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s 38 (2004) 47–63
Decision Support System0167-9
doi:10.
* C
378-54
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dasgup
Geospatial information utility: an estimation of the relevance
of geospatial information to users
W. Lee Meeksa,*, Subhasish Dasguptab
aGeospatial Intelligence Department, Veridian Systems Division, Veridian Corporation, Chantilly, VA, USAbManagement Science Department, MON 403, The George Washington University, 2115 G Street, NW, Washington, DC 20052, USA
Received 1 September 2001; accepted 1 March 2003
Available online 25 July 2003
Abstract
As the acquisition and use of information are costly, the optimal use of information involves economic tradeoffs. Therefore,
valuing information is attracting research and thought. However, till now, little attention has been paid to the geospatial
information domain, which is increasingly coming to the attention of decision makers seeking to improve decision models by
considering spatio-temporal factors. This paper proposes a metric called Geospatial Information Utility (GeoIU), which will
allow decision makers to assess the degree of utility of accessed geospatial data sets when making decisions that incorporate
those geospatial data and information. The GeoIU metric uses multi-attribute utility theory to assess, score, and weight metadata
queries run against geospatial data and information discovered in distributed sources.
D 2003 Elsevier B.V. All rights reserved.
Keywords: Information utility; Multiple attribute utility; Multi-attribute utility functions; Geographic information systems
1. Introduction particular time of day or attending an appointment
Geospatial data are data having geographic and
spatial orientations: data having some information
content that includes a location component. Often,
geospatial data and information also have a temporal
aspect, an example of which is change detection over
time. The term ‘spatio-temporal’ pertains to these
types of data. People make ‘place’ and ‘time’ deci-
sions everyday: choosing the best route to the neigh-
borhood grocery store given the traffic patterns at a
236/$ - see front matter D 2003 Elsevier B.V. All rights reserved.
1016/S0167-9236(03)00076-9
orresponding author. Tel.: +1-703-251-7491; fax: +1-703-
04.
ail addresses: lee.meeks@veridian.com (W.L. Meeks),
ta@gwu.edu (S. Dasgupta).
with your dentist are two simplistic examples [31]. It
has been estimated that nearly 80% of all data has a
location component [9]. Geospatial data are collected
and analyzed by organizations in order to solve a
broad array of public and private sector problems.
For example, epidemiologists have long studied the
case of Dr. John Snow who traced the 1854 outbreak of
cholera in London’s Soho district to a public water
pump on Broad Street by annotating on a street map
the home addresses or work locations of the sick and
dying. By correlating the locations and numbers of
cholera-infected patients, even though they were of
different social classes (and hence, different seemingly
unconnected lifestyles), Dr. Snow isolated the source
of the outbreak: the offending Broad Street water
W.L. Meeks, S. Dasgupta / Decision Support Systems 38 (2004) 47–6348
pump. After a test of the water revealed bacterium
responsible, he was able to convince skeptical offi-
cials to remove the pump’s handle so no one could
draw contaminated water from the pump. The results
were dramatic and immediate; the number of
cholera cases quickly dropped off [51].
Today, the use of spatio-temporal data and infor-
mation greatly improves decision-making in many
different fields [16]. Examples in government in-
clude health and safety, public works, recreation and
culture, land use and zoning, administration, finan-
cial operations, other management, and military and
intelligence support. Examples in business include
marketing support and planning, retailing, various
types of services, wholesaling and distribution, and
other broad technical planning and support in a wide
variety of industries [1]. Thus, spatio-temporal anal-
yses are now becoming part of rigorous analytical
frameworks for local, national, and global security
and business intelligence realms. When using spatial
and temporal information to improve decision mak-
ing, attention must be paid to uncertainty and sensi-
tivity issues [21]. Attention must also be paid to
spatial and temporal scales relevant to the decision
being supported [45] and to the quality and utility of
available data, with respect to the intended use(s) of
the data [42]. This last issue defines the core
problem geospatial information utility (GeoIU)
addresses: that decision makers collect and use geo-
spatial data of varying spatial and temporal scales
in order to improve decision making, but more
attention needs to be paid to finding appropriate
Fig. 1. A simplified proces
methods for assessing the utility of the geospatial
data being used [10].
Beyond the data issues are system ones. Geospatial
data are collected by various means, processed, and
stored, or disseminated for later use by geographic
information systems (GIS), which require geospatial
data for spatial or spatio-temporal analysis and pre-
sentation. GIS are considered to include data, hard-
ware, software, procedures, operators (i.e., analysts),
and analytical requirements (i.e., the problem needing
to be solved). GIS exist primarily to ingest, manipu-
late, model, analyze, display, and output value-added
geospatial data, information, and products in multiple
file formats for some information (e.g., display) or
decision-making (i.e., through analysis) purposes
[12,28,33]. Fig. 1 depicts a simplified process model
for GIS.
Burrough [13] provides several definitions of GIS
by focusing on different perspectives; a tool-box view,
a database view, and organization-based view. These
views comport nicely with a broad 2001 summary of
information systems research conducted by Orlikow-
ski and Iacono [43]: they report on tool, proxy,
ensemble, and computational views of information
technology. Burrough’s three views identify some of
the complexity of the evolving geospatial sciences
domain: (1) GIS provide tools for analysts and deci-
sion makers to access and analyze complex, tempo-
rally, and spatially oriented data in order to improve
decision making; (2) GIS operate on spatially oriented
data, yet geospatial data and spatial databases are
complex; and (3) organizations use GIS to improve
s model for GIS use.
W.L. Meeks, S. Dasgupta / Decision Support Systems 38 (2004) 47–63 49
product and/or service offerings, and to improve
decision making by incorporating factors in ways
not previously considered.
Following data and systems’ issues come issues
surrounding the data providers. Geospatial data and
information are produced by a wide variety of gov-
ernmental and commercial producers and vendors. For
several years, a niche geospatial community of pro-
viders and users has been dealing with the evolving
complexity of the use and analysis of geospatial data.
Only now are geospatial data and spatio-temporal
analyses migrating beyond domain- or industry-spe-
cific niches. Decision makers in many fields, indus-
tries, and organizations are now coming to grips with
the benefits and challenges inherent in the storage,
retrieval, transmission, and analysis of these types of
data. They are also becoming aware of the breadth of
analyses that are possible when information-rich geo-
spatial and spatio-temporal data are made available
[8,24,34]. A community of interest that has sprung up
to integrate advances in GIS and geospatial data into
matters pertaining to US-focused homeland security,
called Homeland Infrastructure Foundation Level Da-
tabase (HIFLD), is aggressively pushing the bound-
aries on issues related to GIS and geospatial data. At a
recent HIFLD conference (Nov 2002), the most im-
portant issues pertaining to the use of geospatial data
were identified to be: data type (i.e., form of the data),
data schema or structure, metadata (i.e., presence,
quality, completeness), pedigree (i.e., source), quality
(i.e., pertains to content, horizontal, and vertical
accuracy), and currency (i.e., age relative to use) [5].
Conferences and communities of interest such as these
encourage the convergence of data, GIS, providers,
and users.
For purposes of this paper, it is useful to broadly
classify GIS and geospatial data users into two broad
categories: (1) governmental users and (2) non-gov-
ernmental users. Governmental users are primarily
interested in public domain uses of geospatial and
spatio-temporal data: for example, military planners
may require highly accurate, very current digital data
sets for planning flight routes for cruise missiles.
Flight route planning requires digital elevation mod-
els to support terrain contour matching algorithms
within the missiles’ guidance modules. To optimally
employ so-called ‘‘smart weapons’’ such as these,
planners and targeters must have access to current,
high-quality digital data sets with minimal horizontal
and vertical accuracy errors. In order to reduce
operational risk in the development of missile flight
routes based on new digital geospatial data sets, the
‘pedigree’ or quality of the supporting data must be
assessed [34]. As mentioned previously, many other
public sector uses of GIS and geospatial data abound:
fuel modeling to predict and prevent wildfires, ca-
dastral records and land tax planning, information
visualization of municipal government services via
Web-based GIS applications, and many more. Each
of these uses of geospatial data has different require-
ments for data accuracy, currency, and form; some
applications have stringent requirements and others
less so.
Non-governmental users are primarily interested in
commercial or business intelligence analyses of geo-
spatial data and information. These users may also
have varying needs for highly accurate and current
data sets: for example, planners in cell phone compa-
nies may employ GIS technologies and data analyses
in order to determine optimal site locations of a new
array of digital wireless telephone signal towers. Their
analyses might focus on making maximum use of
both send and receive signal strengths vis-a-vis local
terrain limitations (e.g., received signal strength is a
function of transmitted power, number and locations
of transmitter towers, radio frequency line-of-sight
obstacles, etc.) in order to minimize the number of
towers needed while providing a guaranteed quality of
service for cell phone subscribers; using fewer, well
placed towers may mean lower operating costs and
higher operating margins. Similar to the missile flight
route problem, a wireless telephone tower location
analysis based on geospatial data of poor or uncertain
quality is subject to errors, which may roll through the
calculations, quite possibly resulting in improperly
located towers, reduced systems performance, higher
installation and operations and maintenance costs, and
unhappy customers.
While assessing the value of geospatial informa-
tion content is now attracting the interest of research-
ers, current theories and approaches for valuing
information concentrate on the estimation of the
content value of textual information [7]. For example,
popular search engines use several different evalua-
tion schemes such as keyword proximity, keyword
density, and synonym matching, among others, to
W.L. Meeks, S. Dasgupta / Decision Support Systems 38 (2004) 47–6350
estimate the quality of links and files returned from
Internet text searches. However, no such algorithmic
approaches exist for users, producers, and brokers of
geospatial information to estimate the value of the
geospatial information relative to the needs and uses
of geospatial users, producers, and brokers, nor to
dynamically perform these analyses rapidly, or in an
automated way. On the surface, text-oriented search,
retrieval, and valuation tools seem to have little use-
fulness and no analog in the geospatial domain. The
geospatial equivalent of a textual keyword or key
concept could be any spatially oriented feature com-
monly found within the geospatial data. Examples
include a road network or a single road, a bridge, a
stream, river, or other linear or area geographic feature
leading researchers to consider if the key ‘things’ to be
searched for are so different, what mechanisms must be
found or developed for performing search and retrieval
operations for geospatial data and information?
Considering both governmental and non-govern-
mental uses of geospatial data, the quality of the uses,
analyses, or decisions made based on available geo-
spatial data depends on the quality of the underlying
data being used. This paper outlines an algorithmic
approach to estimating the content value of geospatial
information and data sets for researchers and users of
this type of information.
Within the US government, the National Imagery
and Mapping Agency (NIMA) and the US Geological
Survey (USGS) are responsible for setting standards
and guiding the geospatial community (which in-
cludes all governmental and non-governmental enti-
ties) focused on government-related uses of geospatial
data [34]. The predecessor organization of NIMA, the
Defense Mapping Agency, had a long-established,
periodic product evaluation schema for evaluating
then-current products (i.e., geospatial data is provided
in many hard- (e.g., maps and charts) and soft-copy
forms (e.g., digital elevation models, raster map dis-
plays, vector map data sets)) based on published
product specifications. This approach to determining
product adequacy through performing product evalu-
ations from a producer’s point of view was flawed and
is no longer being used as official policy [42].
To determine the adequacy, relevance, and useful-
ness—herein defined as the ‘‘utility’’—of geospatial
information provided to consumers of the same within
the US government, geospatial products (i.e., both
hard maps and digital data sets) are presently assessed
as adequate in an adequate/not adequate binary ‘‘vac-
uum’’. That is, the geospatial information either meets
all predefined product adequacy standards and spec-
ifications for all content components, or it does not. If
it does not, then the intended consumers of these data
sets are not allowed access to them. This de facto
binary quality process is more about control than
access to data, and presumes:
(1) Information not meeting some predefined standard
has no value;
(2) All consumers of geospatial data essentially have
the same quality needs for all situations;
(3) Collectively, the producers of geospatial informa-
tion possess the same knowledge and awareness
of the consumers’ geospatial data and information
needs as the customers themselves and can
therefore evaluate the value of the geospatial
information provided to them [40].
As a result of this situation, two members of the
Veridian Corporation (including one of the authors)
were tasked under the auspices of NIMA’s Geospatial
Information Infrastructure Implementation Integrated
Process Team (GI3 IPT) to hypothesize an alternative
to the legacy product evaluation schema. The govern-
ment’s interest was two tracked: (1) perform a rapid
analysis of the theory needed to develop a proof-of-
concept prototype tool to automate geospatial infor-
mation utility (GeoIU) assessments, and (2) develop
the prototype. The work for the government was
performed during February to June 2001. This re-
search continues the theoretical analysis.
The robustness of the information age, including
activities such as mass customization of products and
information, is replacing mass production and mass
media as the preferred business value paradigms.
Government is learning from the business community
the lesson that one size does not fit all: that everything
is susceptible to change over time. The same is true of
geospatial information needs: as geospatial information
producers adopt a stronger customer orientation, they
must re-think how to assess the usefulness of the
geospatial information they provide to their customers.
In this paper, we propose a new metric called Geo-
spatial Information Utility (GeoIU), which estimates
the relevance of geospatial information to users. An
Fig. 2. A model building construct.
W.L. Meeks, S. Dasgupta / Decision Support Systems 38 (2004) 47–63 51
important point is that performing a utility assessment
based on the relevance of a select data set from a user’s
point of view, for any of n-specific intended uses at the
instant of assessment, is not the same as performing an
accuracy assessment. The literature is replete with
processes and cases for performing accuracy assess-
ments and computing error matrices on geospatial data
sets. Whereas, accuracy assessments measure statisti-
cal ‘‘goodness’’ against an absolute standard, such as
for a product specification [2,3,18,19,35,38,39,41],
utility assessments measure usefulness relative to the
user’s intended use [40].
GeoIU does not use a binary metric for adequacy;
instead, it provides users and producers with real-time,
interval information about the usefulness of available
data sets in addressing specific information needs.
GeoIU is responsive to the users’ intended uses because
it provides users the ability to weigh specific attribute
and utility measures based on their specific intended
uses, and it supports mechanisms for users and pro-
ducers to prioritize geospatial data acquisition and
production. More importantly, it directly addresses
the three assumptions listed above:
(1) Users of both hard- and soft-copy geospatial data
and products frequently comment that something
is better than nothing; therefore, geospatial
information producers should routinely allow
access to users of the best geospatial information
available at any given time, irrespective of its
estimated adequacy or its status with respect to
being finished data or work-in-process data.
(2) It is incorrect to assume that all users are the same,
or that they have the same information and
information quality needs. Therefore, it is also
folly to assume all intended uses, such as military
mission planning or installation planning for wire-
less telephone signal towers, have the same data
needs.
(3) As with most other products, physical or informa-
tional, the users of a product or service are the
ones best able to determine its utility versus its
intended use.
This paper examines information retrieval with
respect to determining information relevance. Then
it proposes a model to perform information utility
calculations using an information relevance paradigm
based on prior exploratory work performed by the UK
Defense Imagery and Geospatial Liaison Staff to the
US National Imagery and Mapping Agency [39]. In
the following sections, we provide a theoretical frame-
work based on a review of available literature related
to this concept, we provide a vision of how GeoIU
might be used to aid decision makers using geospatial
data and information and how it would be constructed,
we report some preliminary results, and finally we
provide implications for further research. Excellent
GIS and geospatial sciences glossaries can be found at
Refs. [2,3,13,41].
2. Envisioning GeoIU
This research is a model building effort based on
the following construct (Fig. 2), modified from Daa-
len, Thissen, and Verbraeck [23].
Obermeier [42] explicitly defined the goals and
functions needed in a model such as GeoIU; however,
as discussed above, other goals and functions have
been derived from the need for this sort of dynamic,
real-time service to be provided to GIS users to
improve their understandings about the limitations
of the analyses they perform. Outside of, but in
W.L. Meeks, S. Dasgupta / Decision Support Systems 38 (2004) 47–6352
complement to, the processes of GIS, a program of
research into geospatial information utility model
building must address three sequential concepts: (1)
information retrieval, (2) information grading or val-
uation, and (3) information presentation.
As shown in Fig. 3 this paper is primarily
concerned with the information valuation or grading
aspect—the aspect most critical to decision sciences.
The study does this through examining multi-criteria
decision-making (MCDM) [46] and multiple attribute
utility theory (MAUT) [14]. The first and last con-
cepts: information retrieval and information presenta-
tion complement GeoIU model building, thus they are
considered, but only to the degree that they affect
GeoIU research. However, these concepts that remain
interrelated must be considered together when hypoth-
esizing and articulating a logical approach to address-
ing information relevance for geospatial information,
and for developing an algorithm capable of computing
and displaying the utility of geospatial information
within selectable parameters based on users’ informa-
tion needs. Finally, given the dearth of information
valuation research in the geospatial domain, many of
these concepts are examined in broader, non-geo-
spatial contexts as we ponder their specific applica-
bility to geospatial data and information. This paper
assumes that a conceptual correlation exists between
concepts, as they are being applied in the textual
Fig. 3. Assisting the GIS process with utili
domain, to the problem of determining information
utility for geospatial data.
As shown in Fig. 4, a top-level information utility
schematic, GeoIU is comprised of metrics about the
quality of available geospatial information and metrics
about the user’s intended uses for the information.
Considering in Fig. 1 the process model GIS use, the
first issue for a GeoIU ‘‘tool’’ is the information
retrieval problem.
3. Information retrieval
Information retrieval is about information discov-
ery and delivery, that is, discovering the right data sets
and bringing them to the analyst or application that
needs them for some user-defined purpose. Within
this model, the information quality aspect can be
further described as shown in Fig. 4. The key compo-
nents of information quality—relative to the users’
needs—are based on metadata queries, which score
metadata values found in selected, distributed geo-
spatial data sets and query scoring functions.
In this era of ‘‘information explosion’’, providing
the right information to the right person within a
reasonable amount of time is a very important goal
for today’s information retrieval systems [7,15]. Due
to the characteristics of different retrieval methods,
ty assessments via the GeoIU model.
Fig. 4. Basic functional structure for geospatial information utility.
W.L. Meeks, S. Dasgupta / Decision Support Systems 38 (2004) 47–63 53
conventional information retrieval models often suffer
from inaccurate and incomplete queries as well as
inconsistent content relevance. We agree and further
believe that users will have their own individual
intended interpretation of the semantic meanings of
their query terms, and that these meanings are not
always captured via search mechanism interfaces.
Chen and Kuo’s [15] discussion highlights problems
users of geospatial information utility will face: that is
rarely complete or homogeneous data (or metadata) of
uniform quality available. This is true even where
complex and well-defined metadata structures exist.
Horng and Yeh [29] propose a novel approach to
automatically retrieve keywords and then to use
generic algorithms to adapt keyword weights. The
core of the GeoIU algorithm rests on capturing and
applying user-defined search parameters, which are
based on intended use of the data returned from the
search, and which are compared in a relative ranking
fashion to determine appropriate weights. While tex-
tual retrieval and scoring models, such as Horng and
Yeho’s, rely on keyword weights, which help in the
examination—the scoring and weighting—of the tex-
tual data itself, the proposed GeoIU algorithm scores
retrieved geospatial metadata values as the de facto
keywords of a geospatial data search, in lieu of textual
keywords. The GeoIUmodel allows for weighing them
similarly, based on a user’s intended use of the geo-
spatial data discovered through the resultant search
query. The ability to develop appropriate user-defined
weighing factors contributes directly to increasing
customization of the GeoIU model.
Classical information retrieval theory uses the lan-
guage of documentation sets for the results of queries
[7,54]. Applied to the GIS problem, a document set
can be thought of as a consistent group or series of
maps, drawings, or records that have the same subject
matter, format, or purpose [41]. Information retrieval
strategies should be insensitive to modest changes in
the relevant document set since individual relevance
assessments are known to vary widely [54]. Investi-
gated are net change relevance assessments relative to
the evaluation of retrieval results. Very high correla-
tions were found among rankings of systems produced
using different relevance judgment sets. We consid-
ered this approach in the development of the GeoIU
model, but extend the Voorhees approach [54] because
it limits the authors’ concept for information utility as
their model is intentionally capable of detecting and
reporting on longitudinal (i.e., over time) changes in
GeoIU scores based on underlying changes in the
source data and metadata.
The structure and organization of geospatial infor-
mation utility queries and calculations should be
extended to an Internet-based search, discovery, and
retrieval paradigm in order to gain access to more and
richer data sources over time. Web or intranet search-
ing is shifting from presenting a list of ‘‘hits’’ to a
richer, more complex presentation of information,
delivering information in a meaningful context [6].
According to Sherman [47], ‘‘searching is a modern
technological wonder, but algorithms can only do
algorithmic functions. Humans perform other types
of functions, including putting information into con-
text and drawing relationships’’. We posit the future of
searching for geospatial information will be best
achieved by combining routine indexing searches with
putting rich human needs back into the search process.
And, as applied to this research, to clearly accommo-
date intended use(s) of discovered data as part of both
retrieval and evaluation functions.
Studies have been performed by analyzing over 1
million Web queries by users of the Excite search
Excite search engine. The Spink, et al. [49] study found
that most people use few search terms, few modified
their queries, view their retrievedWeb pages, and rarely
use advanced search features. This study provides
insight into public practices and choices inWeb search-
ing. The work of Spink et al. [49] has particular
relevance to the authors’ study as the prototype GeoIU
tool accounts for both ‘‘basic’’ and ‘‘power’’ users.
Implications about the sophistication of users are useful
in considering the breadth and complexity of search
and grading parameter inputs. Statistical association
W.L. Meeks, S. Dasgupta / Decision Support Systems 38 (2004) 47–6354
measures have been widely used in information re-
trieval research [17]. Usually, these are based on
clustering documents and terms on the basis of their
relationships. Applications of association measures for
term clustering include automatic thesaurus construc-
tion and query expansion. Further, Jansen and Pooch
[32] discuss the current state of research on Web
searching. Considering this research to be at an incip-
ient stage, they hypothesize this to provide a unique
opportunity to review the state of research in the field,
identify common trends, develop a methodological
framework, and define terminology for future Web
searching studies. Importantly, they propose a frame-
work and implications for the design of studies into
Web information retrieval systems, which are useful in
focusing the GeoIU model, particularly with regards to
interfaces.
4. Information valuation
Methodologies must account for the shortfalls in
available data. As described in this paper, the users of
GeoIU must be able to: (1) define appropriate query
parameters, and (2) weigh the scoring protocols pro-
vided within the information quality segment of the
methodology as shown here.
After the retrieval problem is solved (or at least
addressed), the scoring of retrieved metadata values
must be accomplished. Given the dearth of direct
literature focused on valuing geospatial information,
we looked at two key topics: MCDM, characterized
by Saaty [46], Teng et al. [52], and Vincke [53], who
all discuss similar approaches to addressing and
solving multi-criteria decisions and to developing
multiple attribute utility models. Malczewski [38]
provides excellent treatment of the topic with respect
to GIS and spatio-temporal supported decisions. We
used these concepts both to clarify the domain (i.e., a
content issue) and to clarify our approach to applying
Multicriteria Decision Analysis to the framework of
GeoIU algorithm development. A related topic is
Butler’s MAUT [14]. We relied most heavily on
MAUT for our model development.
Fraser and Gluck [27] state that little is known
about how users employ metadata to evaluate the
relevance of geospatial information objects in satisfy-
ing the users’ information and decision making needs,
and that this might differ from non-geospatial infor-
mation needs. Metadata is normally defined as data
about data. In the United States today, geospatial
metadata structures are nominally guided by metadata
standards being developed by a multi-agency task
force called the Federal Geographic Data Committee
(FGDC) [4]. Similar standards are evolving interna-
tionally [30]. However, though these standards are
becoming more universally accepted, there remains
great variability in their application. For example,
even when well-defined metadata structures exist,
the values necessary to populate metadata tables are
often left null. An interesting and necessary outgrowth
of geospatial information utility research should be a
reevaluation of existing metadata structures in order to
link future metadata structures with different ways to
query and analyze metadata.
Marsden [39] provides a very useful model for
determining geospatial information utility. This model
considers the Fraser and Gluck [27] requirements for
robust and available geospatial metadata without
describing how that metadata is to be made available.
This model can be summarized as: information utility
is a function of the quality of the information made
available in any given query and of a user’s informa-
tion search needs, as related in user-defined functional
terms. The term ‘‘intended use’’ is used to describe the
end purpose or how geospatial information is com-
monly used. The notion of intended use provides an
insightful framework for describing use cases around
which user profiles and parametric preferences can be
developed.
Learning the users’ interest categories in a dynamic
environment, such as the Web, is challenging because
they (i.e., users’ interests) change over time. Novel
schemes to represent the users’ interest categories,
using adaptive algorithms to discover the dynamics of
users’ interest through the use of positive and negative
relevance feedback, have been developed [55]. Em-
pirical studies confirm the effectiveness of this sche-
ma to accurately model a user’s interest and to adapt
appropriately to various levels of changes in the user’s
interests. The notion of accommodating change in the
users’ interests over time is relevant and has been
considered in the GeoIU model and prototype tool
development. Spink and Greisdorf [48] hypothesize
that though the current dichotomous approach to
information relevance has produced abundant infor-
W.L. Meeks, S. Dasgupta / Decision Support Systems 38 (2004) 47–63 55
mation retrieval research, relevance studies that in-
clude consideration of the users’ relevance judgments
will provide greater clarity and congruity.
5. Information presentation
The data fusion issue is not trivial, but it is becoming
better understood over time [8,20,34,36,37]. Just as
there is routine data fusion with geospatial data content,
so too must an information utility algorithm be able to
fuse geospatial metadata. For example, a user’s query
for all road network data within a given scalar band,
within a selected area of interest (AOI), may yield
many different data holdings within several distributed
governmental, academic, foreign, and commercial
databases. If successfully accessed, there may be sev-
eral areas of overlap. These areas must be fused and
evaluated within the context of a new ‘‘whole’’. Dunlop
[25] presents the main lines of discussion about Mira, a
working group of the Commission of the European
Union Information Technologies Program, designed to
advance research in the area of evaluation frameworks
for interactive and multimedia information retrieval.
The Mira working group brought together many of the
leading researchers in information retrieval and hu-
man–computer interaction. Dunlop identifies the need
for more varied forms of evaluation, which need to be
considered to complement engine evaluation.
Stratigos and Curle [50] examined some common-
ly held ideas about end-users, specifically their skills
and abilities, and their understanding of the proper use
of the Internet and commercial information products.
They undertook this study because many assumptions
have been made about end-users and their informa-
tion-seeking skills. This study presents survey results,
one of the most important of which is discovering that
behaviors for verifying information generally have not
changed. The common portrayal of end-users as
uncritical consumers of information is not very accu-
rate. The study shows that users are not lazy infor-
mation seekers who accept at face value the first piece
of information that comes along; rather, users, in
general, are not likely to trust unverified information.
The Stratigos and Curle assumptions and results are
relevant to our work because we presume: (1) users
want to assert some control over their (geospatial)
information search and manipulation operations and,
(2) users are sophisticated enough to use some or all
of the ‘‘mass customization’’ features we envision for
them when the GeoIU model is operationalized as a
systematic application. Coincidentally, since some or
all of the data may need to be purchased, the infor-
mation utility query must aid the user in determining
whether or not to make the purchase.
The importance of the current literature lies not in
its alignment with this specifically focused topic,
because there are a few direct links between the
literature and the information relevance problem for
producers and users of geospatial information. Rather,
the importance of the literature lies in its ability to
point to important trends and ideas for further inves-
tigation in closely related disciplines. These pointers
have provided focus for the exploratory construct, and
have contributed to both the iterative development of
the algorithmic methodology and of a prototype tool
itself.
6. Computing GeoIU
The focus of this research is to determine a user-
oriented methodology for estimating the relevance
of geospatial information to users within existing
and future uses of these data and information. The
proposed GeoIU metric measures the usefulness of
geospatial information in terms of its spatial accu-
racy (accuracy is typically comprised of both spatial
accuracy (i.e., of the objects features contained in a
database) and content accuracy (i.e., of the attrib-
utes associated with each feature); in this context,
spatial accuracy is the measure of merit); currency;
percentage of area coverage; ‘‘usability’’ as deter-
mined by user-defined data types or forms; and
availability for use within a required datum. This
research attacks the core of the information utility
problem, which is the requirement to access select-
ed metadata records associated with specific geo-
spatial information data sets over any selected area
of interest (AOI), and integrate and analyze those
metadata in such a way as to create a utility
function for the underlying information set or
holding.
Fig. 7 provides the current GeoIU model within
an architectural schema as it applies Saaty’s [46],
Buede’s [11], and Butler et al. [14] multiple attribute
Fig. 5. Considering evaluative functions with respect to information quality.
W.L. Meeks, S. Dasgupta / Decision Support Systems 38 (2004) 47–6356
utility models to the GeoIU problem. The evolution of
the models shown in Figs. 4–6 has lead to the
adaptation of the Marsden model, shown in Fig. 7.
Eastman et al. [26] address the need and a partial
approach, using raster techniques, for applying a
multi-criteria/multi-objective approach to geospatial
decision making. A prototype GeoIU desktop appli-
cation was developed using the model in Fig. 7 in
order to provide a proof of concept under contract to
NIMA [42]. The GeoIU tool searches the metadata of
targeted test datasets to examine and compare record-
level values for each geospatial data set or product
against normative function curves. The metadata val-
ue retrieved from a user’s GeoIU query provides a
function-derived score. This score represents a the-
matic-level score for one data query record within a
user-defined area of interest (AOI). Following the
Fig. 6. Considering evaluative functions
successful retrieval of one or more geospatial data
sets, two things can occur:
(1) the geospatial content must be manipulated within
the GIS.
(2) The geospatial metadata may or may not also be
accessed for separate evaluation and manipula-
tion.
Irrespective of the power and functionalities found
within a user’s particular GIS, toolset, or application
suite, where more than one data set has been
retrieved, quite frequently, the user is able to employ
the GIS application to fuse together various compo-
nents of the data set or multiple data sets, which
often contain thematic layers. Over time, more robust
GIS applications are being developed, including
with respect to information needs.
Fig. 7. Architectural schematic of the geospatial information utility function.
W.L. Meeks, S. Dasgupta / Decision Support Systems 38 (2004) 47–63 57
those for use within common Web browsers. For
example, a popular GIS tool such as Environmental
Sciences Research Institute’s (ESRI) ArcView GIS is
able to ingest geospatial data of different types,
including digital elevation data, road network cover-
age data and hydrographic, and fuse these data
together within the desktop application for the pur-
pose of performing a detailed terrain analysis such as
a cross-country mobility study based on soil compo-
sition and moisture content, slope percentage derived
from a detail elevation model or source, and vege-
tation density [44].
Computing GeoIU is a function of a real-time
analysis of available metadata for selected data sets
W.L. Meeks, S. Dasgupta / Decision Support Systems 38 (2004) 47–6358
within targeted areas of interest. Besides being
algorithmically driven, which implies repeatable
consistency, information utility assessments could
easily be performed via a simple desktop tool or
application. This study hypothesizes that GeoIU may
be based on something as simple as a weighed-average
calculation using the sum of the weights for each
evaluation criteria (e.g., accuracy, currency, coverage,
usability form, and datum availability) applied to
factor scores for each criterion. The weight given to
each criterion would be user-defined. These calcu-
lations would be performed within use-oriented
scalar bands already familiar to geospatial consum-
ers. These bands of scalar representations (i.e.,
symbolizations) are important because they repre-
sent different levels of data density and feature
symbolization that users require to perform various
geospatial information analyses. Each evaluation
criterion will have comparative utility score values
found in community-defined look-up tables or func-
tion curves. Calculating GeoIU internal to the
GeoIU tool leads to a raw score for each criterion,
normalized for comparison and presentation. Nom-
inally, the computed IU score of an evaluated data
Fig. 8. Illustrating the effects of
set, within any intended use scale band, for exam-
ple, could then be summarized as the weighted-
average sum of all criteria scores:
GeoIUscore ¼X
GeoIUfactor scores
� individual criterion factorweights
Fig. 8 illustrates an example of the methodology
being evaluated in a proof-of-concept prototype tool
developed in early 2001. Only area coverage and
currency are illustrated here; however, this method-
ology of using look-up tables and continuous distri-
bution scoring functions must be based on parameters
(e.g., the shapes of the curves, including their coef-
ficients and exponents) agreed upon by the geospatial
information using community. These agreements
for developing scoring mechanisms suitable for add-
ressing critical metadata attributes (e.g., age of
available data in years or months for the currency
rating) must continue for all critical information
utility factors.
For an information utility assessment to be made
for a selected area of interest (e.g., the western
partial GeoIU calculations.
W.L. Meeks, S. Dasgupta / Decision Support Systems 38 (2004) 47–63 59
coast of equatorial Africa), the following steps
represent the sequence of implementation actions:
(1) A user uses an interactive graphical user interface
to select a specific area of interest at one or more
selected scale bands.
(2) The user then designates their own relative
attribute score weights as a means to establish
relative priorities, and then also selects any
minimum query thresholds for each of the utility
factors for their current intended use (note: the
sum of the weights must = 100%).
(3) The IU tool then queries metadata of available and
selected databases, linking to predetermined fields
for current data values relative to the IU assess-
ment (e.g., age of the data, spatial accuracy, etc.).
(4) As a result of each metadata query, individual
component utility factor scores are calculated.
(5) Each component utility factor score is then
multiplied by the previously defined utility factor
weights, providing individual criterion weighed
values.
(6) The individual weighed-values are then summed
together for computation of the information utility
assessment score for the selected area of interest.
(7) Where thematic ‘‘layer’’ information is available
(in either the data or metadata), the IU assessment
is preferred at the thematic level based on the
greater richness of the data at this level of detail.
When thematic-level IU scores can be calculated,
they are done and then aggregated into the final
IU score for the selected area of interest.
7. Model methodology
As depicted earlier, the basic GeoIU model focus-
es on two critical inputs: (1) information quality
based on the structure and results of metadata
queries, and (2) information needs based on the
users’ query-specific evaluative parameters. To an-
swer preliminary ‘Assess the Model’ questions, we
assumed a qualitative logic of inquiry oriented on
eliciting the most important data quality and intended
use stories from a limited user pool. To this end, we
began by parsing our small user pool of users by type
and interest. Divided into focus groups, called tech-
nical exchange meetings (TEMs) in government
parlance of 8–10 participants each, we held three
TEMs on GeoIU issues during May and June 2001.
In order to focus our TEM participants’ attention,
we developed an orientation of Constructive Empir-
icism [22] that we dubbed ‘‘anecdotal empiricism’’.
This term arose from the evolved structure of the
dialogue sessions with the TEM participants. The
term is meant to imply that we sought the TEM
participants’ experiences in their current geospatial
data use paradigms, through anecdotal storytelling.
This came to pass even though we attempted to
structure the TEM sessions through administration
of a two-page questionnaire focused on metadata
scoring and weighting issues.
Though we meant that there be statistical rigor to
this pilot data collection effort, as it turned out, the
users’ interest was so keen on discussing the core
GeoIU concepts and their enthusiasm for telling their
geospatial data use stories was so great that the
questionnaires served only as a vehicle for focusing
the TEM dialogues. Ultimately, they were not a
meaningful means to structure the users’ responses.
The questionnaire included questions about users’
data quality, currency, accuracy, form, and datum
issues. Of particular interest was uncovering effective
means to translate users’ stories as baseline data
points into a form useful in developing the scoring
functions for each of the principal areas of interest
(e.g., currency, accuracy, form).
As predicted, users based on three critical factors
of interest shown in Table 1.
These three critical factors of interest have been
empirically derived from interaction with participants
in the pilot TEMs. These factors can be thought of as
intersecting in a 4� 5 data cube that constitutes a
universe of possible user classes as shown in Fig. 9.
The part of the research dependent on characteriz-
ing and understanding users leads to scheduling the
user TEMs in order to present to them the core
concepts described and to perform a preliminary data
collection. The purpose of the data collection was
two-fold: (1) to gain insight into appropriate scoring
functions for each of the target metadata attributes
scored (e.g., age of the data, horizontal and vertical
accuracy, form of the data, the datum in which the
data are referenced, and area coverage related to the
selected AOI) and, (2) to gain insight into partici-
pants’ preferences for functions and interfaces. These
Table 1
Three factors of interest in characterizing government users classes
Sample scales of interest Sample functions
of interest
Broad
environments
of interest
Macro (e.g., < 1:500,000) Planning Land
Medium
(e.g., 1:25,000–1:500,000)
Operating Sea
Micro (e.g., >1:25,000) Modeling and
Simulation
Air
Detailed analyses Urban
(1) Scale is expressed as a ratio, where larger numbers represent
smaller scales and the rule of thumb is that large scales represent
small areas and vice versa. (2) The littoral zone occurs at the
meeting of sea and land, and it represents a special case for classes
of geospatial data and products. (3) TEMs are government parlance
for technical exchange meetings, which can be considered some-
what akin to a focus group with narrowly focused objectives.
W.L. Meeks, S. Dasgupta / Decision Support Systems 38 (2004) 47–6360
pilot meetings were held in April and May 2001. A
total of 20 participants were interviewed in these
focus group-type meetings. Despite many TEM par-
ticipants’ unfamiliarity and some initial skepticism
with the core concepts associated with GeoIU and
the limited time available in these pilot meetings, the
first meetings provided tremendous insight into a
small population of users’ preferences, and some
opportunities for designing future TEMs on a much
larger scale.
A successful, albeit very limited prototype GeoIU
tool was developed as a Web-based desktop applica-
tion for NIMA during the writing of the 2001 Geo-
spatial Transition Plan [34]. This proof-of-concept
was tested on small ‘‘slices’’ of several different data
sets obtained from several different government-
owned databases. The types and quantities of records
Fig. 9. Considering relevant function
accessed represented a tiny fraction of types and
quantities available across the globe. However, this
approach did satisfy the proof-of-concept, ‘‘can we do
it?’’ question. As with most prototypes, the chief
value is as a communication vehicle between geo-
spatial information users and counterpart researchers,
systems developers, and other domain analysts. Re-
garding the ability to query metadata records for
associated geospatial content records, the prototype
development team encountered varying degrees of
success with the small number of different metadata
structures examined. This effort and the inspection of
various metadata attribute populations indicate that
significant follow-on effort needs to focus on meta-
data standards and attribute population.
8. Conclusion
The information utility metric being developed and
validated via this research is oriented on providing
decision makers who rely on the geospatial informa-
tion or other spatio-temporal data with a higher degree
of confidence in the quality of the underlying data
they employ in their decision support systems. Risk
can be managed or eliminated through greater insight
into the quality of the underlying data—from the
users’ intended use-based point of view, estimation
not currently available today.
9. Implications for researchers
This is an underserved field that has tremendous
potential based on the growing interest in spatio-
s in the user population cube.
W.L. Meeks, S. Dasgupta / Decision Support Systems 38 (2004) 47–63 61
temporal information. There exists both an opportu-
nity and a need for researchers to develop, test, and
refine models such as these. Just as practitioners have
the need for these types of tools in order to improve
the quality of their decision-making, researchers have
something to offer in terms of providing the analytical
rigor needed to improve the quality of these models.
There are both short- and long-term challenges for
those interested in furthering this work. In the short-
term, the models need refinement, primarily through
applying statistical confidence to the scoring func-
tions. Beyond that, a broader set of metadata attributes
should provide the basis for retrieval scoring and
weighting; age, accuracy, datum, and form of data
are not enough—though they served as useful for this
initial exploration—greater breadth and depth need to
be applied to the metadata attributes that are exam-
ined. In the longer term, work needs to be applied to
the metadata structures themselves. Defenders of the
current metadata structures argue that the metadata
standards and structures are sound and have been
vetted within the community.
10. Implications for managers
We posit this will be important and useful work for
managers because of the growing dependence on
geospatial information to aid managers of organiza-
tions in their decision-making activities. Envisioned
are five general use cases that the GeoIU model can
support:
o To provide longitudinal evaluations of changes in
available GI data and information by holding
constant one or more fixed query parameters
o To support multi-perspective (i.e., multiple user
class types) for organizational evaluations of
geospatial data over common areas of interest
o To support pre-acquiring evaluations of expensive
or difficult to acquire GI data
o To support focused or tailored geospatial analyses
o To support evaluations of the compatibility of GIS
tools and intended GI data sources
For both producers and users of geospatial data and
information, the litmus test will be whether the concept
and implementation of geospatial information utility
are sufficiently rigorous and useful to satisfy the users’
needs. To summarize: the proposed geospatial infor-
mation utility metric will measure the usefulness of
geospatial information found in multiple, distributed
libraries for individual users based on their missions,
tasks, and geospatial analysis problems.
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Lee Meeks is a PhD candidate at The George Washington University
in the Management Science Department of the School Of Business
and Public Management. His degrees include an MBA from George
Washington University and a BS from the US Naval Academy. He
works as a Senior Scientist and Program Manager within the
Geospatial Systems Group of Veridian Systems Division in Chan-
tilly, VA. He has spent over 7 years in the employ of or consulting to
the National Imagery and Mapping Agency. His current research
interest is improving organizational decision-making through the
innovative application of information technologies. His focus is
advances in geographical information systems (GIS) as a subset of
information systems, as GIS and spatio-temporal data are applied to
organizational decision-making. His previous publications have
been for the National Imagery and Mapping Agency. He has
presented at the Institute for Operations Research and Management
Science (INFORMS) annual meeting.
Subhasish Dasgupta is an assistant professor of information systems
in the School of Business and Public Management, The George
Washington University. He received his PhD from Baruch College,
The City University of New York (CUNY), and MBA and BS
degrees from the University of Calcutta, India. His current research
interests are electronic commerce, information technology adoption
and diffusion, and Internet-based simulations and games. He has
published in journals such as European Journal of Information
Systems, Logistics Information Management, Journal of Global
Information Management, Journal of Global Information Technol-
ogy Management, Simulation and Gaming Journal, and Electronic
Markets: The International Journal of Electronic Commerce and
Business Media. He has presented at numerous regional, national,
and international conferences.
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