform a given task after the implementation of 

GIS (efficiency gains). We designated 17 tasks as “blanket” 

applications; those that could be performed with The National 

Map at its minimum resolution.6 An additional 10 tasks were 

designated as “quilt” applications; those that could be performed with The National Map at a resolution greater than the 

minimum. Finally, 13 applications were unspecified because it 

is unclear what resolution would be required to perform them. 

The average value per application per year of the “blanket” 

applications alone (n = 17) is ~$286,057. The average value 

per application per year of the combined “blanket” and “quilt” 

applications (n = 27) is ~$268,772, and the average value per 

application per year of all given application values (n = 40) 

is ~$257,536. Table B1 contains these applications and their 

values and calculations.

Although some of these applications may be performed 

only once or twice during a typical year (for example, voter 

redistricting, watershed analysis), others could be performed 

many times (for example, custom mapping). There is no clear 

way to determine how often an organization or an individual 

repeats an application. For the purposes of this analysis, it 

was appropriate to make a conservative estimate. Thus, we 

made the assumption that the average application is performed 

monthly. With this assumption we found a per-application 

value of $21,461 to $23,838 depending on which of the 36 

values are used. FGDC survey data indicates that the majority 

(70 percent) of the geospatial data used is of the lower resolution variety, which would correspond to the “blanket” minimum resolution data offered by The National Map (Stephen 

R. Gillespie, written commun.). As shown, the decision to use 

application values that we have designated as “blanket” or 

“quilt” has a minor to moderate influence on the final value in 

our analysis. 

In order to be even more cautious with our per application value, estimate we assume a value of $1,000 per application, or about 1

⁄21 to 1⁄24 of the larger estimates. This is done 

largely because this study attempted to measure change in 

value, not total value. Further, the values in the literature are 

almost all cost savings reported by sophisticated (tier 1 or 2) 

GIS users with at least a moderate level of infrastructure in 

their organization. Less sophisticated users with little or no 

infrastructure may derive a much smaller cost-savings benefit 

per application.

For example, suppose that in terms of operating costs 

and infrastructure, the average tier 1 user is essentially 100 

times larger than a tier 3 user (and therefore has that many 

more application occurrences) and 10 times larger than a tier 

2 user. The per application values for tiers 1, 2, and 3 would 

be $21,461 to $23,838, $2,146 to $2,384, and $215 to $238, 

respectively. A more reliable estimate of the differences 

between tier usage comes from the FGDC Survey of GIS data 

users, producers, and distributors that indicates that tier 1 has 

approximately 4 times as many application occurrences as tier 

2 (expenditures of $296,831 and $69,386, respectively) (Stephen R. Gillespie, 2003, written commun.). Further, assuming that tier 2 has approximately 4 times as many application 

occurrences as tier 3 gives per-application values of $21,461 

to $23,838, $5,365 to 5,960, and $1,341 to $1,490 for tiers 1, 

2, and 3 respectively. 

The value of $1,000 improvement per application implementation is an extremely conservative estimate and safe starting point for several reasons. The first and most obvious is that 

our own analysis based on the data from the literature implies 

higher values (~$1,300 to $24,000 per application) than are 

ultimately used in this analysis. A second reason is that we are 

making the estimate almost entirely based on reported costsavings (efficiency) benefits. There are few available data on 

effectiveness and innovation benefits, which—if available—

would surely elevate the average application improvement 

value and might boost it considerably. As The National Map is 

implemented and data is collected on users, our understanding 

of the value of applications will improve. 

Applications and Benefits

Some of the journal articles and technical reports we 

reviewed include a list of potential or actual applications that 

GIS is used for in particular organizations. We have compiled these lists to generate a larger, comprehensive list of 

GIS applications included in this report. The majority of the 

literature cites qualitative examples of the benefits of geospatial data. There are also rare quantitative data on the benefits 

of geospatial data available in the literature. The benefits are 

almost exclusively in the form of calculated cost savings to 

a particular organization. As far as our understanding of GIS 

applications and benefits is concerned, the most useful of 

the reports available in the literature is the Baltimore County 

Office of Information Technology (2001) cost-benefit analysis 

of the county’s GIS. The authors list the GIS cost savings to 

each department within the organization, as well as the cost 

6 The National Research Council’s analysis of The National Map, titled Weaving a National Map developed the “blanket vs. quilt” concept to illustrate the 

USGS goal of completing The National Map in a uniform and complete way 

across the entire country (the blanket), while providing more types and higher 

resolutions of data in certain places, where such data were available (the patchwork quilt). We examined primarily “blanket” applications in our analysis.
A Cost-Benefit Analysis 19

Application Blanket apps. 

(n=17) 

 Blanket and 

quilt apps. (n=27) 

 Blanket, quilt, and 

undefined apps. 

(n=40) 

Adequate public facilities $ 13,000 $ 13,000 $ 13,000 

Agriculture preservation $ 300,000 $ 300,000 $ 300,000 

Basic services mapping $ 9,800 $ 9,800 $ 9,800 

Development review and tracking $ 156,000 $ 156,000 $ 156,000 

Floodplain analysis $ 35,000 $ 35,000 $ 35,000 

Master planning $ 353,600 $ 353,600 $ 353,600 

Watershed planning/management $ 360,000 $ 360,000 $ 360,000 

GIS viewing application $ 312,260 $ 312,260 $ 312,260 

Automated mapping $ 31,200 $ 31,200 $ 31,200 

Custom cartography/spatial analysis $ 158,000 $ 158,000 $ 158,000 

Voter redistricting $ 16,200 $ 16,200 $ 16,200 

Map production $ 135,000 $ 135,000 $ 135,000 

Maps $ 1,086,667 $ 1,086,667 $ 1,086,667 

Geographic records $ 866,667 $ 866,667 $ 866,667 

Non-records related productivity improvements 

(crew delays, administration) $ 793,333 $ 793,333 $ 793,333 

One time savings (avoided system development, map 

backlog elimination) $ 180,000 $ 180,000 $ 180,000 

Garbage Truck Routing $ 56,250 $ 56,250 $ 56,250 

Average value blanket $ 286,057 

Alley reconstruction $ 15,000 $ 15,000 

Curb/gutter conditions/repair/permits $ 1,200 $ 1,200 

Drainage complaint investigation $ 6,000 $ 6,000 

Legislative analysis $ 44,200 $ 44,200 

Water and sewer administration $ 97,000 $ 97,000 

Crime analysis interface $ 20,800 $ 20,800 

911 address fi le interface $ 20,800 $ 20,800 

Generic mail labels $ 48,880 $ 48,880 

Census bureau appeal $ 1,800,000 $ 1,800,000 

Savings from improved electrical system analysis $ 340,000 $ 340,000 

 Average value 

blanket + quilt $ 268,772 

Building permit review $ 6,750 

Coastal Zone Management Program $ 643,700 

Lacquire $ 20,000 

Property analysis $ 6,750 

Rights-of-Way fee/maintenance $ 11,700 

Rural legacy $ 1,220,000 

Property tax assessment audit $ 61,680 

Code enforcement $ 45,400 

Zoning notifi cations $ 42,400 

Other $ 806,667 

Ambulance Dispatch $ 150,000 

Utility Distribution $ 2,534 

E-911 Mapping $ 27,000 

 

 Average value 

blanket +quilt+ 

undefi ned $ 257,536 

Table B1. Calculating the value of an application.

Key

Blanket

Quilt

Undefi ned
20 The National Map

savings that the organization’s GIS capability brings to particular applications.

A table summarizing the applications and, where available, 

data on the benefits of particular applications is provided at 

the end of this appendix (table B2). Those applications that are 

expected to be performed at the minimum resolution (“blanket” 

applications) guaranteed by The National Map are indicated in 

yellow along with estimated yearly cost savings. Those that can 

be performed at some higher resolution (“quilt” applications) 

are shown in blue along with estimated yearly cost savings. 

Other applications that The National Map may be frequently 

used for, but with no available benefits estimate are highlighted 

in green. Table B3 is simply a list of county-level applications 

collected by the Geography Discipline of the USGS.

Phone call data surveys

In order to supplement the data collected from the literature, we directly contacted several representatives of organizations that produce, distribute, and use geospatial data. Contacts 

included leads from pilot projects of The National Map, USGS 

State liaisons, USGS geospatial data contractors, and other 

sources of information recommended to us by our contacts.

In our conversations with each representative, we were 

particularly interested in collecting quantitative data on the 

following topics to aid us in our benefits estimation:

• Usage levels.

• Applications (uses) of the data.

• Efficiency and effectiveness benefits.

Usage

Respondents gave several different units of measure for 

usage levels of their geospatial data (for example, hits/day on 

a website, number of data files downloaded, number of data 

licenses purchased). In counties/regions with sophisticated 

GIS capability (such as Clark County, Nevada) websites 

receive a lot of traffic, often thousands of hits per day. In 

smaller counties with limited GIS capabilities, usage levels 

are, as expected, much lower. 

Applications

The respondents provided several examples of geospatial-data applications. These include web interfaces that allow 

users to generate and print their own maps or other data plots, 

routing applications, environmental assessment uses, billing and addressing applications, and others. The applications 

listed by the respondents do not constitute an exhaustive list; 

rather it should be viewed as a set of examples. 

Benefits

As in the literature, contacts rarely provided quantitative 

data regarding the benefits of geospatial data to their organization. However, many contacts implied that the use and production of geospatial data has improved their organization and 

were able to give anecdotal examples to illustrate the benefits. 

These examples ranged from several thousand to hundreds of 

thousands of dollars in benefits, but, again, were not based on 

any rigorous analysis.
A Cost-Benefit Analysis 21

Application

Blanket

Quilt

Est. Ann. 

Benefi t 

(anecdotal)

McInnis and Blundell, 1998

Property maps N Y

Disaster and emergency planning Y Y 

Automated tax assessment using other data sets such as soil, topography, and 

climate to describe land productivity

Y Y

Right-of-way assessments Y

Internet property research N Y

Growth analysis Y Y

Wildlife habitat monitoring and protection Y Y

Resolving areas of public/private confl ict such as hunting and fi shing access Y Y

Tax assessment, including locating untaxed parcels N N

Establish institutional controls on land use near Superfund sites Y Y

Zoning/master planning Y Y 

Address information can be included in enhanced 911 N Y

Weekly ownership updates for county government Y

Septic permitting system N N

Automated property owner notifi cation N N

Automated permit and development tracking N Y

Volumetric analysis for coal mine reclamation N N

Reclamation plan analysis (for example, assessing revegetation potential based on 

slope, and vegetation type)

Y Y

Database of groundwater wells in the vicinity of coal mines N Y

Aesthetics (for example, view shed analysis for proposed mines) Y Y

Wildlife (for example, potential fi shery impacts of a proposed pipeline) Y Y

One-time applications (for example, calculating the volume of material to removed 

from a mine tailings pile)

Y Y

Watershed analysis Y Y

Interactive well-fi nder on the web N Y

Drought monitoring Y Y

Natural Heritage Program

Underground Storage Tank (UST) analysis N N

Road reports (updated data available at information kiosks and on-line) Y Y 

Road rating (condition of the road used to prioritize road repairs) Y Y

Rural addressing Y Y

Flood plain delineation Y Y

Automated land records searches N Y

Automated underground utilities information N

Environmental cleanup coverage Y Y

Baltimore County GIS

Accident location analysis Y Y

Address matching N Y

Address validation for Data entry N Y

Table B2. Applications and their estimated values (where available).

[Those applications that are expected to be performed at the minimum resolution (“blanket” applications) guaranteed by The 

National Map are indicated in yellow along with estimated yearly cost savings. Those that can be performed at some higher 

resolution (“quilt” applications) are shown in blue along with estimated yearly cost savings. Other applications that The National 

Map may be frequently used for, but with no available benefits estimate are highlighted in green]
22 The National Map

Adequate public facilities ? Y $13,000 

Agriculture preservation Y Y $300,000 

Alley reconstruction N Y $15,000 

Approved development locations Y Y

Assessor cards scanned N N

Basic services mapping Y Y $9,800 

Bridge inventory and inspections Y Y

Building permit review N ? $6,750 

Bulk trash routing Y Y 

Cadastral (property) map preparation updates N N

Capital project management N

Commercial land inventory N N

Commercial properties real estate database N N

Communication tower locations Y Y

Complaint tracking and response Y Y

Conservation master plan management Y Y

County water and sewer master-plan mapping N N

County-owned structures/space inventory N

Crime analysis Y Y

Critical area analysis Y Y

Curb/gutter conditions/repair/permits N Y $1,200 

CZMP application ? ? $643,700 

Data distribution applications Y Y

Data maintenance applications

Data quality control applications

Data query and display application Y Y

Demographic analysis ? ?

Detour plans Y Y

Development review and tracking Y Y $156,000 

Districting Y Y

Down zoning

Drainage complaint investigation N Y $6,000 

Easement mapping

Economic development site selection Y Y

Functional area/program

Engineering design/studies Y Y

Enterprise zones

Environmental investigation review Y Y

Facilities management N Y

Flood control/inspections N Y

Floodplain analysis Y Y $35,000 

Forest management plan Y Y

Future water, sewer, storm drain, roads, and water tank mapping Y Y

Grinder pump locations Y Y

Growth management Y Y

Gunpowder watershed ecological model Y Y

Hazmat tracking Y Y

Hydrologic Modeling Y Y

Internet site posting

Application

Blanket

Quilt

Est. Ann. 

Benefi t 

(anecdotal)

Table B2. Applications and their estimated values (where available)—Continued.
A Cost-Benefit Analysis 23

Application

Blanket

Quilt

Est. Ann. 

Benefi t 

(anecdotal)

Table B2. Applications and their estimated values (where available)—Continued.

Investigation of surplus property N N

Lacquire ? ? $20,000 

Land acquisition databases Y Y

Land use analysis Landfi lls and recycling facilities management Y Y

Legislative analysis ? Y $44,200 

Management of the Chesapeake Bay Program Y Y

Master planning Y Y $353,600 

Master roads inventory/street segment integration N Y

NPDES stormwater management N Y

Nutrient reduction strategies

Open space analysis Y Y

Park development siting Y Y

Patron analysis N N

Pavement cuts permits N N

Pavement marking inventory (re-stripping) N Y

Preliminary alignment studies N

Property analysis N Y $6,750 

Public access Y Y

Public works maintenance Y Y

Repaving support N

Reservoir profi les N Y

Rights-of-way fee/maintenance ? Y $11,700 

Routing Y Y 

Rural legacy ? ? $1,220,000 

School location mapping Y Y

Shoreline land use study Y Y

Sidewalk inventory/repair N Y

Signal inventory/design N Y

Site analysis/plan development N Y

Smart growth Y Y

Snow removal/routing issues Y Y

Solid waste collection routes Y Y

Standardized map production Y Y

Storm drain culvert studies N Y

Street naming Y Y

Street sign inventory N Y

Street sweeping routing Y Y

Streetscapes investigation

Study area maps Y Y 

Traffi c calming

Truck traffi c routing Y Y 

Utilities key sheet mapping Y Y

Utilities maintenance programming N Y

Utilization of planimetric/topographic map in lieu of surveys Y Y

Vacant land analysis Y Y

Water and sewer administration N ? $97,000 

Water and sewer amendment process N N

Water and sewer pumping stations Y Y

Water quality monitoring N
24 The National Map

Watershed planning/management Y Y $360,000 

Work order management N

Zoning-hearing case development and analysis 

Zoning layer Y Y

Zoning review cases Y Y

Blueprint for a citywide GIS (Scottsdale), 1997

GIS viewing application Y Y $312,260 

Automated mapping Y Y $31,200 

Crime analysis interface N Y $20,800 

911 address fi le interface N Y $20,800 

Property tax assessment audit N $61,680 

Code enforcement N $45,400 

Zoning notifi cations $42,400 

Generic mail labels N Y $48,880 

Custom cartography/spatial analysis Y Y $158,000 

Census bureau appeal N Y $1,800,000 

Voter redistricting Y Y $16,200 

USFS Large Fire Incident Management, 1999

Location of camps Y Y

Division assignments

Transportation logistics Y Y

Perimeter maps every 6 hours Y Y

Fire intensity information N Y

BAER mapping

Plan implementation

Suppression damage locations Y Y

Structure protection Y Y

Resource determination Y

Line construction Y

Tactical design Y Y

Perimeter maps every hour Y Y

12 hr. expected situation report

Strategy development Y Y

Perimeter maps every 3 hours Y Y

Retardant and water dumps Y Y

Air operations

Alternative creation 

Fire modeling Y Y

Johnson and Craig, Dakota County GIS 1997

Condemnations N Y

New Library Siting Y Y

Transit Scheduling N Y

Highway Mapping Y Y

Pesticide education

Selling Tax Forfeit Properties N Y

Assisted Living Planning N

Mosquito Control N Y

Traffi c Planning Y Y

Application

Blanket

Quilt

Est. Ann. 

Benefi t 

(anecdotal)

Table B2. Applications and their estimated values (where available)—Continued.
A Cost-Benefit Analysis 25

County GIS Applications

A through E E through P P through Z

Accident Location Analysis Easement Mapping Public Works Maintenance

Address Matching Economic Development Site Selection Repaving Support

Address Validation for Data Entry Engineering Design/Studies Reservoir Profi les

Adequate Public Facilities Enterprise Zones Rights-of-Way Fee/Maintenance 

Agriculture Preservation Environmental Investigation Review Routing

Alley Reconstruction Facilities Management Rural Legacy

Approved Development Locations Flood Control/Inspections School Location Mapping

Assessor Cards Scanned Floodplain Analysis Shoreline Land Use Study

Basic Services Mapping Forest Management Plan Sidewalk Inventory/Repair

Bridge Inventory and Inspections Future Water, Sewer, Storm Drain, 

Roads and Water Tank Mapping Signal Inventory/Design

Building Permit Review Grinder Pump Locations Site Analysis/Plan Development 

Bulk Trash Routing Growth Management Smart Growth

Cadastral (Property) Map Preparation 

Updates

Gunpowder Watershed Ecological 

Model Snow Removal/Routing/Issues

Capital Project Management Hazmat Tracking Solid Waste Collection Routes

Commercial Land Inventory Hydrologic Modeling Standardized Map Production

Commercial Properties Real Estate 

Database Internet Site Posting Storm Drain Culvert Studies

Communication Tower Locations Investigation of Surplus Property Street Naming

Complaint Tracking and Response Lacquire Street Sign Inventory

Conservation Master Plan Management Land Acquisition Databases Street Sweeping Routing

County Water and Sewer Master Plan 

Mapping Land Use Analysis Streetscapes Investigation

County-Owned Structures/Space Inventory Landfi lls and Recycling Facilities 

Management Study Area Maps

Crime Analysis Legislative Analysis Traffi c Calming

Critical Area Analysis Management of the Chesapeake Bay 

Program Truck Traffi c Routing

Curb/Gutter Conditions/Repair/Permits Master Planning Utilities Key Sheet Mapping

CZMP Application Master Roads Inventory/Street Segment 

Integration Utilities Maintenance Programming

Data Distribution Applications NPDES Stormwater Management Utilization of Planimetric/Topographic 

Map in Lieu of Surveys

Data Maintenance Applications Nutrient Reduction Strategies Vacant Land Analysis

Data Quality Control Applications Open Space Analysis Water and Sewer Amendment Process

Data Query and Display Application Park Development Siting Water and Sewer Pumping Stations

Demographic Analysis Patron Analysis Water Quality Monitoring

Detour Plans Pavement Cuts Permits Watershed Planning/Management

Development Review and Tracking Pavement Marking Inventory (Restripping) Work Order Management

Districting Preliminary Alignment Studies Zoning Hearing Case Development and 

Analysis

Down Zoning Property Analysis Zoning Layer

Drainage Complaint Investigation Public Access Zoning Review Cases

Table B3. Alphabetical list of county-level GIS applications.
26 The National Map

Appendix C. STELLA Software and NB-Sim Model Details

The STELLATM software produced by High Performance 

Systems, Inc. (HPS)7 is a program that enables users to construct 

models of physical, social, or information systems and simulate 

changes in those systems over time. This is extremely useful 

software because, in addition to allowing fuzzy mental concepts 

to be translated into tangible systems diagrams and mathematical relationships, it can generate results of the system dynamics 

and allows a wide range of experiments with those systems. The 

differential equations and other mathematical complexities are 

transparent to the user, who only has to draw the simple model 

diagram and define a few mathematical relationships.

STELLA is based on the simple idea of stocks—those 

things that can accumulate—and flows—the fluxes into and (or) 

out of the stocks. The user draws boxes for stocks and thick, 

pipe-like arrows representing flows into and out of the stocks. 

The diameter of the pipes, and thus the rate of flow, can be 

adjusted by a third STELLA object, called a converter, which 

effectively opens or narrows the pipe and increases or decreases 

the rate of flow into or out of a stock. The operator sets initial 

conditions for stocks and flows and creates the mathematical 

relationships that underlie the relations between them. The 

inputs may be constant values, time-dependent functions, or 

functions that depend on values of other variables or inputs. The 

user also specifies the time-step interval with which to calculate 

the new values of model objects and the duration of the model 

run. Results are most commonly displayed as time-series graphs 

of the variables of interest but can also be shown as scatterplots, 

tables, or other types of data display.

We decided to use STELLA software as a way to operationalize the theoretical framework for benefits estimation. 

We realized that, because of the large amounts of uncertainty 

around the initial values and rates of change of such model 

inputs as the proportions of users in each tier, the number and 

type of applications, the costs and benefits of using spatial 

data in a set of applications, and the rates of application diffusion and innovation, we would not be able to produce a single, 

definitive answer to the net present benefit of The National 

Map. We knew that, despite a solid theoretical foundation for 

tracking the benefits and costs of developing and using spatial 

data provided through an on-line viewer and server, there were 

simply too many unknowns and too few empirical data sources 

to develop a single answer that would prove unshakeable.

The revised strategy aimed to provide a “best-guess” 

answer that would arise out of a rigorous analytical and 

mathematical simulation of the entire system, but also toward 

developing a simulation tool, so that readers could enter values 

and rates reflective of their own beliefs and observations of 

the system. We decided that STELLA would be the vehicle to 

create, run, and deliver that simulation. By giving the Office of 

Management and Budget a run-time version of the STELLA 

simulation model, into which examiners can load numbers of 

their own choice, we hoped to preempt concerns about bias 

or weaknesses in the data. We also hoped to demonstrate that, 

despite limitations in the data, the theoretical framework is 

rigorous and reliable, and can be used to track changes in the 

benefits and costs of The National Map over time, as better 

data and more observations become available.

Our task, then, was to convert the equations and variables 

in the theoretical framework into stocks, flows, converters, 

and equations in STELLA. In some cases, this was straightforward, but in other cases, there is not a direct one-to-one translation. The critical point, though, is to make sure that every 

variable and trend in equation 5 from section 2 is captured and 

represented somewhere in the model. Table C1 is a summary 

of each variable in the theoretical model’s equation 5 and its 

analog in the STELLA model. Table C2 contains the baseline 

values for each variable in the model and gives a brief reference to or explanation of its source.

NB-Sim Model Steps

We named the STELLA computational simulation model 

“NB-Sim” for “Net Benefit Simulator.” This section describes 

the steps NB-Sim takes in assessing the net benefits of The 

National Map system. The illustrations are screen-save images 

of the user interface page (fig. C1) and model wiring diagrams 

(figs. C2-C8). These figures should assist readers in following 

the processes involved in NB-Sim. 

Initially, there are 3,078 counties arrayed across 3 tiers of 

sophistication. The majority of these counties (65 percent) are in 

Tier 3, whereas only 5 percent are in Tier 1 counties. The other 

30 percent are in Tier 2. In the base case, none of these counties 

are users of data provided by The National Map because The 

National Map does not yet exist. The tiering structure described 

here relates to their existing capacity to use their own spatial 

data (or other non-National Map data), and the sophistication 

of the applications they do in a pre-National Map world. As 

The National Map gets implemented across the country, these 

counties may eventually become users of The National Map

data. The applications it enables them to do can begin to diffuse 

across the country, new innovative uses for spatial data will be 

developed, and those too will diffuse, and the USGS can be 

fairly credited with the increase in net benefits these users derive 

from the improved data and distribution system.

There is also a fourth type of spatial data user, the Federal 

Government, which is a separate tier-like entity unto itself. 

As The National Map develops, Federal agencies are likely 

to experience similar increases in the amount, quality, and 

efficiency of their spatial data use, and will receive many of 

the same benefits. However, because the Federal Government 

represents a single entity performing tasks with data that are 

fundamentally different in scope than local uses, it made sense 

to simulate their use separately. After describing how we have 

7 STELLATM is a product of High Performance Software, Inc. (URL http:

//www.hps-inc.com). Any use of trademarks in this paper is for descriptive 

purposes only and does not imply endorsement by the U.S. Government.
A Cost-Benefit Analysis 27

Description or explanation

Item in 

theoretical 

model

Analogous object(s) in NB-Sim Equation or initial value

Net present benefi t of The 

National Map program

NBTNM TNM Net Present Benefi t

Calculated as Total 

Present Benefi ts – Total 

Present Costs

Total program cost 

(calculated) CTNM

Total Present Costs Calculated from other 

inputs (next 4 lines)

Years Needed to Develop 10 years

Years of Development Funding 10 years

Annual Development Cost $25,000,000 / year

Maintenance Cost $5,000,000 / year (during

and after development)

Discount rate r Discount Rate 0.032/year (3.2%)

Time horizon of interest t = 1...T Run Specs...Length of Simulation 30 years

Calculation time increment Annual Dt 1 year

Tiers (or states) of users and 

applications s1, s2, s3

Tier1/2/3 The National Map Users; 

Tier1/2/3 Applications; Federal 

Applications

Defi nitional 

Initial tier distribution of all 

3,078 counties N/A Tier1/2/3 Initial Fraction 0.05; 0.30; 0.65

Number of applications 

available to each tier at each 

time

jst = 1...Jst

Tier1/2/3 Applications;

Federal Applications

Initially: 5, 8, 10; 25 

applications; calculated 

thereafter

Average net benefi t per spatial 

data application in each tier 

and time ⎯vst

Tier1/2/3 Mean NB per 

Application; 

Federal Mean NB per Application 

Drawn from Normal 

dist’n; user sets mean 

(default = $1,000/$10,000 

per app.); SD = 1/4th mean

Total number of applications 

that are implemented by each 

tier in each time period

st Tier1/2/3 AppUse; Fed’l App Use Tier1/2/3/Fed Application 

*Tier1/2/3/fed Users * Pst

Proportion of available 

applications that is 

implemented

Pst Not explicit. Calc’d as at right‡ Tier1/2/3 Diffusion * 

RANDOM(0,1)

Number of data users in tier at 

each time kst...Kst

Tier1 Users, Tier2 Users, Tier3 

Users

Initially: (3078 counties) 

* (Tier1/2/3 Initial 

Fraction); calculated 

thereafter

Rate of innovation of new 

apps

Ist

Tier1/2/3 InnovationRate; Federal 

Innovation Rate 

2% per user per year 

(same in all tiers)

Proportion of diffusion 

completed in each time step Dst Tier1/2/3 Diffusion Graphic function of TNM

completion percentage

Rate of one-tier User 

advancement in a time step Ast

Tier 2 To 1 Advancement, 

Tier 3 To 2 Advancement 

1% of tier per year

1% of tier per year

Benefi t of The National Map BTNM Total Present Benefi ts

Calculated as discounted 

sum of net benefi ts of 

apps. from 3 tiers

Table C1. Variables in the theoretical and simulation models and their baseline values.
28 The National Map

Parameter Value Source/Explanation

Annual development cost $25,000,000/yr. Exhibit 300

Annual maintenance cost $5,000,000/yr. Assumption

Years of development needed 10 years Exhibit 300

Years of development funding 10 years Exhibit 300

Annual discount rate 3.2% per year OMB Circular A-94

Tier 1 applications 5 Assumption; based on surveys 

and lit review

Tier 2 applications 8 Assumption; based on surveys 

and lit review

Tier 3 applications 10 Assumption; based on surveys 

and lit review

Federal applications 25 Assumption; based on surveys 

and lit review

Total counties 3,078 U.S. Census Bureau

Tier 1 fraction 5%

Judgment categorization based 

on FGDC Survey, interviews, 

literature review

Tier 2 fraction 30% As above

Tier 3 fraction 65% As above

Tier 3 to 2 advancement 1% per year

Conservative estimate from rates 

of spread of other technologies 

and industries

Tier 2 to 1 advancement 1% per year As above

Tier 1 mean net benefi t/application $1,000/ 

application

Mean of the applications we 

found

Tier 2 mean net benefi t /application $1,000/ 

application As above

Tier 3 mean net benefi t /application $1,000/ 

application As above

Federal mean net benefi t application $10,000/ 

application As above

Standard deviation for normal distribution 0.25 of mean Assumption & experimentation

Tier 1 diffusion rate Max = 90% Shape from literature; values are 

assumed

Tier 2 diffusion rate Max = 72.5% As above

Tier 3 diffusion rate Max = 49% As above

Tier 1 innovation rate 2%/yr Assumption; based on other 

technologies

Tier 2 innovation rate 2%/yr As above

Tier 3 innovation rate 2%/yr As above

Federal innovation rate 2%/yr As above

Federal emergency application net benefi t $1M / event Extrapolation from history

Emergency application occurrence rate 2% of years Assumed, random draw

Table C2. Default NB-Sim model inputs and their explanations.
A Cost-Benefit Analysis 29

modeled what users in tiers at the county level experience, we 

will describe the analogous Federal transitions.

The development of The National Map, as well as standardizing and updating the data it contains, will take time, so 

the benefits alluded to above do not begin to accrue right away. 

Rather, there is first a process by which the “Counties” become 

“TNM Users.”8

 As is the case throughout NB-Sim, the three 

tiers are handled separately so that the user may assign tierspecific initial values and rates of change (we decided to use 

the same values in each tier, but the model allows for different 

inputs than our baseline). The net benefits from each tier are 

summed at the end, but maintaining separation until then allows 

more flexibility and specificity in modeling. Note that the discussion below describes mainly tier 3, but conceptually identical 

processes take place in tiers 1 and 2. This transition between 

states is modeled as a flow between two stocks—“Counties” 

and “TNM Users.” The flow is dependent on time elapsed and 

on the proportion of The National Map that is completed at that 

time step. At this stage, the choices of the simulation operator 

in defining The National Map program are critical (to avoid 

confusion with the term for counties that apply data from The 

National Map, called “TNM Users” or “Users,” we refer to 

people running simulations with NB-Sim as “operators”). The 

operator works with the NB-Sim interface page (see fig. C1) to 

make simulation decisions as described below.

On the interface page, the operator inputs values for the 

“Years Req’d to Develop TNM” and the “Actual Years of TNM 

Dev’t.” These are not the same thing. The former is a best 

estimate of how long The National Map would take to be fully 

constructed given the operator-determined annual funding level; 

the latter is an expression of belief about how many years of 

funding it will actually receive. From these two settings, NBSim will determine a “Final Development Proportion,” which 

is simply the ratio of years funded to years required. This is the 

percentage of The National Map that will be achieved at the end 

of the time period for which development funding is achieved. 

NB-Sim also calculates an “Annual Implementation Rate,” 

which is the ratio of Final Development Proportion to the Years 

of Development Funding. This essentially takes that development which will be funded and spreads it out over the appropriate time frame. Finally, NB-Sim calculates a running total of 

“TNM Percent Complete” that is updated each year. This comes 

from iterating the Annual Implementation Rate each year and 

calculating how much of TNM is actually in place at the end of 

each time step, capping this at the Final Development Proportion.9

 Figure C2 shows these modeling steps.

The transition from a “Tier 3 County” to a “Tier 3 TNM 

User” is accomplished using the variables described in the 

8 Note that throughout this section, we use the acronym “TNM” for The 

National Map because STELLA allows a limited number of character spaces to 

name its model objects. The acronym is used only in reference to model objects, 

and not to the actual The National Map.

Figure C1. The user interface in the computational model, NB-Sim. 

9 Incidentally, the operator also sets an “Annual Development Funding” and 

an “Annual Maintenance Funding” to represent the number of dollars that will 

be spent in first the development stage and then the maintenance stage of The 

National Map. From these two inputs, combined with the Years of Development 

Funding and the operator-selected Discount Rate, STELLA develops an annual 

cost and a running total of program costs discounted to present value. 
30 The National Map

above paragraph. This flow from the County stock to the 

TNM User stock proceeds at a rate dependent on the number 

of counties in the Tier 3 Counties stock and the fraction of 

The National Map that has been completed at each point in 

time. Each year, The National Map is advanced toward its 

completion by including more of the country’s spatial data in 

its database, and, gradually, more counties make the transition from Counties to TNM Users (see fig. C3). When the 

Final Development Proportion has been reached, this flow 

stops, and any counties left in the County stock stay there, 

never to have The National Map vision realized in their 

county. If The National Map is funded for as many years as 

the operator believes it will take to develop, then adequate 

time exists for all counties in all three tiers to be converted 

to TNM Users. Funding TNM for fewer years than necessary 

will not allow it to be fully developed in all places, and thus 

Figure C2. Wire diagram illustrating underlying dynamics of the NB-Sim model. Operator inputs for 

years and amounts of funding for The National Map (TNM) (Annual Maintenance Funding, Annual 

Development Funding, Actual Years of TNM Dev’t, and Years Req’d to Develop TNM) allow calculation 

of Annual Costs, implementation rate (Annual Implem Rate), and project completion fractions (TNM 

Percent Complete).

Figure C3. Wire diagram illustrating underlying dynamics of the NB-Sim model. As The National Map

(TNM) is built, counties in each tier become TNM Users. Subsequently, some Users advance upward 

through the tier system. The rates of these transitions are determined by the annual implementation 

rate (Annual Implem Rate) and final development proportion (Final Dev’t Prop’n).
A Cost-Benefit Analysis 31

fail to change all Counties to Users, and to reap many of the 

benefits The National Map could eventually provide.

The discussion thus far has centered on the rate of The 

National Map implementation and the gradual inclusion of 

the data from across the entire Nation into the data distribution 

system. This is the first step in the larger task of estimating the 

total benefits of The National Map. As soon as this process 

begins, several others begin and proceed simultaneously, 

overlapping throughout the entire process. This is properly 

representative of reality, as no one is likely to wait until The 

National Map is completely implemented everywhere before 

starting to use the data in places where it exists. The following 

paragraphs describe these other processes covered by NB-Sim.

Once a group of Tier 3 TNM Users exists, the members of 

the group may begin using The National Map data in a set of 

existing applications. There is a similar, though smaller, pool of 

existing Tier 2 Applications and an even smaller pool for Tier 1 

Applications. The initial sizes of these pools of existing applications can be entered into the model. In each time step, some 

TNM Users will implement some of these Applications. It may 

appear that an estimate of total annual application utilization 

could be obtained simply by multiplying the total number of 

Users by the number of available application implementations 

within each tier, but this would be incorrect.

First of all, not every TNM User knows of every possible application. They will need time to develop both the 

awareness and the organizational commitment to implement 

certain applications. We have modeled this time lag as Diffusion. Based on concepts found in the academic and technical 

literature, we have modeled Diffusion as being a sigmoid 

curve with cumulative diffusion plotted as a function of time. 

The curves first increase slowly, then more rapidly, and then 

level off as The National Map achieves completion (see fig. 

C9). The graphs of cumulative Diffusion are not rates-perunit-time, but can instead be thought of as a degree of “market 

penetration” of the set of applications. Even though applications exist and The National Map provides data to drive them, 

it takes time and a fairly complete National Map for these 

applications to see widespread use across the country. We 

expect that the more complete The National Map is at any 

point in time, the more quickly and completely applications 

will diffuse across the Nation. We also expect that Tier 1 TNM 

Users will adopt new applications sooner and more commonly 

than Tier 2 TNM Users who in turn are faster and more complete than Tier 3 TNM Users. Alternative rates of tier-specific 

Diffusion can be redrawn on a graph of Diffusion versus TNM 

Percent Complete on the operator interface page. 

Note that this concept of Diffusion is separate from the 

transition from County to TNM User described above. That 

transition was about inclusion of location-specific data into The 

National Map system; whereas the diffusion process is about 

information awareness, organizational inertia, and the general 

notion that even most ready and capable data users will still take 

time to implement changes in their use of technology. 

As alluded to above, and shown in figure C4, multiplying the numbers of TNM Users and Applications in each tier 

together to yield a number of implemented Applications is too 

simple of a solution. Instead, this product must be multiplied 

by the cumulative Diffusion (a fraction between 0 and 1) to 

represent the availability of all applications across the country. 

This fraction comes from graphic operator-determined inputs 

like that shown in figure C9.

However, even with incorporation of Diffusion, this 

result would be an overstatement of application use. Not every 

application is needed in every place. Examples of this location-specific need-limitation are that no snowplow routes need 

to be planned in Southern California and no hurricane evacuation routes need to be planned in North Dakota. Further, not 

every application needs to happen each and every year. This 

is a frequency-related need limitation. Given both of these 

limiting factors on the overall rate of Application use in a tier, 

it is extremely unlikely that the fraction of available applications that is actually implemented in a single year would ever 

approach one. Even though the necessary data and capacities 

may exist, the “audience” for these applications in a given 

place in a given year is likely to be much closer to zero than 

to one. Further, this fraction of available applications that are 

implemented is likely to vary from year to year. 

To account for all of these issues, we created a process 

in NB-Sim to generate a random number between zero and 

one for each tier and each year during each model run. This 

random number is not drawn from the uniform distribution 

but from the inverse exponential distribution with an alpha 

parameter (α) of 6, which skews the distribution very close 

to zero. Most of the random draws are less than 0.25, and 

it is rare that more than 10 percent of the draws are greater 

than 0.50. On the basis of our interviews with state and 

county GIS managers and our review of the literature, we 

believe this to be a conservative estimate for need-limited use 

frequency of applications. This method also produces a variance in total application use from year to year as happens in 

reality. This process is not included in the interface page, but 

appears in the model diagram as “Need Limit Prop’n 1” (or 2 

or 3, depending on Tier).

To arrive at a final annual application use rate specific 

for each tier, we multiply this random number by the product 

of the previous multiplication step (which was Number of 

Applications * Number of TNM Users * Cumulative Diffusion fraction). We will show how this tier-specific Application 

Use number is used shortly. First, we show how the number of 

Applications and Users in each tier can increase over time.

Another process we modeled was that of Innovation of 

new applications, also illustrated in figure C4. Recognizing 

that the existence of The National Map is likely to spur the 

development of new uses for digital spatial data, we built in 

tier-specific rates of Innovation. The intrinsic Innovation rate 

is a constant with a default value of 2 percent per user-year. 

While this per-user rate of new application development is a 

constant, because the number of users in each tier increases 

over time as The National Map is developed, the actual number of new applications created each year increases. Innovation increases the number of available applications available 
32 The National Map

Figure C4. Wire diagram illustrating underlying dynamics of the NB-Sim model. The number of application implementations in a tier (Tier3 AppUse) is determined by the multiplication product of number of users of The National Map (TNM) (Tier3 TNM Users), number 

of applications (Tier 3 Applications), a cumulative diffusion fraction (Tier3 Diffusion), and 

a need limitation (Need Limit Prop’n 3). New applications are innovated at a constant rate 

(Tier3 Innovation Rate) multiplied by the number of users. 

Figure C5. Wire diagram illustrating underlying dynamics of the NB-Sim model. The application 

innovation, diffusion, and implementation processes for Tier 1 users of The National Map (TNM). Note 

the availability of applications from lower tiers (Tier2 Applications and Tier3 Applications).
A Cost-Benefit Analysis 33

• The numbers of available applications increases 

through the Innovation process.

• The Innovation, Diffusion and County-to-TNM User 

processes are all dependent on the degree of development of The National Map, which itself is dependent 

on time and the relative Actual and Needed funding for 

development.

• Many TNM Users advance upward through the tier 

structure, thus increasing the number of applications 

available to them.

• The number of TNM Users, the number of Applications, the amount of cumulative Diffusion, and a 

random number between zero and one are multiplied 

together to produce a total number of Application Uses 

occurring in each tier and time period.

As discussed in the analytical framework section, we 

decided it would be a fair assumption to multiply an estimated 

number of Application Uses in each tier and time by an average net benefit of National Map data as they would be used in 

those applications. The alternative would be to determine an 

exact dollar value for each application and summing those net 

benefits over the number of applications we could find. This 

did not seem realistic, so our averaging approach seemed the 

best option. 

Because the bundle of implemented Applications is likely 

to be different from year to year, it seemed inappropriate to 

use a constant value for net benefits. Drawing random values from a normal distribution centered on an accurate mean 

seemed more rigorous. The software features a built-in command to draw a random number from a normal distribution, 

wherein the programmer can select the mean and standard 

deviation. We developed an average value to use as the default 

mean for these random draws on the basis of survey results, 

interviews, and our literature review (see appendix B for 

more details on this choice). The standard deviation is set at 

one-fourth of the randomly drawn value. We felt this produced 

sufficient variation without causing the values for net benefits 

to skyrocket and plummet in consecutive years. Additionally, 

it does allow the potential for very small or, on rare occasions, 

negative average changes to per-application net benefits. The 

interface page allows the operator to alter the value for the 

mean net benefits by simply moving a slide bar on the screen. 

This value will not be used by the simulator every year, but 

will form the mean for a distribution from which the random 

draw will occur in each model simulation year.

In each tier, the number for Application Uses is multiplied 

by the randomly generated dollar value for Mean Net Benefit 

Per Application to produce a tier-specific annual dollar value for 

benefits of The National Map. These are summed across tiers in 

each year and discounted to present value by the user-adjustable 

Discount Rate. All of this creates a flow of Discounted Annual 

Benefits into a stock of Total Benefits in Present Value.

Earlier, the inclusion of a “fourth tier,” the Federal use 

of spatial data, was mentioned (see fig. C6). This simulation 

to each tier, but does not, on its own, increase the use rate of 

those applications. That is achieved through the Diffusion 

process and the transition from Counties to TNM Users. 

In theory, some time after development and spread of The 

National Map is complete, innovation could again decline as 

all practical and useful applications are developed and implemented. Conversely, innovation could proceed indefinitely 

(though likely at a slow rate). We did not find any literature 

that addressed the “back-end” of this innovation curve, nor 

did we claim to know a maximum number of applications 

that could possibly exist at each tier. Any argument we would 

make based on analogous technologies would be suspect, at 

best. Further, whatever the magnitude of this effect, it is likely 

to have its influence well after The National Map has achieved 

a positive net benefit. Therefore, to maintain a conservative 

approach to this simulation, we decided that innovation could 

continue for 20 years after development funding stopped. After 

that time, the flow of new applications would go to zero.

Importantly, not all Applications are available to all 

Users. Tier 3 TNM Users may only utilize Tier 3 Applications. 

Tier 2 TNM Users may utilize Tier 2 and Tier 3 Applications, 

and Tier 1 TNM Users have the technical expertise and data 

to make use of Applications from all three tiers. Figure C5 

illustrates the Application Use portion of the diagram. Comparing it to Tier 3’s Application Use diagram (fig. C4) reveals 

two more stocks, representing the additional availability of tier 

2 and 3 applications. A Tier 1 User thus has a greater number 

of potential applications to implement.

One process related to this idea is that of Advancement 

through the tier system, represented in figure C3. As The 

National Map provides low-cost data, a library of spatial data 

applications and (or) technical support and collaboration, it 

is likely that many TNM Users at tiers 2 and 3 will develop 

greater expertise with applications of digital spatial data. As 

they do, they will be likely to embark on increasingly sophisticated and complex applications. This could upgrade them from 

tier 3 to tier 2 or from tier 2 to tier 1. Thus, the number of Tier 

1 Users is expected to grow over the life of The National Map; 

the number of Tier 3 Users is expected to shrink; and Tier 2 

Users are expected to increase then decrease over time.

Both of these stages of Advancement increase the number of potential applications available to TNM Users. This 

Advancement process of TNM Users moving through the tiers 

is separate and distinct from that of Counties becoming TNM 

Users. On the user interface, there are operator-selected rates 

of TNM Users advancing in each time step. These are labeled 

“Tier 2 to 1 Advancement” and “Tier 3 to 2 Advancement.”

By way of summarizing the discussion thus far, in each 

tier and in each time step, we have:

• A process of Counties becoming TNM Users.

• TNM Users adopting some of a pool of existing applications.

• The breadth of use of this pool of applications 

increases through the Diffusion process.
34 The National Map

of Federal spatial data use is similar to what happens at the 

county level, with some notable exceptions. First, there is 

only one User (in our model, all Federal agencies fit into one 

“User” group), so no Diffusion is necessary. Second, there 

is no County-to-User transformation, so Federal users begin 

reaping benefits from The National Map right away. Also, 

the average change in net benefit of an application of spatial 

data is likely to be much higher at the Federal level than at the 

county level. The model uses a random draw from a normal 

distribution, and the operator has the ability to set the mean 

of that distribution, but the default is $10,000 per application 

implementation rather than $1,000. 

There is one other important difference. Recognizing 

that there are occasional national disasters and emergencies, 

the responses to which would involve widespread use of 

digital spatial and could be greatly improved by The National 

Map, we attempted to capture this dynamic in the model. The 

September 11, 2001, attack on the World Trade Center is an 

example of the sort of emergency use of spatial data of interest 

here. When such events occur, spatial data will be used in a 

way that is beyond the normal purview of any Federal agency. 

We wanted to capture these random non-annual benefits. 

To do this, NB-Sim generates a random number between 

1 and 100 for each model year. If the number is greater than 

98, an added net benefit of data use is added to the normal 

annual net benefits. That is, the default of this added benefit 

is $1 million dollars each year the emergency occurs (which 

should be in no more than 2 percent—or once every 50 

years—on average), but the operator has the ability to change 

this value as well. It would of course be possible to annualize 

these benefits, but averaging out these improvements does not 

capture the stochastic nature of this benefit stream.

The number of Federal “Application Uses” is multiplied 

by the randomly drawn “Average Net Benefit per Application 

Use” to yield a total “Federal Net Benefits” in each year. To 

this product, the “Net Benefit per Emergency Application” 

is sometimes added. The annual Federal Net Benefits are 

added to the total of Net Benefits from the three other tiers to 

produce an annual “Sum of Applications Net Benefits” in each 

year. Figure C7 illustrates this process.

While this is happening on the benefits side of the equation, a much simpler process is happening on the cost side (see 

fig. C8). The values for Annual Development Funding and 

Annual Maintenance Funding (set on the interface page...) are 

adjusted to present value by the same Discount Rate10 and flow 

into a growing stock of total costs in present value. For the 

initial years of The National Map, the model uses the figure 

for Development Funding plus the figure for Maintenance 

Funding, after which it switches to Maintenance Funding only, 

a much smaller figure. The switch between these two funding 

Figure C6. Wire diagram illustrating underlying dynamics of the NB-Sim model. The innovation and implementation of Federal applications (Fed’l Applications), including occasional “emergency” applications of 

spatial data (Random Emergency Generator).

10 By default, the discount rate was set at 3.2 percent per year based on 

OMB’s guidance for 30-year projects in real dollars. The user can adjust this 

rate with a slide bar on the user interface page.
A Cost-Benefit Analysis 35

Figure C7. Wire diagram illustrating underlying dynamics of the NB-Sim model. Numbers of implementations (Tiern

AppUse) in each tier are multiplied by a randomly drawn value (Tiern NB\Application) from a normal distribution 

centered on an operator-determined average (Tiern Mean NB per Application) to yield tier-specific net benefits 

(Tiern NBs). The three tiers and the Federal data uses are then summed to yield annual benefits (Sum of Apps’ NBs).

Figure C8. Wire diagram illustrating underlying dynamics of the NB-Sim model. Each year, the 

total benefits (Sum of Apps’ NBs) and annual program funding (Annual Costs) are discounted and 

flow into accumulating stocks of Total Present Benefits and Total Present Costs. The difference 

between them is the net present value of The National Map (TNM NetPresentValue).
36 The National Map

levels is set at 10 years as a default, but is adjustable by the 

operator on the interface page.

The difference between the two stocks, Total Present 

Benefits and Total Present Costs yields the Net Present Benefit 

of The National Map, also shown in figure C8. The graph 

on the interface page is set up to show these benefit and cost 

streams as they change over time. Where the curves cross, the 

net present benefit equals $0. The year in which this occurs 

can be read out at the bottom of the graph. Continuing the 

simulation beyond this point allows further surplus net benefits 

to accrue to society, the USGS, and The National Map.

There are other graphs available for viewing. The 

operator must simply click the small “dog-ears” in the lower 

left-hand corner of the yellow graph to flip through the other 

graphs. These other graphs can reveal a curve of net benefits 

over time, the number and distribution of TNM Users by 

tier, the number of Applications in each tier, the net benefits 

of applications by tier, and the degree of completion of The 

National Map.

It is also possible to conduct systematic sensitivity 

analyses quickly and easily. That topic is covered in appendix D.

Figure C9. The input window for graphically entering non-constant rates of change. In this case, Tier1 Diffusion is not a constant rate, but is dependent on how complete The National Map is (TNM_Percent_Complete).
A Cost-Benefit Analysis 37

Appendix D. Sensitivity Analysis

Methods

Because of the large ranges of uncertainty around 

many of the input variables to this analysis, it is important to 

measure how sensitive the results are to changes in the “best 

guess” estimates. To conduct this sensitivity analysis, we created “scenarios,” which are combinations of input variables 

that are different from the baseline scenario. In some scenarios, only one variable was changed relative to the baseline; in 

others, many were altered. We ran optimistic and pessimistic 

versions of each single-variable change and of many multiplevariable modifications.

As in the main body of the report, each model simulation 

runs for 30 years, because that is the most relevant time horizon of interest. Conservatively estimating that The National 

Map will take 10 years to develop and populate with data, 

many of the scenarios do not break even until around year 12 

or 16. After that, substantial positive net present values (NPV) 

are generally recognized, but the realm of information technology progresses so rapidly that assuming knowledge of benefits 

streams 40 or 50 years into the future seems unrealistic. The 

30-year analysis horizon seemed a fair compromise between 

these extremes.

Because the NB-Sim model contains several random 

components, a single run was insufficient to provide a result in 

which complete confidence could be placed. It was important 

to run a number of simulations and use NPV of benefits from 

each run to calculate the mean and standard deviation of NPV 

for that scenario. Similar mean and standard deviation calculations were included for the break-even time. Each scenario 

was run 50 times, to reduce the likelihood that a particularly 

high or low draw from any of the random model components 

would disproportionately affect the end results. The means and 

standard deviations are taken from these 50 runs.

Table D1 contains a list of the scenarios we have included 

in the sensitivity analysis. The table is organized along scenario 

number. Following each number, there is a description of the 

changes—again, relative to the baseline values—that were 

made. If no variable is listed in the description, it was no different than the base case. After the descriptions, columns list the 

means and standard deviations around the net present value and 

the break-even time (in years) of each scenario. There is also a 

column for the break-even rate, which is the percentage of runs 

in which a positive net benefit was achieved within the 30-year 

time horizon. If not all of the runs in a scenario show a positive 

net benefit, then no average break-even year can be calculated, 

which is why those columns are labeled with are labeled with 

“NA” in those scenarios. Figure D1 shows the mean NPV of 

each scenario with error bars representing the two-standard 

deviation range (roughly the 95 percent confidence interval) 

around those means.

In total, there were 60 scenarios simulated. These represent two sets of 30 scenarios that are identical except for the 

value entered for Annual Development Cost. Group 1 consists 

of 30 scenarios with the Annual Development Funding set 

at $25,000,000 per year. Scenario 1 is the baseline or “bestguess” scenario. The other 29 are described below. Group 2 

consists of the same 30 scenarios—with Scenario 31 being a 

“baseline” for this group—except funded for development at 

$50,000,000 per year. These two different values were chosen 

because, even though the original Exhibit 300 for the entire 

The National Map program asked for $25,000,000 per year for 

5 years, no one knows the exact cost of building a complete 

version of it. We wanted to compare many different input 

scenarios against cost figures that covered a wide range of possibilities. The two values we used were regarded as reasonable 

as any by the Implementation Team for The National Map.

Sensitivity Results Discussion

Table D1 and figure D1 reveal several important insights. 

First, most scenarios show a positive NPV, even those that 

are far more pessimistic than even the highly conservative 

baselines. Further, most scenarios in group 1 take between 10 

and 15 years to break even. Scenarios in group 2 take longer; 

generally 15 to 20 years. Looking more closely at the scenarios in group 1, with regard to the impact of specific variables 

or combinations, the changes relative to the baseline of $2.05 

billion and a 14-year break-even time are as follows:

• The Innovation rate matters quite a bit. Scenario 2 

assumes there is no flow of new applications into any 

of the tiers, and its NPV is negative in every run. If the 

Innovation rate is 5 percent instead of its baseline of 

2 percent (scenario 4), the NPV more than doubles to 

more than $5 billion, and the break-even time moves in 

by 3 years.

• Cumulative Diffusion also heavily affects the results, 

but not as much. Eliminating Diffusion (scenario 3) so 

that Users will only implement those applications they 

already do or that they create for themselves, drops the 

NPV to only $100 million. Conversely, making Cumulative Diffusion immediate and complete—so that all 

Users have immediate access to every application as 

soon as it is created—increases NPV by more than $1 

billion, and moves the break-even date in by 2 years 

(scenario 19). Keeping the sigmoid shape of Diffusion, 

but either halving (scenario 5) or doubling (scenario 6) 

its rate at each point, decreases or increases the NPV 

by about $1 billion.

• Scenarios 8 and 9 assume a constant total budget for 

development funding but either spread that amount out 
38 The National Map

Table D1. Sensitivity analysis scenarios and results. The 60 different scenarios are described and the mean and standard deviation of 

both the net present value (NPV) and the break-even time are reported, along with the percentage of each scenario’s model runs that 

achieved a positive NPV. Because of NB-Sim’s randomness, even a scenario with a positive NPV may not break even in every run.
A Cost-Benefit Analysis 39

Figure D1. Sensitivity analysis results; mean net present value by scenario. The 60 different scenarios 

were each run 50 times. Results from each scenario are plotted here, as the mean net present value 

(NPV, yellow diamond) and the two-standard-deviation range (approximately the 95-percent confidence 

interval; red bars). Note that not all scenarios reach a positive value.

Mean NPV's of Scenarios

-1

0

1

2

3

4

5

6

7

8

9

13579

11131517192123252729313335373941434547495153555759

Scenario Number

NPV (Billions of 2001 dollars)

over 20 years or compress it into 5 years. Essentially, 

these scenarios are a slower and a faster implementation, and it is clear that the faster The National Map

is developed, the sooner and larger its benefits will 

accrue. Scenario 8 drops NPV to less than $1 billion, whereas the faster implementation in scenario 9 

increases it to $2.6 billion. 

• The Initial Tier Distribution and Tier Advancement 

Rates do affect the results, but less dramatically than 

might be expected. Eliminating advancement (scenario 

10) reduces NPV by less than $100 million. Increasing 

the advancement rates to 5 percent per year (scenario 

11) brings an extra $700 million, but only moves the 

break-even date in by 6 months. Changing the Initial 

Tier Distribution to one-third of all counties in each tier 

(scenario 16) makes only a $500 million improvement. 

Putting all counties into tier 3 (scenario 15) causes a 

reduction of only a few million dollars; whereas starting all Counties in tier 1 (scenario 13)—an admittedly 

impossible starting point—doubles the NPV to more 

than $4 billion. Starting with all counties in tier 2 (scenario 14) brings an NPV of more than $3 billion.

• The Initial Number of Applications also fails to 

produce dramatic changes in the final results. Dropping the starting number to just 1 application per tier 

(scenario 17) brings a decrease of $50 million, whereas 

quadrupling the initial number in each tier (scenario 

18) increases NPV by about $200 million.

• The model treats Federal use of spatial data separately 

from county-scale use. To see if this modeling decision made a significant difference, all Federal applications net benefits per application use and innovation 

were eliminated (scenario 7). This brought a decrease 

of about $75 million, which did not indicate high 

sensitivity. Similarly, setting the additional benefit of 

a randomly generated emergency application to $50 

million, instead of its default of $1 million (scenario 

12), actually brings a lower NPV than in the baseline. 

It is about $50 million less, but the standard deviations 

are so much wider that these results are essentially 

equal. This apparent contradiction is explained by 

recalling that the need for the emergency application 

is randomly generated with a likelihood of 2 percent 

per year. Each scenario consists of fifty 30-year runs, 

and even a difference of one or two “emergencies” 

could explain this difference. The important insight is 

that this is a variable for which the exact value is not 

significant relative to other inputs.

• A very sensitive variable is the Increase in Average 

Net Benefit of an Application. In the baseline, each 

tier sees an additional $1,000 in net benefits each 

time an application is implemented, and the Federal 
40 The National Map

Government receives an improvement of $10,000 per 

application. If those values are halved (scenario 24) or 

doubled (scenario 28), the NPV drops to less than $1 

billion or increases to almost $5 billion.

• Two scenarios addressed the effects of partial development of The National Map. These examined what would 

happen if the program would need 10 years to develop 

at a certain funding level, but only receives 5 years of 

funding (scenario 22), or if it needed 20 years of funding 

but received only 10 years (scenario 23). Both of these 

scenarios broke even on average, but scenario 23 only 

achieved a positive NPV in 84 percent of the model 

runs. Also, the NPV dropped to $500 million and $100 

million in the two scenarios, underscoring the importance of funding The National Map to completion.

• We considered combinations of pessimistic changes 

in variables. Scenario 25 cuts the net benefits per 

application in half and eliminated tier advancement. 

This dropped NPV to $1.1 billion. Combining those 

changes with an Innovation rate of 1 percent (scenario 

26) brings the NPV down to $300 million. Those 

changes and lowering Initial Number of Applications 

in each tier (scenario 27) to 1 lowers the NPV to just 

more than $100 million. These results show that the 

positive NPV of The National Map is robust to even a 

combination of extremely pessimistic assumptions.

• We also combined pessimistic and optimistic changes in 

the same scenarios. Scenario 29 eliminated Innovation, 

but set the Initial Number of Applications to 50 in each 

tier and 100 at the Federal level. The increased applications were not truly sufficient to replace the lack of 

new applications, as NPV dropped to only $13 million. 

In scenario 30, The National Map could be built much 

more slowly—taking 20 years—but if the net benefit 

per application doubles to $2,000 (or $20,000 for 

Federal applications), the NPV is almost $2.3 billion. 

Compared to Scenario 8—the 20-year implementation 

version of baseline—this brings an NPV that is more 

than twice as large.

• Finally, we investigated two particularly odd scenarios. 

In scenario 22, we attempted to simulate alternative 2 from the original Exhibit 300. That alternative 

would begin to implement The National Map with no 

additional funding, but only through an internal shift 

of funding to that task. We assumed that would take 

50 years and adjusted the model accordingly. With all 

other values equal to baseline, the NPV decreased to 

$22 million. This is a very small number, but, because 

there was no initial investment made, the break-even 

year is year 1, when NPV’s are zero dollars because no 

additional money has been spent.

• In scenario 21, we removed all randomness from the 

model, made Diffusion immediate and complete, and 

removed the random emergency application completely. The Need Limitation Proportions were set 

at 15 percent for each tier, meaning that in each year 

15 percent of all diffused and available applications 

were implemented. That number was not based on any 

empirical belief and is perhaps overly optimistic. The 

net effect of these changes was to make every model 

run identical and the total benefits curve smooth, 

rather than kinked as it is in all other scenarios. The 

NPV jumped to $2.9 billion. Although it would be a 

mistake to put much faith in this result, though. If the 

Need Limit Proportion is 25 percent, then the NPV 

is more than $5 billion; if it is 5 percent, then NPV is 

only $700 million. In all other scenarios, this number is 

randomly drawn from a declining exponential function; 

here, that proportion is fixed. This non-random model 

is fundamentally different from the baseline or any 

other version of the model, and comparing them would 

probably not be fair or accurate.

In the group 2 scenarios, the general directions and the 

magnitudes of changes seem to be largely the same as in the 

group 1 scenarios, so that discussion will not be repeated here. 

It is important to note, however, that in some cases (scenarios 

33, 53, 56, 57, and 59), a scenario that produced a small, but 

positive, NPV in group 1 will bring a negative NPV in group 

2 because of the added development cost. In general, as noted 

above, the projected NPV was smaller and the break-even 

period came several years later.

The most important insight to be gained from this sensitivity analysis is that the exact values used in the default case 

are less important than recreating a fair representation of their 

interactions. Even with extremely conservative assumptions 

of initial values and rates of change, The National Map can be 

expected to bring about $2 billion in net benefits to users of 

the data it will provide. This exact value cannot be expected 

to be fixed, but seems relatively robust to major changes to all 

but the most sensitive variables.