SPATIAL DATA ACQUISITION AND INTEGRATION

ObjectiveTo extend and improve existing technologies for the capture, integration, and consolidation of multiple spatial data resources, including maps, remotely sensed imagery, and topographic measurements; to develop methods for automating the acquisition of new high precision spatial data and its integration into existing spatial databases by means of consistent geo-referencing resulting from image analysis and understanding, suitable geometric transformations, and the use of appropriate algorithms and statistical methods.

BackgroundGeographic information provides the basis for many types of decisions, in areas ranging from simple wayfinding to management of complex networks of facilities, or the wise use of natural resources. In all of these areas, better data can only lead to better opportunities to draw the right conclusions and make the best decisions. What constitutes better data, according to several standards groups and users groups, includes, among other things, greater positional accuracy and logical consistency and completeness. Technological advances are making it possible to capture positional information with ever improving accuracy and precision. Commercial remotely sensed images from space will soon offer a resolution of one meter or better. Satellite telemetry using the Global Positioning System can now achieve accuracies well within one centimeter. But each new data set, each new data item that is collected, however accurate it may be, can only be fully utilized if it can be placed correctly into the context of other available data and information.

Because of the very high costs of human expertise, more and more geographic data sets are being built by automated processes of data capture, followed by integration with other data. Digital Orthophoto Quadrangles (DOQs), a comparatively new form of geographic information, provide a good example. According to the National Mapping Division of the U.S. Geological Survey:

"Digital orthophotos require several types of inputs to produce an orthogonally rectified image from the original perspective image captured by the sensor. Chief among these are: 1) the unrectified raster image file acquired from the scanning of the diapositive image or directly from the sensor, 2) a digital elevation model with the same area of coverage as the digital orthophoto, 3) the photoidentifiable image and ground coordinates of ground control positions (a minimum of four) acquired from ground surveys or aerotriangulation, and 4) calibration information about the sensor collector device. These four inputs are used collectively to register the raw image file mathematically to the scanner or to the sensor platform, to determine the orientation and location of the sensor platform with respect to the ground, and to remove the relief displacement from the image file." [http://www-nmd.usgs.gov/www/ti/DOQ/spec_processing.html]

Geographic data sets show two clear trends: they are becoming increasingly abundant and they are growing ever more precise. Remote sensing technology alone generates a vast amount of raw spatial data daily, much of which is redundant, or is information poor, or is too complex for adequate analysis with current technology before the next new batch of raw data

arrives. Remote sensing technology also promises and delivers continuous precision improvements in image resolution and data capture methods. What have not kept pace with these data generating advances are similarly advanced data assimilation strategies and methodologies.

One approach to facilitating integration of spatial data is to mandate uniformity through standardization and agreed upon formats and requirements. While this may work in the short term for a temporal cross-section of similar data, it cannot fully address the ever evolving character of the captured spatial data. Historical databases from a pre-standards era must coexist with current standardized products; and future fully 3-D spatial datasets must be reconciled to other data within a context of current 2-D spatial data standards.

While remotely sensed and imaged data is becoming available in greater and greater quantities, and at higher resolutions, it is not yet easy to integrate data from different sources because of variations in resolution, registration, and sensor characteristics. Without the ability to integrate data from different sources, we are faced with extensive duplication of effort and unnecessary cost. Imagery can play a very valuable role in updating old data sets, but this process is similarly impeded by the problems of integration. Yet we cannot afford to discard old data.

The term conflation is often used to refer to the integration of data from different sources. It may apply to the transfer of attributes from old versions of feature geometry to new, more accurate versions; or to the detection of changes by comparing images of an area from two different dates; or to the automatic registration of one data set to another through the recognition of common features. Too often, however, methods of conflation and integration have been ad hoc, designed for specific purposes and of no general value. For example, much effort in the past was directed at the update of the relatively low accuracy TIGER database from the U.S. Bureau of the Census with more accurate topographic data.

Technological advances, including vastly greater computing speeds, larger storage volumes, better human/computer interaction, better algorithms, and better database tools, are making conflation and integration more feasible than ever before. A general theoretical and conceptual framework would address at least five distinct forms of integration, all residing in a common database: map to map (different scales, different coverages, etc.); image(s) to map (elevation mapping, map revision, etc.); image to image (different resolutions, wavelengths, etc.); map to measurement (verification, registration, etc.); and measurement to measurement (adjustment, variance, etc.).

The UCGIS Approach"Interdisciplinary" is the watchword for UCGIS research and associated technology development; and the capture and integration of spatial data requires the collaboration of many participating disciplines, including cartography, computer science, photogrammetry, geodesy, mathematics, remote sensing, statistics, modeling, geography, and various physical, social, and behavioral sciences with spatial analysis applications. We will solve key problems of capturing the right data and relating diverse data sources to each other by involving participants from all specialty areas, including the traditional data collectors, the applications users, and the computer scientists and statisticians who optimize data management and analysis for all types of data sets. We will develop mathematical and statistical models for integrating spatial data at different

scales and different resolutions. We will especially focus on developing tools for identifying, quantifying, and dealing with imperfections and imprecision in the data throughout every phase of building a comprehensive spatial database.

Many organizations and data users have developed and promoted standards for spatial data collection and representation. By adhering to these standards, data collectors and data integrators will improve the consistency and overall quality of their products. The standards alone, while facilitating the sound construction of multifaceted spatial (and spatio-temporal) databases, do not, in and of themselves, offer the means by which to integrate fully all types of spatial data efficiently and consistently. Different standards exist for imagery at different scales, for maps at different scales, for adjustment of measurements taken with instruments of different precisions. A single common framework is needed for the diverse types of spatial data. Spatial data integration permits the coexistence of multiple spatially coherent inputs. Spatial data integration must include horizontal integration (merging adjacent data sets) and vertical data integration (map overlay operations); handling differences in spatial data content, scales, data acquisition methods, standards, definitions, and practices; managing uncertainty and representational differences; and detecting and removing redundancy and ambiguity of representation

Importance to National Research NeedsThe National Spatial Data Infrastructure consists of the collective spatial databases of the country and the people and mechanisms for fostering better use of these resources. Integration of information is essential to any information system. Users hope or wish that the integration step can be easily handled; but this only happens rarely when data providers have invested considerable effort to ease the user's burden. Spatial data imposes the additional requirement of correct (or at least consistent) assignment of position to spatial features. Merging spatial data sets under that constraint is uniquely characteristic of a conflation or spatial data integration process.

The benefits of efficient and effective spatial data integration include: reduction or elimination of costs of new data collection; quality improvement of data through added value and greater accuracy, resulting in better decision-making, reduced risk, and increased options for use; and better opportunities for map updating through spatial database maintenance.

High Priority ActivitiesAn inventory of existing and evolving methodologies for spatial data capture and integration needs to be taken. With the inventory in hand, the UCGIS institutions can develop a conceptual framework for integrating diverse data sets based on actual content and quality and on current practices and capabilities.

Several areas of basic research in data acquisition and integration promise significant payoffs in terms of reduced costs and better spatial data products. The basic research areas include: Image processing and analysis, computer vision, geometry of imagery, and other methods of remote sensing; feature recognition, feature matching, feature classification in spatial data sets; algorithm development and data structures for matching and merging spatial data; analysis of impediments to and limitations of spatial data capture/spatial data integration; and

development of map update/maintenance methodologies based on data integration practices.

Several areas of applied research have also been identified and in some cases begun by UCGIS member institutions:

· TIGER database enhancements and adjustments via conflation of other spatial datasets;

· horizontal integration of diverse resources at different scales (e.g., edge matching);

· construction of a US/Mexico common spatial database that includes aerial photos;

· construction of an interstate environmental database;

· development of relative measures of database quality based on positional differences;

· analysis of content differences of spatial databases.

ReferencesAbel, D.J., and M.A. Wilson, 1990. A systems approach to integration of raster and vector data

and operations. In K. Brassel and H. Kishimoto, editors, Proceedings of the 4th International Symposium on Spatial Data Handling, Zurich, Switzerland 2: 559-566.

Department of Commerce, 1992. Spatial Data Transfer Standard (SDTS) (Federal Information Processing Standard 173). Washington, DC: Department of Commerce, National Institute of Standards and Technology.

Federal Geographic Data Committee, 1995. Development of a National Digital Geospatial Data Framework. Washington, DC: Federal Geographic Data Committee, Department of Interior. ftp://www.fgdc.gov/pub/standards/refmod.txt

Kiefer, R.W., and T.M. Lillesand, 1994. Remote Sensing and Image Interpretation, Third Edition. New York: John Wiley & Sons.

National Reserch Council, Mapping Science Committee, 1993. Towards a Spatial Data Infrastructure for the Nation. Washington, DC: National Academies Press.

Saalfeld, A., 1988. Conflation: automated map compilation. International Journal of Geographical Information Systems 2(3): 217-228.

Alan Saalfeld, Ohio State University, 29 October 1996

DISTRIBUTED COMPUTING

ObjectiveTo study the implications of distributed computing on geographic information (and vice versa); to assess likely benefits and compare them to costs; to analyze the associated technical requirements; and to recommend appropriate institutional arrangements and actions.

BackgroundDigital technology is moving rapidly to distributed computing. It is now possible for parts of a database to be stored and maintained at different locations; for users to take advantage of economical or specialized processing at remote sites; for decision-makers to collaborate across computer networks to making decisions; or for large archives to offer access to their data to anyone connected to the Internet. These and a host of other opportunities are offered by recent developments in hardware, software, and large-bandwidth communications technologies (see, for example, Burleson 1994; Onsrud and Rushton 1995; Annitto and Patterson 1995).

In the future, it is likely that large-scale, integrated packages such as GIS will be transformed into collections of smaller, interoperable modules. The free flow of data between them will be enabled by open specifications such as the industry-standard open object specifications, and by the GIS industry's OGIS, or open geodata interoperability specification (http://www.ogis.org; Gardels 1996). Early versions of these "plug and play" GIS software architectures are already appearing. Modules may coexist in one system, or may be distributed across a network and assembled only when needed and with minimal user intervention. Already, we are seeing the rapid implementation of such ideas in the form of "add-ons" to World Wide Web browsers, and in languages like Java.

The GIS field seems set to take significant advantage of these technologies. Moreover, the problems and applications that GIS addresses seem particularly suited to take advantage of distributed computing. Geographic decisions supported by GIS must often be made by many stakeholder groups who are distributed both geographically and socially. Stakeholders are often located in different tiers of the administrative hierarchy. Data custodians may also be distributed, as may be the power to process geographic data in sophisticated software and hardware. On the other hand, a host of issues arise with the implementation of distributed architectures, some technical and some institutional. The research community, through the University Consortium for Geographic Information Science, proposes to conduct a series of systematic studies of the opportunities and impacts of distributed computing.

GIS has already adapted to several changes in computing architectures. Early mainframe systems were quickly extended to remote sites using phone lines and terminals. The minicomputers of the late 1970s were replaced by workstations and personal computers that were increasingly networked for exchange of data. Client/server architectures were adopted in the late 1980s, in a first step towards distributed software. Today, such architectures are being generalized to full distribution, while the user may be presented with an integrated view of the system that may bear little relationship to its actual structure. Indeed, we may reach a time when the entire global network is best conceived as a single, integrated "computer".

Each of these changes has stimulated new growth in GIS applications, in the managerial and institutional arrangements that support it, and in the basic economics of GIS and geographic data in general. These changes are likely to continue in the transition to fully distributed computing architectures. Moreover, such architectures are likely to provide the opportunity for the GIS community to interact with entire new communities, particularly the library community, and for geographic information to become even more important to a range of human activities.

The UCGIS ApproachStudies are needed of the effects of the implementation of distributed computing architectures, and the opportunities they offer to GIS and geographic information in general. In addition to specialists in the technical aspects of the architectures, such as computer scientists, communications experts, and computer engineers, effective research will require the skills of geographers, economists, information scientists, digital librarians, and experts in public policy. UCGIS will play a key role in providing the institutional framework to link experts from these disciplines in a coordinated approach, and to develop partnerships with software vendors and other institutions.

Importance to National Research NeedsSociety is being driven by an unprecedented rate of advance of digital technology. We need to anticipate the new applications and services that will become possible, and the costs and benefits associated with each of them, if we are to continue to push for more economical public services, and more competitive private ones. We need to take advantage of new technologies in education and research. Institutions are generally slow to change, and so it is even more important to anticipate the effects that technological changes are likely to have on them, so that positive changes can occur more quickly, and so that negative impacts can be minimized. This proposed research will focus on geographic information, but will be carried out in full collaboration with more general research activities.

BenefitsThe following are examples of the likely benefits from the proposed research:

· Cost reductions: Monolithic solutions, which fail to take advantage of distributed computing architectures, are likely to become increasingly more expensive. By examining the costs and benefits of alternative architectures, this research will provide the basis for detailed cost/benefit analyses and reductions in costs.

· Distributed custodianship: The National Spatial Data Infrastructure (NSDI) calls for a system of partnerships to produce a future national data framework as a patchwork quilt of different scales, produced and maintained by different governments and agencies. NSDI will require novel arrangements for framework management, area integration, and data distribution. This research will examine the basic feasibility and likely effects of such distributed custodianship, in the context of distributed computing architectures, and the institutional structures that will have to evolve in support.

· Distributed updating: Similar concepts of distributed custodianship, distributed responsibility for data updating, and distributed processing underlie plans for Intelligent

Transportation Systems (ITS).

· Data integration: This research will contribute to the integration of GIS and geographic information into the mainstream of future libraries, which are likely to have full digital capacity. The digital libraries of the future will offer services for manipulating and processing data as well as simple search and retrieval.

· Missed opportunities: By anticipating the impacts of a rapidly advancing technology on GIS, this research will allow the GIS community to take better advantage of the opportunities it offers.

Priority Areas for Research

Short term· Examine the status and compatibility of standards across the full domain of distributed

computing architectures and geographic information at national and international levels; identify important gaps and duplications; examine the adaptability of standards to rapid technological change; and recommend appropriate actions.

· Initiate an effort to prototype a course in GIS taught jointly between two or more UCGIS institutions, to evaluate the ability of current and near-future technologies to support such activities, and the suitability of such courses for non-traditional approaches to GIS education.

· Examine the relationship between such possibilities and current institutional arrangements in higher education; and place them within the context of evolving structures for distributed education.

· Develop an economic model of distributed processing of geographic information; include various assumptions about distribution of costs; and use it to develop a distributed model of GIS computing for the academic community.

· Modify commonly used teaching materials in GIS to incorporate new material on distributed computing architectures.

· Develop methods for intelligent use of bandwidth for transmission of large volumes of geographic data, including progressive transmission and compression; investigate the current status of such methods for raster data; and research parallel methods for vector data.

Medium term· Develop improved models for geographic metadata, to support improved sharing, more

effective search and browse, and more successful evaluation of fitness for use.

· Examine the implications of distributed computing for intellectual property rights to geographic information, within the context of broader developments in this area.

· Conduct case studies of the application of distributed computing in GIS, including horizontal (distribution across different locations), and vertical (distribution at different levels in the administrative hierarchy).

· Monitor the progress of research addressing the technical problems of distributed computing architectures for geographic information: maintenance of data integrity, edgematching, conflation and integration, and automated generalization.

ReferencesAnnitto, R.N., and B.L. Patterson, 1995. A new paradigm for GIS data communications.

Journal of the Urban and Regional Information Systems Association 7(1): 64-67.Burleson, D.K., 1994. Managing Distributed Databases: Building Bridges between Database

Islands. New York: Wiley.Gardels, K., 1996. The Open GIS approach to distributed geodata and geoprocessing.

Proceedings, Third International Conference/Workshop on Integrating GIS and Environmental Modeling, Santa Fe, NM, January 21-25. http://www.ncgia.ucsb.edu/conf/SANTA_FE_CD-ROM/sf_papers/gardels_kenn/ogismodl.html

Onsrud, H.J., and G. Rushton, editors, 1995. Sharing Geographic Information. New Brunswick, NJ: Center for Urban Policy Research, Rutgers University.

Michael Goodchild, University of California, Santa Barbara, 29 August 1996.

EXTENSIONS TO GEOGRAPHIC REPRESENTATION

ObjectivesThe objectives of this research initiative are to (1) extend current 2-D representations and data models, particularly to include more capabilities for representation of volumetric and dynamic (space-time) phenomena, and (2) to develop analytical approaches for supporting those extensions.

BackgroundThe manner in which geographic information is represented, both conceptually and physically as stored data observations, is a central issue for any field that studies phenomena on, over, or under the surface of the Earth. A data representation scheme is required, and is in fact inextricably linked with the process of spatial analysis and the modeling of geographic phenomena. For example, in routing problems the spatial information is typically represented as links between places denoted as points. In dealing with environmental problems, pollutants in air, water or soil tend to be represented simply as grids. For other purposes, these same places may be represented as polygonal objects that are locationally defined by explicit boundaries. These have become known as location-based and feature-based representations, respectively.

The selection of information to be represented, and the representational scheme employed, is thus often driven by the analytical technique to be utilized. Similarly, the results of any analysis can be greatly influenced by how the phenomena under study are viewed. This is why, on an everyday level, a strip map or route map is more easily used for traveling from one place to another than an overall areal map, whereas a route map is virtually useless for showing the overall distribution of various geographic features within a given area.

While it is true that current data representation techniques for geographic information systems (GIS) are capable of representing complex associations among multiple variables, these representational techniques are geared toward representation of static situations on a plane surface at a specific scale. Many of these 2-dimensional representations can be extended conceptually to accommodate volumetric applications, but integration of operational capabilities for representing and analyzing 3-dimensional data has been realized only recently in general-purpose, commercially available geographic information systems. Current spatial data storage and access techniques are also not designed to handle the increased complexity and representational robustness needed for representing heterogeneous types of data for a wide range of analytical and application contexts, as is currently envisioned for handling these same Earth-related problems.

Earth-related data are being collected in digital form at a phenomenal rate. The data volumes that are being generated are far beyond anything we have experienced so far in the geographic realm. The Earth has nearly 1.5 x 1015 square meters of surface area. Thus, a single complete coverage of Spot Image satellite data at 10 meter pixel resolution would total approximately 1.5 x 1013 pixels. If we assume that a single data value for a single pixel can be stored in one byte, then 1.5 x 1013 bytes (or 15 terabytes) of storage would be required for that single, complete coverage. Also, satellite imagery data is normally represented as a gridded

array, or matrix, of cells. It is geometrically impossible, however, to represent the spheroidal Earth with a single mesh of uniform, rectangular cells (Dutton 1983). Other geometries, particularly the triangular mesh, do not exhibit this problem and have other well-known favorable properties (Peuquet 1984).

Various Federal agencies are currently cooperating in the development of a "global spatial data infrastructure." The infrastructure includes the agreements, materials, technology, and people necessary to acquire, process, store, maintain, and provide access to most of the Earth-related data being collected and maintained by the Federal government. Without significant extensions to current representational techniques, these data may be safely catalogued and warehoused, but the Earth scientist will not be able to access these data in any usable manner. We therefore need to develop highly flexible, yet also highly efficient data models for handling Earth-related data of this range and magnitude.

Although many efforts have been made to integrate GIS with dynamic modeling, most of the efforts are limited to development of an interface between two separate types of software systems. Modeling software tends to operate within very narrowly-defined domains using mathematical simulation, while GIS is used primarily for pre-processing of observational data and post-processing for comparative display.

The ability to represent and examine the dynamics of observed geographic phenomena within a GIS context, except in the most rudimentary fashion, is currently not available. We urgently need this capability as an essential tool for examining an increasing variety of problems at local, regional, and global scales. Problems requiring the analysis of change through time and of patterns of change range from urban growth and agricultural impacts to global warming. The research need in this area is of particularly high priority because these representational schemes are needed before analytical techniques based upon these new representations can be developed.

Building dynamic processing within a GIS is difficult because the current GIS data model is geared toward static situations. There are a number of characteristics of space-time data that make development of a space-time representation a difficult and separate problem from volumetric representations. First, temporal units of measurement are not equivalent to spatial units (i.e., we cannot measure time in feet or meters). Second, the nature of time itself is different from that of space. Everything, everywhere at a given moment is at the same location in time. Time also is unidirectional, progressing only infinitely forward. For example, July 4, 1996 will never happen again, although we can examine patterns retrospectively backwards through time (identifying cycles, etc.). Furthermore, there are very complex interactions between space-time processes that also need to be represented in some way.

The limitations of current data models in GIS are due in large part to the continuing use of the (traditional) cartographic paradigm. Although there have been some ingenious exceptions, maps have historically been limited predominately to a flat (2-D) and static view of the Earth. This view is also at a single scale, with assumed exactitude and with no capability for dynamic interaction by the user. These limitations were in large part a limitation of paper (or parchment, etc.) as a cartographic medium.

Current, 2-D approaches do allow for extension to 3-D for volumetric applications, and there are indeed some volumetric geographic data handling systems already in use for graphical and specialized analytical applications. Such systems, however, do not have the representational

flexibility and power needed for addressing complex, global scale analyses. Severe tradeoffs in the capabilities of specific representational techniques currently exist, usually between representational power and efficiency. We therefore plan to extend representational techniques for GIS in order to handle complex and multi-scale volumetric data for interactive analytical and modeling applications.

Information contained within a geographic database may be added to or modified over time, but successive change or dynamics through time cannot be represented, except through some extremely simplistic methods (the most frequently used at present is equivalent to a series of still photographs). Data representation techniques also need to allow for more dynamic interaction with the user for exploratory data analysis and for probing multiple "what if" scenarios.

Given the rapidly increasing use of geographic information systems for policy analysis and decision making, another urgent issue is how to represent data of varying exactness and degrees of reliability, and to convey this additional information to the user. The importance of this issue is underscored by the fact that accuracy in spatial databases was designated as Initiative 1 of the NCGIA. Nevertheless, much work remains to be done on how to handle fuzziness and non-precision, inherent in geographic observational data, within a digital database. This becomes particularly important when multiple layers of data from varying sources are combined (Goodchild and Gopal 1989).

From a human standpoint, spatial relationships between geographical entities (cities, etc.) are often expressed in an imprecise manner that can only be interpreted within a specific application context (e.g., Is New York near Washington D.C.?). Current methods for data representation and query are limited to absolute and exact values and cannot handle inexact terms, such as near, for example (Beard 1994). Nevertheless, inexactness and context dependency is an integral component of human cognition and of the human decision-making process. In order for geographic information systems to become truly useful and user-friendly tools, whether for addressing complex analytical issues such as global change or urban crime or for day-to-day decision making, the data model used by geographic information systems needs to take such cognitive issues into account.

The UCGIS ApproachA primary theoretical issue we plan to address is to find a new representational approach for GIS that optimizes the capabilities of modern computing environment and, new representational techniques recently developed in a number of fields, including GIS and DBMS, and at the same time incorporates human cognition of geographic space. This involves elements from the most philosophical levels (e.g., is time different from space and therefore presents different representational issues?), to the most practical (e.g., high performance computing techniques for handling vastly increased data volumes).

In order to provide sophisticated temporal analysis capabilities as well as the ability to effectively answer a wide range of spatiotemporal queries, we will utilize a multi-representation approach - in other words, the use of multiple representations. This includes combined geometries for location-based representations (rectangular, triangular and hexagonal), and moreover, the use of combined location-based, feature-based, and time-based representations. This multi-representational approach is now generally recognized as the best method by

researachers within the GIS community as well as by developers of commercial GIS, although deliberate use of this approach as a long-term solution for design of geographic databases is a recent development. How tools such as regular polytope geometry and object-oriented design can be used in achieving the needed representational capabilities specifically for volumetric and dynamic geographic data has only begun to be explored. Recent attempts at extending current representational techniques to include time have mostly served so far to demonstrate the complexity of the problem (Peuquet 1994). Several efforts have been working worldwide on the representation of geographic data, and separately, of dynamics within database management systems (DBMS) (Tansel et al. 1993). Besides continuation of these areas of effort, how temporal DBMS techniques can be applied to combined space-time representation also needs to be investigated.

Developing new ways of representing geographic data requires an interdisciplinary effort involving geographers, computer scientists (particularly those currently involved in database management or high-performance computing), applied mathematicians, cognitive scientists, and experts from the application domains.

Although much conceptual work is required on extending methods for geographic representation, the proof and practical refinement of any new data model lies in implementation and empirical testing on real-world data. This requires significant investment in programming time and computing resources. The method of data representation is the most fundamental element upon which any type of software is designed and built. It is therefore rarely possible to replace data models within existing software. This means that software to test new data models is necessarily custom-built and unique.

Importance to National Research Needs/BenefitsThe need to better understand the effects of human activities on the natural environment at all geographic scales is now viewed with increasing urgency. In natural resource management within the developed world, the emphasis is shifting from inventory and exploitation toward maintaining the long-term productivity of the environment. This task requires interactive space-time analysis at multiple scales in order to understand the complex interrelationships of environmental systems. As only one component of this, Global Circulation Models (GCMs) are currently being used to study climate dynamics, ocean dynamics, and global warming. Verification and refinement of these models requires sophisticated analysis of large volumes of multidimensional data, particularly the study of change through time and of patterns of change through time over the Earth, in the oceans and through the atmosphere.

In an urban context, the need is for interactive and real-time problem-solving in emergency response situations (e.g., floods, wildfires) for preservation of life and property. There has also been recognition of the need for predicting human/environmental interactions with increasing population and development through the use of multiple "what if" scenarios. For all of these diverse uses of GIS, it is essential to have the ability to perform interactive space-time analysis at multiple scales, and upon data of known reliability.

Enormous amounts of data, already in digital form, are being collected for the study of a diverse range of urgent environmental, economic and social problems. Nevertheless, current representational techniques for storing and accessing these data within GIS are not adequate. Without significant advancements in representational methods, access to these data in the form

needed for analysis will be impossible.

Priority Areas for Research

Short term (concrete benefits in 1-2 years)· Apply new DBMS techniques, particularly temporal DBMS, to the geographic context,

examining alternative ways of representing the temporal component, evaluating alternative temporal DBMS designs, and identifying aspects of time in geographic data that cannot be represented using existing DBMS.

· Apply high-performance computing techniques to the geographic context, examining methodologies for distributed databases and distributed processing that take the spatial nature of the data and of the potential retrieval queries into account.

Medium term (concrete benefits in 3-5 years)· Develop new strategies/techniques that combine current approaches, such as (a) the use of

object-oriented programming techniques and (b) the use of regular polytope geometry for multi-representations.

· Develop a space-time data model that can represent dynamic processes and spatial interactions in an effective manner using a multi-representation approach.

· Develop new graphical interface (visualization) techniques that utilize the increased representational capabilities needed for large, multi-scale, heterogeneous data.

· Develop new query language capabilities for handling the increased dimensionality of spatio-temporal data (e.g., while standard query languages have been extended to handle spatial queries, research is still needed to make appropriate extensions to space-time).

Long term (first concrete benefits in 5 years)· Develop a new, multidimensional representational theory that more closely reflects human

cognition, yet is also highly efficient and minimally complex from a computing standpoint.

· Develop characteristics based upon the new representational approach that allow geographic databases and associated analytical capabilities to be implemented with predictable characteristics.

In terms of priorities for research, the highest priority should be placed on the long-term effort, with second-highest priority on the medium-term efforts. The reasoning behind this is simply that many private GIS providers and government agencies are already funding or are themselves directly participating in the areas identified as short-term efforts. Areas identified as longer-term efforts represent where most work needs to be done and where the highest benefit will be derived. Nevertheless, these areas are also those least likely to gain support of private GIS providers or government agencies simply because of the length of time these efforts will require for sustained support before concrete benefits could first be realized in operational GIS.

Each of these priority areas of research also requires the context of an example problem.

The test problem should be one that includes multiple scales, as well as multiple dimensions and a diverse range of data types. The purpose of this is to provide a focus for the research so that solutions developed are indeed directly useful and applicable. Example test application problems include the global water cycle, global carbon cycle, Central American forests, land use change and social impacts, crime, dynamic changes in urban neighborhoods, and emergency response.

ReferencesBeard, K., 1994. Accommodating uncertainty in query response. Proceedings, Sixth

International Symposium on Spatial Data Handling, Edinburgh, Scotland. International Geographical Union.

Dutton, J., 1983. Geodesic modelling of planetary relief. Proceedings, Auto-Carto VI, Ottawa.Goodchild, M.F., and S. Gopal, editors, 1989. Accuracy of Spatial Databases. London: Taylor

and Francis.Peuquet, D.J., 1984. A conceptual framework and comparison of spatial data models.

Cartographica 21(4): 66-113.Peuquet, D.J., 1994. It's about time: a conceptual framework for the representation of

spatiotemporal dynamics in geographic information systems. Annals of the Association of American Geographers 84: 441-461.

Tansel, A.U., J. Clifford, S. Gadia, S. Jajodia, A. Segev, and R. Snodgrass, 1993. Temporal Databases. Redwood City, CA: Benjamin/Cummings Publishing Co.

Donna Peuquet, Pennsylvania State University, 29 August 1996.

COGNITION OF GEOGRAPHIC INFORMATION

ObjectiveResearch in the cognition of geographic information deals with human perception, memory, reasoning, and communication. It involves the spatio-temporal and thematic attributes of the objects and events in the real world, and how these attributes are represented digitally. Basic research in geographic cognition is relevant to a host of issues involving geographic information: data collection and storage, graphic representation, spatial analysis, interoperability, decision-making, the societal context of GIS, and more. We believe that many aspects of GIS usability, efficiency, and profitability can be improved by greater attention to cognitive research.

BackgroundA growing number of researchers are addressing questions about cognitive aspects of geographic information. Such work is part of a research tradition begun primarily in the 1960s by urban planners, behavioral geographers, cartographers, and environmental psychologists. Planners began to study how humans perceive and learn about places and environments. Behavioral geographers started developing theories and models of human reasoning and decision-making leading to behavior in space, such as shopping, migration, and daily travel. Cartographers initiated research on how maps are perceived and understood by map users, both expert and novice. Finally, environmental psychologists refocused traditional questions about psychological processes and structures to understand how these processes operate in built and natural environments, such as offices, neighborhoods, public buildings, cities, and wilderness areas.

During the decades since the 1960s, several additional disciplines within the behavioral and cognitive sciences have contributed their own research questions and methodologies to this topic. Within research psychology, the subfields of perceptual, cognitive, developmental, educational, industrial/organizational, and social psychology have all conducted research on questions of how humans acquire and use spatial and nonspatial information about the world. Architects have joined planners in an attempt to improve the design of built environments through an understanding of human cognition of those environments. Both anthropologists and linguists have conducted research on human conceptualization and language about space and place. Artificial intelligence (AI) researchers within computer science developed simulations of spatial intelligence, in some cases as part of the design of effective robots.

More recently, within the past 5-10 years, an interest in geographic cognition has developed within the geographic information community, a community that now includes many of the disciplines described above. These researchers have begun to address a host of issues at the intersection between geographic information and cognition. How do humans learn about geographic information, and how does this understanding vary as a function of the medium through which the information is learned (direct experience, maps, descriptions, virtual systems, etc.)? What are more natural and effective ways of designing interfaces for geographic information systems? How do people develop concepts and reason about geographical space, and how does this vary as a function of training and experience? How do people use and understand language about space and about objects and events in space? How can complex

geographical information be depicted to promote comprehension and effective decision-making, whether through maps, models, graphs, or animations? How and why do individuals differ in their cognition of geographic information, perhaps because of their age, culture, sex, or specific backgrounds? Can geographic information technologies aid in the study of human cognition? How does exposure to new geographic information technologies alter human ways of perceiving and thinking about the world?

This description of topics and questions makes it clear that research in the cognition of geographic information has strong ties with other research priorities proposed by the UCGIS. Several of the priorities, including "Representations", "Scale", "Spatial Analysis", and "Uncertainty", deal in part with questions of the representation and depiction of complex spatiotemporal information. In all cases, important research needs to be done on how best to communicate this information accurately and effectively. "Interoperability" includes concerns about sharing geographic information between distinct groups of users. "GIS and Society" involves questions about social decision-making processes that depend in part on how information is understood by participants in decision-making groups. These ties with other UCGIS priorities further suggest the importance of research on geographic cognition.

The UCGIA ApproachThe UCGIS will support progress on these research issues in several ways. Most centrally, the UCGIS will provide coordination and facilitate communication among the several disciplines that have relevant contributions to make. By promoting cognitive research, and printing and disseminating material such as this research agenda, the UCGIS will create a critical focus on issues of geographic information science for the disparate disciplines and research programs. This focus currently exists only among a small number of potentially relevant researchers; most such researchers are largely unaware of the importance of their work to issues of geographic information. And more than create a focus, the UCGIS approach will go far towards prioritizing the research issues. In identifying these priorities, and by dispersing findings from this research, the UCGIS will help ensure that cumulative progress is made.

Importance to National Research NeedsResearch on geographic cognition is important to many areas of high priority within the national research and development agenda. An understanding of how humans conceptualize geographic features and spatial information will support attempts to create geographic information standards and promote interoperability of systems, including distributed information systems. Good examples of this include national and international data standards, and current attempts to create digital geographic libraries. Research on geographic cognition will improve the functionality and dissemination of many information technologies, including data capture technologies, geographic information systems, and Intelligent Transportation Systems. It will also play a major role in research on improving the effectiveness of geographic education.

BenefitsInadequate attention to cognitive issues is a major impediment to the fulfilment of the potential of geographic information technologies to benefit society. Cognitive research will lead to improved systems that take advantage of an understanding of human geographic perception and

conception, particularly that of spatial and geographic "experts". It will undoubtedly aid in the design of improved user interfaces and query languages. The possibility that it might lead to improvements in representations, operations, or data models is very real and should be investigated as well. In any case, a geographic information technology that is more responsive to human factors in its design will potentially greatly improve the effectiveness and efficiency of GIS. It will promote more equitable access to information and to technologies, allowing us to respond to differences among users. Thus, relatively inexperienced or disadvantaged users will gain access to geographic information technologies, and experienced or expert users will gain greater power and efficiency in their use. Finally, cognitive research holds great promise for the advance of education in geographic information at all levels. This includes both traditional concerns about poor general knowledge of geography, and more specific concerns, such as education about the critical issues of global and environmental change, or distilling the concepts and approaches of geographic information experts.

A specific example concerns the design of In-Vehicle Navigation Systems (IVNS), part of the broader topic of Intelligent Transportation Systems. Research has shown that the effectiveness of IVNS placed in automobiles depends on the modality and format in which information is depicted to the user. For most users, verbal instructions have been shown to lead to faster processing and fewer errors than map depictions. Further research will help determine which types of features are most useful to be included in computer generated instructions and how these features should be described. Maps are useful in certain circumstances, however. Other research has shown that the orientation of these maps is critical; software and hardware must be implemented to support real-time realignment of digital maps during travel. Again, additional research will help determine the best way to design these maps to optimize communication of geographic information for the automobile traveler.

Another example concerns digital geographic library systems. Basic research on the human conception of geographic features is needed to design interfaces in order to optimally support queries to the system. It is clear that this will depend a great deal on the user's level of training and experience in geographic information. Cartographers, Earth scientists, and schoolchildren all have very different needs in this respect. Future research will help determine efficient methods of allowing for these differences in digital libraries.

Priority Areas for ResearchSeveral specific research questions can be identified as being of high priority at this time. The geographic information sciences can make considerable progress on the following questions within a 3-5 year time frame:

· Are there limitations of current data models that result from their inconsistencies with human cognitive models of space, place, and environment? What benefits could be derived from reducing these inconsistencies? Are there alternative data models that would be more understandable to novices or experts? Research on categorization indicates that humans discretize what is essentially a continuous physical world. How can the nature of human categories be assessed and incorporated into GIS? How do limitations of human categorization impact our ability to reason with geographic information? Self-report inventories and memory tests will help answer these questions, including sorting and category identification tasks.

· How can interfaces for wayfinding be designed and implemented in Intelligent Transportation Systems to improve their effectiveness and efficiency for tasks such as route choice and the production of navigational information? Examination of errors and response times during the use of alternative systems will provide information on the strengths and weaknesses of particular designs.

· How can natural language be incorporated into GIS? How should it be? Possibilities to investigate are interpretation of natural language queries, automated input of natural language, and automated output of natural language instructions. Linguistic studies can be focused on issues of geographic and spatial language.

· Spatial metaphors are frequently used to express nonspatial information ("spatialization"). How can such metaphors best be used to represent and manipulate information? Both the speed and correctness of interpretations of spatializations can be tested.

· How can GIS be used to represent and communicate important information in novel ways? Examples include information about error and uncertainty, scale and scale changes, and temporal information and process (as in animation). Performance measures can be collected on geographic tasks that require subjects to interpret the meanings of particular depictions of error, scale relationships, or temporal change.

· What are the possible applications of immersive virtual-environment (VE) technologies to the exploration of information with GIS? What is the relationship of a VE format to traditional cartographic representations? Understanding the impact of such new media requires both systematic comparison to existing media and strategies for understanding novel experiential situations. Again, knowledge tests can be administered after exposure to VE representations and compared to exposure to traditional map representations.

ReferencesAlm, H., 1993. Human factors considerations in vehicle navigation aids. In D. Medyckyj-Scott

and H. Hearnshaw, editors, Human factors in GIS. London: Belhaven Press, pp. 148-157.

Davies, C., and D. Medyckyj-Scott, 1994. GIS usability: recommendations based on the user's view. International Journal of Geographical Information Systems 8: 175-189.

Egenhofer, M.J., and D.M. Mark, 1995. Naive geography. In A.U. Frank and W. Kuhn, editors, Spatial Information Theory: A Theoretical Basis for GIS. Berlin: Springer-Verlag, pp. 1-15.

Frank, A.U., 1993. The use of geographical information systems: the user interface is the system. In D. Medyckyj-Scott and H. Hearnshaw, editors, Human Factors in GIS. London: Belhaven Press, pp. 3-14.

Mark, D.M., 1993. Toward a theoretical framework for geographic entity types. In A.U. Frank and I. Campari, editors, Spatial Information Theory: A Theoretical Basis for GIS. Berlin: Springer-Verlag, pp. 270-283.

Montello, D.R., and S.M. Freundschuh, 1995. Sources of spatial knowledge and their implications for GIS: an introduction. Geographical Systems 2: 169-176.

Nyerges, T.L., D.M. Mark, R. Laurini, and M.J. Egenhofer, editors, 1995. Cognitive Aspects of Human-Computer Interaction for Geographic Information Systems. Dordrecht: Kluwer Academic.

Peuquet, D.J., 1988. Representations of geographic space: toward a conceptual synthesis. Annals of the Association of American Geographers 78: 375-394.

Daniel R. Montello, University of California, Santa Barbara, 23 October 1996.

INTEROPERABILITY OF GEOGRAPHIC INFORMATION

ObjectiveResearch questions in the area of geographic information interoperability deal with the interchange of spatial data held in incompatible or proprietary systems, and with related issues of exchange standards, geographic data semantics, metadata, and the development of formal knowledge-based integrated languages for communicating between domain-specific systems. Communication is required if we are to effectively integrate, exchange, and share data and information across platforms, systems, and emerging computing paradigms.

BackgroundInformation systems and distributed database systems pose problems of interoperability that are related, but differ in important ways. A distributed database management system (DBMS) is a system to manage multiple geographically distributed databases as a single, integrated database. Distributed databases are typically designed within a global schema. Local and global DBMS functions are designed simultaneously and local DBMSs are homogenous with respect to data model and functional interface (Bright et al., 1992). Interoperability generally refers to a bottom-up integration of pre-existing systems and applications that were not originally intended to be integrated, but are systematically combined to address problems that require multiple DBMS and application programs (Litwin et al., 1990). Usually, different organizations develop systems and applications to address their specific sets of problems. Since these systems are developed independently, it is unlikely that they will use the same data model or semantic representation of geographic information.

Interoperability requires the exchange of data between these heterogeneous data models and thus a consistent set of interpretations must be provided for that information. Ensuring this consistency requires semantic interoperability; in other words, agreement on the meaning of the exchanged information (Sciore et al., 1994). Thus, it is clear that "... the achievement of interoperability should be viewed as an enabling condition for interoperation between application systems and semantic integration of information from diverse sources." (Drew et al., 1993). Thus, interoperability relies heavily upon communication of information between organizations, application programs, and databases wherein formal language and model representations of complex geographic information have been resolved.

Geographic information interchange standards efforts over the last 10-15 years have produced a number of national and international standards documents. The prevailing approach in the information interchange arena has been to develop interfaces that allow translation of spatial data from one proprietary format to a standard or "neutral" format, from which the information can again be translated into a second proprietary format. Much effort has been directed at formalizing general aspects of storing and retrieving geographic properties and entities, notably cartographic entities.

Metadata, a key component for any interoperability scheme, has received attention but has generally been viewed as a header on the data. As metadata evolves towards a machine readable form, improved reliability and consistency in the interchange of geographic information

will occur. For example, the SEQUOIA 2000 project (Anderson and Stonebraker, 1994) adapted the Spatial Archive and Interchange Format (SAIF), which is based on object-oriented data modeling, to manage metadata for large volumes of remotely sensed data. Further work is needed in storing and representing metadata, specifying metadata requirements for geographic domains, and building tools that are able to find commonalties between interchanged data from different agencies.

A long-term goal of interoperability within geographic domains is the machine interpretation of geographic data semantics. This necessitates a considerable amount of research into developing approaches for formally representing geographic phenomena, in terms of their structure, semantics, and behavior. This also begs the question of the role of "intelligent" tools to aid in the process as this would appear to be a knowledge-intensive activity. In the short term, interoperation between geographic databases and process-based models, such as those currently being addressed within the GIS and environmental modeling community, serves to identify limitations in communication of geographic information. The issues and theories that emerge from this body of research may well serve the long term goal of improving semantic representation and eventual development of language standards for communicating geographic information. Even within one apparently narrow field such as environmental modeling, however, application domains such as gap analysis, emergency response, and environmental monitoring may each require their own semantic translations.

Much of the capability of GIS as a tool for analysis is derived from formal models of geographic features. In the past these models were viewed largely from a cartographic perspective. The need to address problems that are non-cartographic, such as environmental modeling problems that require understanding of physical processes, has brought about a perceived need to integrate GIS and process-based models developed within the scientific community. A research goal of model integration within a GIS context is to determine the compatibility between models, such as their spatial and temporal scales, their spatially explicit representation, the languages to support the dynamic nature of simulation models, and error propagation between process-based models and other levels of GIS analysis. Theories and methods that develop through efforts to integrate GIS and process based models will serve the longer term goals of developing geographic data models and language support for the communication of geographic information between organizations.

A longer-term goal of interoperability within the GIS community is the development of canonical data models of geographic information. Early forms of data models, including the relational model, provided no direct support for the complex features of geographic domains, such as the relationships between an airport as an entity and its components (runways, control tower) and facilities. Semantic models developed since the late 1970s have been able to account for more semantically demanding domains, by incorporating data abstractions such as generalization and specialization, classification, and aggregation. Generalization and specialization allow for new classes of entities to be defined in terms of existing classes of entities. Alternatively, a specific set of entities can be defined and grouped at a later time by identifying common properties. This abstraction allows for a very simple form of inference-inheritance which allows specialized entities to inherit properties defined already in a generalized entity. Classification allows for the definition of classes of entities (e.g., road), which group individuals with respect to one or more common properties (e.g., Interstate-95). Aggregation allows for properties about a class of individuals to be specifically related to the

class, either explicitly with an attribute value, or with the use of rules and integrity constraints. For example, the statement "All paved roads must be paved" can either be used as a rule to classify an individual road under the class "paved roads," if it is paved, or as an integrity constraint that checks to make sure that every individual road checked into the DBMS as a paved road is in fact paved.

Further work is required in order to take advantage of semantic data models, particularly in how geographic domains are defined. One approach that seems to have considerable applicability to geographic information representation and communication is to extend the representation capabilities of existing data models, such as extended entity-relationship modeling (Gogolla and Hohenstein, 1991), or a unified extended relational model and structured query language using various models developed and provided by the GIS industry (Robinson and Tom, 1993). Another approach that seems promising for resolving potential conflict between spatial information collected and represented for different purposes is to develop integrated systems using the semantic modeling abstractions (Robinson and Mackay, 1996). A key activity in this arena is the evolving Open GIS Specification, which provides a comprehensive model of geodata and geoprocessing interoperability, based on an essential model of how information communities perceive and utilize geographic information, an abstract definition of the required interfaces and types for realizing this model, and implementation specifications for providing this in a particular distributed computing environment.

The UCGIS ApproachBasic interoperability obstacles include multiple modeling approaches, domain-specific conventions for organizing and cataloging data, and alternative data structures even for similar geodata models. These are further complicated by the fact that a user often has incomplete or even incorrect knowledge of a remote data set. Recognized research needs are both immediate, short term where interchange standards, metadata, and data set/library issues are primary, and more fundamental or long term where semantic representation languages for spatial data and the development of canonical models of geographic information are formalized.

UCGIS provides a unique institutional infrastructure which brings together specialists from the various disciplines (both technical and cognitive) required to successfully accomplish interoperability. The Consortium will convene experts from government, industry, and academia and deliver a comprehensive solution for both the short-term domain specific issues, as well as longer term interdomain problems.

Importance to National Research NeedsThe interest and desire to access distributed information throughout a national or global network of geodata repositories serves to justify the urgent need for solving interoperability problems. Different users or information communities have different earth models, which in turn manifest themselves in the semantics of geographic information. This goes beyond an encoding issue and is often not considered a language issue, but rather it is a world view characterization problem which involves how perceived elements of the landscape are named, defined, described, and modeled by various communities of geographic information users. Without significant

advancements in interoperability, access and interchange of data within and between domains will be impossible.

BenefitsThe following are examples of the likely benefits from the proposed research:

· The ability to access and translate data based on a process of discovery and dynamic interpretation when the salient factors cannot be known in advance.

· The ability to provide a consistent logical view of geographic information, independent of the underlying data model or format.

· The identification of domain specific questions that require integrated geographic information systems.

· The development of a new paradigm of global information access to digital libraries.

· A reduction in dependency on monolithic or "stovepipe" solutions.

Priority Areas for ResearchFrom the foregoing discussion it should be plain that GIS interoperability (GISI) problems span several disciplines, require knowledge-intensive approaches leading to intelligent tools, and require a formal approach to modeling and managing the semantics of geographic information science. We divide projects on geographic information interoperability into those addressing

Short-term ProjectsIn the short term a goal for the field as a whole is a more complete formal specification of the semantics underlying models of geographic information and their use in the intercommunication among systems with a significant geographical component. This can be addressed by the development of geographic information systems that include process-based models and some formal method of representing the semantics of a specific domain. Although we are at risk of identifying non-scaleable solutions if we are too focused on narrow problems, it is anticipated that such projects would develop largely in specific domain sciences (e.g. hydrology, ecology, regional science) where a significant project component would be directed at how well models work together.

Within each domain-specific system it is common for domain evolution to lead to semantic heterogeneity. In effect, this constitutes an important problem that can help lead to an understanding of how to develop larger, more complex GISI solutions, since it necessitates two (or more) versions of the same application being able to work with heterogeneous data definitions.

These short-term projects should attempt to address, among other problems:

· The identification of domain specific questions that require integrated geographic information systems.

· The identification of implications for choosing a specific set of models and what kinds of modifications are required to make various models work together (e.g., scale

corrections).

· Understanding the role of scale, data statistical support, and model assumptions in determining the ability to answer questions.

Long-term ProjectsIn general terms, a major long-term goal should be the development of languages (including visual and logic-based), semantic theory, geographic knowledge representation to support GISI, and a new paradigm of global information access to digital libraries. In particular, long-term projects:

· Should focus on development of languages based on a semantic theory capable of addressing problems of semantic heterogeneity as part of the inter-system communication process.

· Identify a number of significant contributions recognized by the short-term projects, and attempt to generalize some of these results.

· Have a strong multi-disciplinary basis and significant grounding in two or more scientific domains. They would be primarily directed at development of a formal knowledge-base for communicating geographic information between domain-specific systems.

· Develop a new paradigm of global information access to digital libraries.

ReferencesAnderson, J.T., and M. Stonebraker, 1994. SEQUOIA 2000 metadata schema for satellite

images. ACM Sigmod Record 23(4): 42-8.Bright, M.W., A.R. Hurson, and S.H. Pakzad, 1992. A taxonomy and current issues in

multi-database systems. IEEE Computer 25(3): 50-9.Drew, P., R. King, D. McLeod, M. Rusinkiewicz, and A. Silberschatz 1993. Report of the

workshop on semantic heterogeneity and interoperation in multidatabase systems. ACM Sigmod Record 22(3): 47-56.

Gardels, K., 1996. The Open GIS approach to distributed geodata and geoprocessing. Proceedings, Third International Conference/Workshop on Integrating GIS and Environmental Modeling, Santa Fe, NM, January 21-25. http://www.ncgia.ucsb.edu/conf/SANTA_FE_CD-ROM/sf_papers/gardels_kenn/ogismodl.html

Gogolla, M., and U. Hohenstein, 1991. Towards a semantic view of an extended entity- relationship model. ACM Transactions on Database Systems 16: 369-416.

Litwin, W., L. Mark, and N. Roussopoulos, 1990. Interoperability of multiple autonomous databases. ACM Computing Surveys 22: 265-93.

Open GIS Consortium, 1996. The OpenGIS Abstract Specification: An Object Model for Interoperable Geoprocessing, Revision 1. http://www.opengis.org/public/96-015r1.ps

Robinson, V.B. and D.S. Mackay, 1996. Semantic modeling for the integration of geographic information and regional hydroecological simulation management. Computers, Environment, and Urban Systems 19(5/6): 321-39.

Robinson, V.B. and H. Tom, 1993. Towards SQL Database Language Extensions for Geographic Information Systems. Publication No. NISTIR 5258. Gaithersburg, MD: National Institute of Standards and Technology, U.S. Department of Commerce.

Sciore, E., M. Siegel, and A. Rosenthal, 1994. Using semantic values to facilitate interoperability among heterogeneous information systems. ACM Transactions on Database Systems 19: 254-90.

D. Scott Mackay, University of Wisconsin; Kenn Gardels, Xavier Lopez, Howard Foster, John Radke, University of California, Berkeley; 28 October 1996.

SCALE

Objective· to assess the sensitivity of data, spatial properties of data, and analyses to changes in spatial

and temporal scale;

· to identify critical scales at which data content and structure change significantly, and to identify the ranges of scales over which processes and spatial patterns are invariant;

· to quantify information content as a function of sampling interval and/or observation scheme, and information loss as a function of data generalization methods;

· to develop theory and methods for intelligent database generalization, data enhancement, and data reconstruction;

· to develop alternative data models (i.e., formalized schema for organizing spatial information) that permit variable-resolution representations, integrated multiple scale representations, and scale-related modeling of the data that describe a spatial data set (called metadata); and

· to explore theoretical linkages between internal (i.e., database), external (i.e., map), and cognitive (i.e., semantic) concepts of scale that permit consistent representation across these domains.

BackgroundScale is not a new issue, nor is concern restricted to geographic information scientists. Scale variations have long been known to constrain the detail with which information can be observed, represented, analyzed, and communicated. Changing the scale of data without first understanding the effects of such action can result in the representation of processes or patterns that are different from those intended. The spatial scaling problem presents one of the major impediments, both conceptually and methodologically, to advancing all sciences that use geographic information. Likewise, temporal scaling problems are not well understood, and thus difficult to formalize. In an information era, a massive amount of geographic data are collected from various sources, often at different scales. Before these data can be integrated for problem solving, fundamental issues must be addressed.

Recent work on the scaling behavior of various phenomena and processes (including research in global change) has shown that many processes do not scale linearly. The implication is that in order to characterize a pattern or process at a scale other than the scale of observation, some knowledge of how that pattern or process changes with scale is needed. Attempts to describe scaling behavior by fractals or self-affine models, which mathematically relate complexity and scale, have proven ineffective because the properties of many geographic phenomena are not strictly repeated across multiple spatial or temporal scales. Multi-fractals have shown some promise for characterizing the scaling behavior of some phenomena, but it is more likely that fractals will offer only a partial model. Alternative models are needed to understand the impacts of scale changes on information content of databases. Scale-based benchmarking of process and analytical models will help scientists to validate hypotheses, which

in turn will improve geographic theory-building.

Despite a long-standing recognition of the implications of scale on geographic inference and decision-making, many questions remain unanswered. The transition from analog (i.e., maps) to digital representations of geographic information forces users of those data to formally deal with these conceptual, technical, and analytical questions in new ways. It is easy to demonstrate by isolated example that scale poses constraints and limitations on geographic information, spatial analysis, and models of the real world. The challenge is to articulate the conditions under which scale-imposed constraints are systematic, and to develop geographic models that compensate or standardize scale-based variation. Mishandling scale can bias inference and reasoning and ultimately affect decision-making processes. New types of analyses, for example the Geographical Analysis Machine (GAM) proposed by Openshaw and others (1987), may offer methods that are less sensitive to scale than traditional quantitative techniques.

The widespread adoption of geographic information systems (GIS) contributes to the scale problem, but it may offer solutions as well. GIS facilitates data integration regardless of scale differences. The capability to process and present geographic information "up" and "down" local, regional, and global scale ranges has been advocated as a solution to understanding the global systems of both natural (global climate change) and the societal (global economy) processes, and the relationships between the two.

Fundamental scale questions will benefit from coordinated research efforts. With the development of alternative models of scale behavior, novel methods for describing the scale of data, and intelligent automation for changing scale, information systems of the future can sensitize users to the implications of scale dependence and provide scale management tools.

The UCGIS ApproachIssues of scale affect nearly every application of GIS and involve questions of scale cognition, the scale or range of scales at which phenomena become apparent, optimal digital representations, technology and methodology of data observation, generalization, and information communication. Effective research in the area of scale will require interdisciplinary efforts of geographers, spatial- and/or geo-statisticians, cartographers, remote sensing specialists, domain experts, cognitive scientists, and database designers. Research on scale is under way in geography (Hudson, 1992), remote sensing (Quattrochi and Goodchild, 1997), cartography (Buttenfield and McMaster, 1991), spatial statistics (Geographical Systems, 1996), hydrology (Sivapalan and Kalma, 1995), and ecology (Ehleringer and Field, 1993) among others. Scale research in many institutes, agencies, and in the private sector began in an ad hoc fashion. Motivated both by practical needs as well as theoretical development, recent attention is focused on formalizing the study of scale, on developing theory, and on exploring robust methods for information representation, analysis, and communication across multiple scales. The UCGIS offers a central forum for communication among researchers in various fields and encourages the application of research findings for practical management and policy issues. UCGIS will also provide a focus on this fundamental research topic and initiate generalization of conclusions from disciplinary research into generic approaches and principles for scale.

Importance to National Research NeedsIt has become clear that global and regional processes have implications for local places and that individual and local decisions collectively have global and regional implications. Therefore, scientific information about global and regional patterns and processes must be understood on a local level and vice versa. As the policy making and scientific communities come to terms with these relationships, systematic understanding about spatial and temporal variations in scale gain importance. Geographical information plays an ever larger role as we move to an increasingly automated information economy. Our understanding of scale and the management of data at various scales must keep pace. Ultimately data and information must inform, and produce better decisions.

BenefitsResearch in this area will provide formalization in the following areas:

· Definitions of scale concepts. There has been much confusion and misuse of the term "scale." Lam and Quattrochi (1992) set a basis for clarifying the term. More thorough work will help clarify all connotations of scale in a multi-disciplinary context. These connotations are inherently related. A good understanding of the conceptual relationship between these connotations should benefit the geographic information users in conjunction with other standardization efforts in geographic information use

· Systematized bases for scale-related decision-making. Basic research on the effects of scale on information content and loss will yield practical information for the many users of geographical information. In an increasing array of management and policy settings, decisions about the appropriate scales of analysis are made every day. Identification of critical scales and of scale-invariant data sets or modeling procedures can make those decisions explicit and better informed.

· Practical guidance on data integration. Often, data at sub-optimal or disparate scales are the best available. Using imperfect data that are available is in many cases preferable to using no data at all, but there are implications for validity of results. For the user community, creation of knowledge about scale provides principles to improve a data set's fitness for use and guidelines by which to discount model results when necessary. A better understanding of the problem of conflation (the procedure of merging the positions of corresponding features in different data layers) will enable the fusing of datasets, produced at different scales—or the same scale from different sources. This currently represents a serious impediment in the map overlay process, a critical component of GIS. A better understanding of conflation is also necessary for the integration of datasets produced by different agencies.

· New methods for quantifying and compensating for the effects of scale in statistical and process models. Scientists and land managers apply a variety of analytical tools to answer geographic questions. Many existing methods do not allow the user to adjust or compensate for the effects of the data scale on the analysis results. Methods that are sensitive to scale will allow for the inclusion of scale correctives.

· Intelligent automated generalization methods. GIS tools can be expanded to provide users with methods for intelligently changing the scale of their data. Basic research is still

needed to understand how scale changes are perceived, and this in turn can inform interface design. Intelligent generalization will permit the encoding of raw observations in digital form to derive more responsive, application-specific archived representations.

· Improved understanding of cognitive issues of scale. Many scale questions involve human cognition. This issue is explicit especially during human-computer interaction, and must be dealt with technically during interface development. It ultimately affects a chain of decisions. Basic research lays a foundation for answering many conceptual and technical questions of proper use of spatial and temporal scales in geographic information processing.

Potential Projects· Identify the ranges of scale over which an encoded attribute classification scheme is valid. A

project of this type will help build a better understanding of the linkages between the scale of a spatial representation and the appropriate corresponding attribute detail. For example, land cover data represented as 30 m pixels should include more detail in the landcover classification than similar data represented as 1 km pixels. Studies of these relationships in specific settings and for specific data sets (e.g., landcover classification) will be performed with the aim of building theoretical bases for understanding these relationships more generally.

· Identify the optimal scales of analysis in various domains and critical scales at which the content or structure of phenomena change suddenly (e.g., in ecology or demography). This work will involve application of sensitivity analyses and spatial statistical tools for describing those sensitivities. Ultimately the goal is to allow users of geographic data to determine the appropriate scales prior to data collection or analysis.

· Perform a cost-benefit analysis comparing the use of pre-generalized data (e.g., soils polygons) versus automated generalization of raw data (e.g., soil pits). "Cost" could refer to data collection costs and/or to computational cycles; "benefits" can similarly serve as a metaphor for data validity. As automated generalization methods become readily available (many are now in commercial GIS packages), users may wish to access data in the rawest form possible as opposed to using data generalized for a specific purpose. Research of this type would have implications for data collection and archival agencies as well as users.

· A well researched, yet still unsolved, problem associated with spatial generalization is the creation of multiple versions of databases. Research in cartographic generalization has taken several directions, including algorithmic design and testing, the design of models and conceptual frameworks, the application of expert systems, and the modeling of cartographic features. Thus far, most of the work in generalization has focused on what is termed "cartographic" generalization that involves the graphical considerations associated with scale change. A second, less researched, area is in "model" generalization, where generalization operators (simplification, smoothing, aggregation, agglomeration, and others) are applied to an original digital landscape model (DLM) in order to create secondary representations of the database, called digital cartographic models (DCM). These terms, DLM and DCM, are taken from the European cartographic literature, where a significant amount of work has been completed in this area.

· Modeling descriptive data about data sets (i.e., metadata) to assess the consequences of downloading data at a finer resolution than is needed for a particular GIS application. The question is to determine if the choice of appropriate scale can be made on the basis of metadata alone. With the expansion of global computer networks and the use of those networks for geographic data transmission, efficient modes of communicating data content are needed. Scale is clearly a fundamental component of any metadata report. What are the most appropriate modes of representing scale and what is their relative effectiveness?

· Design and development of a multi-scale database is needed. We still lack the database managment tools and associated functions needed to effectively and efficiently store multi-scale data, perform multi-scale analysis, and intelligently change scale. This project should be aimed at the development of a software product that would be useful in GIS applications.

ReferencesButtenfield, B.P., and McMaster, R.B., editors, 1991. Map Generalization: Making Rules for

Knowledge Representation. New York: Longman Scientific and Technical, 245 pp.Ehleringer, J.R. and Field, C.B., editors, 1993. Scaling Physiological Processes, Leaf to Globe.

New York: Academic Press, Inc.Geographical Systems, 1996. Special issue on The Modifiable Areal Unit Problem.

Geographical Systems 3(2-3). Guest editors: D. Wong and C. Amrhein.Hudson, J., 1992. Scale in space and time. In R.F. Abler, M.G. Marcus, and J.M. Olson,

editors, Geography's Inner Worlds: Pervasive Themes in Contemporary American Geography. New Brunswick, NJ: Rutgers University Press, pp. 280-300.

Lam, N., and Quattrochi, D.A., 1992. On the issues of scale, resolution, and fractal analysis in the mapping sciences. Professional Geographer 44: 88-98.

Openshaw, S., Charlton, M., Wymer, C., and Craft, A., 1987. A Mark 1 geographic analysis machine for the automated analysis of point data sets. International Journal of Geographical Information Systems 1(4): 335-358.

Quattrochi, D.A., and Goodchild, M.F., editors, 1997. Scaling in Remote Sensing and GIS. Boca Raton, FL: CRC/Lewis Publishers Inc.

Sivapalan, M., and Kalma, J.D., 1995. Scale problems in hydrology: contributions of the Robertson Workshop. Hydrological Processes 9(3/4): 243-250.

Daniel G. Brown, Michigan State University; Aaron Moody, University of North Carolina; Barbara P. Buttenfield, University of Colorado; Arthur Getis, San Diego State University; Robert McMaster, University of Minnesota; Ling Bian, University at Buffalo; 22 September 1996

SPATIAL ANALYSIS IN A GIS ENVIRONMENT

ObjectiveBy taking advantage of the ability of geographic information systems (GIS) to store, select, manipulate, explore, and display geo-referenced data, the purpose of this research is to develop within a GIS environment a variety of methods, techniques, and approaches for the analysis of spatial and time-space data and models.

BackgroundProblems of human health, social deprivation, global environmental change, industrial and economic development, and a host of others all demand that we make sense of what is happening in the world around us. The term spatial analysis encompasses a wide range of techniques for analyzing, visualizing, simplifying, and theorizing about geographic data. Methods of spatial analysis can be as simple as taking measurements from a map, or as sophisticated as the most abstract forms of mathematical statistics.

At the same time, we are being flooded by the benefits of new technologies for Earth observation. New remote sensing satellites are providing unprecedented amounts of data on aspects of the Earth environment, and new sources of demographic, social, and economic data are also becoming available at finer spatial detail. Yet our ability to "drink from the firehose", extract meaning, and make useful decisions has not kept pace. We can no longer rely on the human eye and brain alone, but must augment them through the development of improved techniques for sifting through data to find patterns and outliers; for more effective visualization of data; for testing theories and hypotheses; and for making decisions.

To remain at the cutting edge of GIS technology, analytic and computational methods must be devised that allow for solutions to problems conditioned by GIS data models and the nature of spatial and space-time inquiries. New forms of statistical analysis are needed to assess the relationships between variables in a variety of spatial contexts. New theories must be devised that frame our understanding of relationships between variables at new levels of resolution and dimension. What is the relationship, for example, between moisture and plant growth when our reference is a square kilometer of earth space? How do we assess the clustering of cases of malaria when our environmental data are recorded in little rectangles one meter across?

Spatial data must be treated differently from other types of data. Stronger relationships exist within and among variables that are near to one another. Because the size and configuration of spatial units varies dramatically, we find relationships within and among variables that are due as much to the nature of the spatial units as to the nature of the variables being studied. Standing in the way of confirmatory spatial data analysis, including modeling, are questions having to do with spatial scale, spatial association, spatial heterogeneity, boundaries, and incomplete data. Without reasonable responses to these problems, the usefulness of GIS as an analytical tool in a sophisticated research environment will come into question. By the use of GIS, previously prohibitive, computationally intensive, and highly visual ways of spatial analysis have become accessible at reasonable costs.

The UCGIS ApproachSpatial analysis is the bridge that links fundamental data models to GIS technology, with the result that applications are enhanced and research findings broadened and deepened. The UCGIS emphasizes those research areas that integrate a variety of these activities. The GIS framework includes both the georeferenced data and the tools for data manipulation. The linkages to applications allow spatial analysts to inform applied practitioners of new and more profitable ways to do research and, in like manner, practitioners are able to develop new analytic approaches useful to particular applied fields in the social, physical, and environmental sciences.

UCGIS calls on spatial analysts from both the physical and human sciences to assist in the development of spatial statistics, geostatistics, spatial econometrics, structural and time-space modeling, mathematics and computational algorithms, that can take advantage of the flexibility, capacity, and speed of geographic information systems. Those well-schooled in theory, empiricism, data collection, data manipulation, programming, and computer technology will be in the best position to make advances in this field, but practitioners such as epidemiologists, ecologists, climatologists, regional scientists, landscape architects, and environmentalists can add much to the development of GIS-related research.

Importance to National Research NeedsFor the United States to remain on the cutting edge of GIS technology, we must foster the development of appropriate techniques for analysis in a variety of rapidly changing fields. By engaging in fundamental research in spatial analysis, we can achieve a better understanding of spatial scale, spatial association, spatial heterogeneity, spatial movement, and bounding effects, and can develop more appropriate tools for modeling continuous and discrete data. We will improve our handling of very large spatial data sets (e.g., disaggregated census data, remotely-sensed data at a global scale) and we will discover the appropriate GIS tools for pattern recognition, data generalization, edge detection, and fuzzy pattern analysis.

BenefitsThe research topics outlined in the following section point to the priorities the scientific community must support as we move to the 21st Century. Better techniques of spatial analysis, coupled with GIS, will have applications that span a vast range of societal concerns:

· Disease distribution: The study of the transmission of infectious diseases such as dengue, malaria, and AIDS would benefit from placing disease incidence into a spatial ecological framework where the data coverages of a GIS would be studied in a space-time framework.

· Traffic management and land-use planning: Real-time traffic analysis in a GIS framework will aid in the development of highway infrastructures, traffic and travel demand management, and land use planning.

· Environmental problems: GIS could become better able to provide the environment for the analysis of data extracted from models of water, air, and other types of environmental variables. Problems of fire control, species diversity, hydrology and flood control, hazard mitigation, and park usage are ideally suited for analysis with a GIS framework.

· Landscape characterization and measurement: A compelling problem of those using remotely sensed data for the analysis of such things as land cover and land use is the classification of high resolution data. By bringing image analysis into a GIS analytical framework, various classification schemes can be tested and used in the analysis of land cover data.

· Social, cultural, economic analyses: Economists and other social scientists will have the opportunity to use block and county data to test theories using spatial econometric analyses. The development of the use of these data sets in a GIS framework will increase our understanding of all sorts of social processes, including patterns of employment and unemployment, crime, economic growth, and population change.

· Physical processes: The analysis of hydrologic and climatologic processes under varying physiographic regimes in a GIS framework will enable researchers to pinpoint trends (global change), anomalous events, and, in general, further applied research in these fields.

· Improving the accessibility and equity of opportunities and services: GIS can allow for more sensitive configuration of economic activities and public sector services. The spatial data handling capabilities of GIS allow for detailed representations and analyses of the spatial distribution of disadvantaged populations and their accessibility to opportunites and services. GIS-based techniques for solving sophisticated and realistic location and distribution problems can allow these systems to be configured to maximize accessibility and equity.

Priority Areas for ResearchFuture tools must be not only spatial but also spatial-temporal. They must address certain key questions: How do we handle very large spatial data sets (e.g. disaggregated census data, remotely-sensed data at a global scale)? What techniques can account for the influence of spatial data on the type of analysis employed (e.g., scale and aggregation effects)? What generic GIS tools [e.g., Openshaw's (1994) pattern-spotters and testers, data simplifiers, edge detectors, and fuzzy pattern analyzers] are appropriate for spatial analysis? The following are examples of long term (five to ten years) fundamental research projects:

Methods, techniques, and approaches are needed for analysis of:

· Massive spatial data sets: As georeferenced data sets become larger, methods must be found that use, pare, classify, and manipulate the rich information inherent in very large spatial data bases.

· Exploration of spatial and space-time data: Exploratory spatial data analysis must be extended to space-time data in order to develop models that better represent reality.

· Confirmatory (significance) procedures: Statistical (Bonferroni-type) procedures must be found that recognize the dependence of georeferenced data and allow researchers to engage in testing hypotheses.

· The variogram and kriging: These useful geostatistical procedures are just now being incorporated into GIS frameworks. Research must go forward in this area in order to take full advantage of GIS as a research tool, including the incorporation of these

procedures in broader analyses and models of continuous spatial phenomena..

· The impact of scale and the development of scale independent procedures: Perhaps most fundamental to preparing GIS for spatial analytical work are the modules and algorithms that evaluate the effect of scale change on research results. The development of scale correctives and scale independent methods is a compelling research need. There is great research interest in the question of the extent to which interzonal analysis is affected by the configuration and regionalization of spatial data units.

· Global versus local effects: Global analysis of massive data sets has proved superficial and inadequate. There is a need for procedures and tests to use window, kernel, individual and other local measures to find and equate the characteristics of non-stationary spatial data.

· Identification of essential or extreme observations: Procedures must be found that can identify in a spatial setting key observations, groups of observations (clusters), or hot spots that draw attention to the anomalous regions.

· Devising computationally intensive procedures: There is a need to interface GIS with tools that take advantage of great increases in the capabilities of computational platforms. Computationally intensive tools can allow for: 1) large data sets to be used effectively, 2) more sophisticated and extensive simulations of complex spatial phenomena, and 3) the solution of complex location and distribution problems (see below). Potential techniques include neural nets, fuzzy sets, wavelets, microsimulation, artificial intelligence, natural language processing of textual information, artificial life, real-time data analysis, numeric optimization techniques and massively parallel algorithms. These new techniques should be evaluated in a GIS environment for their usefulness in practical applications.

· Econometric modeling in a GIS environment: Spatial econometrics is a new and burgeoning field. It is important to attempt to link the sophisticated procedures of the econometrician with the functionality and flexibility of GIS. In addition, there is a need to find appropriate estimators and testing devices for heterogeneous, non-uniform geo-referenced data.

· Spatial interaction models in a GIS framework: Perhaps one of the most used model types among spatial analysts is the spatial interaction-type model. Developing these models in a GIS environment will provide marketing, transportation, and human interaction specialists with greater analytical power than is currently available. Also needed are techniques for visualizing spatial interaction flows in a sophisticated manner.

· Operations research: Many fields benefit greatly from the functionality and data manipulative power of GIS, including a such operations research type problems as routing, location-allocation, coverage and other optimization procedures. Most existing techniques require simplistic representations of spatial objects and relationships for the sake of tractability. Needed are GIS-based solution procedures that can manipulate spatial entities in their native form and recognize the complex spatial relationships that occur between these entities.

ReferencesAnselin, L., 1996. SpaceStat, Version 1.80. Morgantown: Regional Research Institute, West

Virginia University.Anselin, L., and A. Getis, 1992. Spatial statistical analysis and geographic information systems.

Annals of Regional Science 26: 19-33.Cressie, N., 1991. Statistics for Spatial Data. New York: Wiley.Fischer, M.F., and P. Nijkamp, editors, 1993. Geographic Information Systems, Spatial

Modeling and Policy Evaluation. Berlin: Springer-Verlag.Fotheringham, S., and P. Rogerson, editors, 1994. Spatial Analysis and GIS. London: Taylor

and Francis.Kaluzny, S.P., S.C. Vega, T.P. Cardoso, and A.A. Shelly, 1996. S+SpatialStats: User's Manual,

Version 1.0. Seattle: MathSoft, Inc.Miller, H.J., 1996. GIS and geometric representation in facility location problems.

International Journal of Geographic Information Systems 10(7): 791-816.Openshaw, S., 1994. Two exploratory space-time attribute pattern analyzers relevant to GIS. In

S. Fotheringham and P. Rogerson, editors, Spatial Analysis and GIS. London: Taylor and Francis.

Openshaw, S., 1996. Parallel simulated annealing and genetic agorithms for re-engineering zoning systems. Geographical Systems 3: 201-220.

Arthur Getis, San Diego State University, 23 October 1996.

THE FUTURE OF THE SPATIAL INFORMATION INFRASTRUCTURE

ObjectiveEffective policies, strategies, and organizational arrangements together constitute a spatial information infrastructure (SII), and are necessary to facilitate the sharing, integration, and use of spatial information across a broad set of government, industry, academic, and public sectors. Our goal is to address the challenges of incorporating a wide variety of spatially-referenced information into various problem solving domains, serving purposes as diverse as education, government and industry. Specifically, through this effort UCGIS will:

· provide analytical research and support in spatial information policy development and management relevant to government agencies and non-government organizations;

· help decision makers evaluate and understand the likely consequences of their information policy decisions and choose among alternative policies; and

· improve the public and private sector understanding of ways in which spatial information policy and technologies can better serve broad societal needs.

We will address such issues as ownership of digital spatial data, protection of privacy, liability, access rights to the spatial data compiled and held by governments, and the economics of spatial information production and dissemination. The goal is to help policy makers, scientists, business leaders, and citizen groups understand the relationships between government policy and spatial information resources, services and infrastructure—and so facilitate the accelerated growth and utilization of spatial information resources toward meeting societal needs.

BackgroundIn the early 1990s, the National Research Council's Mapping Science Committee articulated a vision of how spatial information handling might best be approached from an organizational perspective (NRC, 1993). This led to a plan for the creation of a national spatial data infrastructure (NSDI) and its recognition as critical to serving national priorities (Office of President, 1994). In addition, a list of designated executive science and technology priorities, such as science education, technology transfer, high-performance computing and networking, digital libraries, global change and international competitiveness, all have significant spatial information components, as do traditional land management activities. These priorities are mirrored at state and local levels of government which address similar issues at their levels. However, there is growing need for increased coordination between programs, and to make the outcomes of these activities appropriate and available to address social needs.

Information about the character and location of natural and cultural resources and their relationship to human and economic activities is essential to making decisions about the future. In response to this need, geographic information systems and associated technologies have proliferated rapidly in recent years among all levels of government, academia, and industry. Government agencies and the scientific community are using digital geographic data and

technologies for such purposes as forecasting weather, utilities management, emergency vehicle routing, and aircraft navigation. The commercial and private sectors are routinely using geographic information for such purposes as customer needs assessment, facilities and inventory management, precision farming, site location, car navigation, and a host of similar activities.

Despite the large investments in spatial data development by government and the private sector, there is often a lack of knowledge of, and experience with, the complex policy-related issues arising from the community-wide creation, compilation, exchange, and archiving of large spatial datasets. Technical, legal, and public policy uncertainties interact, making it difficult to utilize information resources fully in pursuit of social goals. The ownership of digital spatial data, protection of privacy, access rights to the spatial data compiled and held by governments, and information liability are concepts that require clarity in the new, automated context. Observations of the ramifications of following different policy choices are needed to provide guidance for the future.

The government sector plays an important role in developing the fundamental spatial information infrastructure due to its activities in the systematic collection, maintenance, and dissemination of spatial data. These resources have significant uses beyond their governmental purposes. For example, subsequent use of spatial information by organizations can stimulate the growth and diversity of the information services market. At the same time, public access to government information remains essential to ensuring government accountability and democratic decision making. Reconciliation of the tensions inherent in these and other policies becomes more important as we move toward global economies and international networked environments. Rigorous and impartial analysis is urgently needed to inform decision makers on the economic, legal and political ramifications of choosing one policy over another.

The UCGIS ApproachThe SII research agenda is based on three tenets, which motivate the program's mix of natural and social science perspectives:

· Technology and polity, institutions and traditions: Democratic governments must develop in ways that enhance public participation while making responsible use of science and technology.

· Technical facts matter: The institutional policy issues associated with spatial information infrastructure development cannot be fully understood without an understanding of the technical component of public issues.

· Information policy issues arise at all levels, from local to global: Each jurisdiction, whatever its size, has its own culture and set of practices. In the modern, automated communications environment, these jurisdictions are less independent and influence one another in new ways.

Research activities will draw upon UCGIS specialists from various academic disciplines including information science, planning, law, economics, geography, political science, and engineering as contributors to project work. In addition, it will convene experts from government, industry, and academia and draw on their perspectives and insights. Rigorous analysis of local, state, national, and international initiatives will be undertaken from independent, multidisciplinary perspectives. UCGIS researchers will employ various

methodologies (e.g., surveys, case studies, impact assessment, comparative analysis) to evaluate the ramifications of alternative legal, economic, and information policies. The multidisciplinary expertise of the Consortium will deliver a form of comprehensive analysis that would not be possible otherwise.

Importance to National Research NeedsThree important activities will be advanced by this research:

· The first is stimulating economic growth in the geographic information industry. Government policy and practice in regard to both technology and information can contribute signficantly to stimulating economic activity. Governments create and consume great quantities of data and technology (in our context, GIS software, global positioning systems, remote sensing). How it obtains and distributes this material is important both to itself and to society in general. In addition, a large and growing private information industry functions in part by adding value to government data.

· The second activity is strengthening institutional capacity. An educated and specially trained workforce is an important component to building Spatial Information Infrastructure capacity. This area of development activities will focus on the training and education of people with the knowledge, skills, and insights in geographic information science and technology as well as institutional factors.

· The third activity is promoting democratic processes. New geographic information technologies can make it easier for the public to obtain access to government information, and to become involved as stakeholders in land-related decisions. Broader participation by the public will in turn result in broader voter support for system investments.

BenefitsIncreased research in support of future spatial information infrastructure will:

· improve efficiency, effectiveness, and equity of investments in spatial information;

· strengthen institutional capacity;

· promote the continued growth of the domestic geographic information industry; and

· enhance public access and participation in land-related decisions.

Since government institutions are the single largest producers of spatial information, they can serve as model developers of a spatial information infrastructure that promotes the community-wide sharing and use of spatial data and technology. The social and economic benefits of sharing these resources with public, private, and other government sectors have yet to be realized. The advance of electronic networks (Internet, the World Wide Web, intranets, etc.) have made it practical to share data among many organizations at all levels and over great distances.

Priority Areas for ResearchThe principal activities that underlie SII research and development can be assigned to four broad

areas:

· Information Policy: The factors that shape the development of spatial information policy and law reflect traditional and contemporary culture and technology. Research will identify optimal government information policies and practices for promoting a robust spatial information infrastructure. Basic policy issues include intellectual property rights, information privacy, and liability, as they pertain to spatial data. A range of perspectives, from local to global, will be considered.

· Access to Government Spatial Information: In this area, research will examine how government information policies affect access to, and use of, data to a broad spectrum of public and private sector stakeholders for a variety of public and private (commercial) purposes. Public and private roles in information creation (e.g., partnerships, cooperative research and development agreements, etc.) will be a subject of particular attention.

· Economics of Information: Spatial information is an unusual commodity of great value. Issues of cost recovery, pricing, and markets for spatial data, and their relationship to intellectual property rights, are of central importance. Understanding the economic characteristics of information, especially government information, is important. Applicable concepts include public goods theory, network externalities, and processes of value addition.

· Local Generation and Integration of Spatial Information: Locally generated information and knowledge is increasingly important because the technology makes it possible for local people to obtain and use local data more effectively. Contributions can be systematic or ad hoc, coming from civic groups, schools, local institutions, and informed individuals. Local users can make significant contributions of their own local knowledge, identify gaps in existing data resources, or identify erroneous observations. Developing the technical and institutional means to support creation and contribution of local knowledge presents a novel challenge to technologists and decision makers.

The following are examples of specific projects which could be undertaken, all within the context of spatial information infrastructure.

· Conduct real-time case studies designed to measure the effects of different legal, economic, and information policy choices on development of spatial information infrastructures.

· Assess the Federal Geographic Data Committee (FGDC) or state GIS projects funded to date, assessing costs, benefits, effectiveness, and efficiencies, and identifying aspects of current government information policies which require revision or improvement.

· Develop curriculum, educational programs, and professional training to build information resource management (IRM) capacity for managing digital spatial information libraries.

· Develop alternative strategies for increasing public access to government information based upon digital and other emerging dissemination and retrieval technologies.

· Examine the role of pricing and cost recovery practices on public access and commercial uses of data.

· Compare local, state, and national government dissemination policies as a means for analyzing alternative approaches for allocating public and private funds to sustain

government investments in spatial information infrastructure.

· Develop guidelines for increasing public participation in the identification, creation, use, and exchange of relevant spatial information resources to inform community decision making.

· Experiment with local knowledge-based collaborative projects which incorporate various information types to support public awareness and enhance decision making processes.

· Model the components and dimensions of an expanded view of the SII, focusing on technology and institutional developments and how they are embedded in other processes and media.

ReferencesBranscomb, L.M., 1993. Empowering Technology. Cambridge, MA: MIT Press.McClure, C., P. Hernon, and H. Relyea, 1989. United States Government Information Policies:

Views and Perspectives. Norwood: Ablex Publishing.National Research Council, 1993. Toward a Coordinated Spatial Data Infrastructure for the

Nation. Washington, DC: National Academy Press.National Research Council, 1996. The Unpredictable Certainty: Information Infrastructure

Through 2000. Washington DC: National Academy Press.Office of the President, 1994. Coordinating Geographic Data Acquisition and Access: The

National Spatial Data Infrastructure Presidential Executive Order 12906 (April 11, 1994). Washington, DC.

Perritt, H.H. Jr., 1996. Law and the Information Highway. New York: John Wiley & Sons.Varian, H., 1995. The Information Economy: How much will two bits be worth in the digital

marketplace? Scientific American (1 September), p. 200.

Harlan J. Onsrud, University of Maine; Xavier R. Lopez, University of California, Berkeley; Lyna Wiggins, Rutgers University; 29 September 1996

UNCERTAINTY IN SPATIAL DATA AND GIS-BASED ANALYSES

ObjectiveThis research priority calls for a systematic effort to advance the understanding of uncertainty associated with spatial data and the propagation of this uncertainty through GIS (Geographic Information Systems) based data analyses. This understanding is required for developing strategies for managing uncertainty in decision-making involving spatial data. Strategies are needed for identifying, quantifying, tracking, reducing and reporting (including visualizing) uncertainty in spatial data and GIS-based analyses, and for development of standardized means by which uncertainty can be addressed in daily GIS applications.

BackgroundGeographic data (used interchangeably with spatial data in this document) are unique. A datum about a geographic feature contains three different kinds of attributes: the typological attributes (describing the type of a geographic feature), the locational attributes, and the spatial dependence (the spatial relationship with other features). For example, a datum about a forest can be the type and species combination of the forest (as typological attributes), the location and spatial extent of the forest (the locational attributes) and its relationships with its surrounding landscape features (spatial dependence). These attributes also change over time making geographic data very complex and difficult to manage.

Geographic data are observations of geographic features or phenomena (referred to as geographic reality). Geographic reality often cannot be exhaustively observed (measured) since it is next to impossible to obtain measurements for every point over a landscape. It is also difficult to measure geographic reality accurately due to its continuous (slow or rapid) variation over time and due to the limitations of instruments, financial budgets, and human capacity. In these respects, geographic data at creation are only approximations of geographic reality. Furthermore, the basic schemes (Couclelis 1992) used to represent geographic data in GIS are not dynamic and deal only with a static, invariable world. They do not deal with complex objects which may consist of interacting parts, or display variation at many different levels of details over space and over time. A discrepancy therefore exists between geographic data and the reality these data are intended to represent. This discrepancy propagates through, and may be further amplified by, spatial data management and analyses in a GIS environment. Thus uncertainty is an integral part of results from GIS analyses.

Uncertainty is an index (indicator) used to approximate the discrepancy between geographic data in GIS and the geographic reality these data are intended to represent. Uncertainty information associated with a geographic data set should be perceived as a map depicting varying degrees of uncertainty associated with each of the features or phenomena represented in the data set. The difference between uncertainty and error is that uncertainty is a relative measure of the discrepancy while error tends to measure the actual value of this discrepancy (Goodchild et al. 1994 p.142 and Hunter et al. 1995). Since the true value for every geographic feature or phenomenon represented in a geographic data set is rarely determinable and the exact value of this discrepancy for every feature or phenomenon cannot be obtained in most cases, uncertainty should be used instead of error to describe the quality of geographic data

and the products from GIS. Uncertainty in geographic data consists of three components: uncertainty in the typological attributes, uncertainty in the locational attributes, and uncertainty in spatial dependence. These issues are important because increasingly geographic data and GIS techniques are used to support policy decisions. Geographic data are often used under the assumption that they are free of errors. The beguiling attractiveness, the high aesthetic quality of cartographic products from GIS and the analytical capability of GIS further contribute to an undue credibility, at times, of these products (Abler 1987 p. 305). However, undeserved and inappropriate acceptance of the accuracy of these data is often not warranted due to reasons discussed above (Goodchild and Gopal 1989 pp. xii-xiii). Error-laden data, used without consideration of its intrinsic uncertainty, has a high probability of leading to inappropriate decisions.

The UCGIS ApproachUncertainty exists in every phase of geographic data life cycle (data collection, data representation, data analyses, and final results), transcending boundaries of disciplines and organizations. The proposed research involves an inter-institutional (UCGIS) research team consisting of domain experts, GIS experts/spatial statisticians, application users including decision makers, data producers, and GIS software vendors to plan and execute the following research tasks:

1) To study and understand the detailed mechanics of uncertainty origination in geographic data and the specific propagation processes of this uncertainty through GIS-based data analyses.

2) To develop techniques for reducing, quantifying, and visualizing uncertainty in geographic data, and for analyzing and predicting the propagation of this uncertainty through GIS-based data analyses.

3) To participate in prompting and advising a testing institute for:

· testing new methods of managing uncertainty in geographic data and GIS analyses;

· implementing strategies and methodologies for reducing, quantifying, tracking, and reporting uncertainty in GIS implementation, in geographic data collection and generation, and in spatial data standards and decision making processes;

· streamlining research findings on uncertainty in daily GIS applications.

Importance to National Research NeedsIncreasingly, policy decision-making involves the use of geographic data and GIS techniques. For example, making detailed policy decisions on how to preserve the Florida Everglades calls for detailed analyses of the environment using state-of-the-art GIS technology and geographic data about the Everglades. Location and allocation of urban resources (such as transportation planning, fire station location and fire truck routing, school zoning, etc.) often employ the use of GIS techniques and geographic data. The reliability of the resulting policy decisions very much depends on the quality of geographic data used for reaching these decisions since the quality of data affects the quality of decisions and the evaluation of decision alternatives.

Concern about uncertainty intrinsic to spatial data and analyses is not new, but systematic efforts to study the problem are much more recent. Data errors and uncertainties in GIS were

identified in the research agenda of the National Center for Geographic Information and Analysis (NCGIA) as one of the most important impediments to the successful implementation of GIS (NCGIA 1989). The Center devoted its first research activity to the accuracy of spatial databases. Progress has been made in this field since then (Goodchild and Gopal 1989, Goodchild 1992a, and Mowrer et al. 1996). Despite recent progress, most of the research findings are applicable only to artificial or exhaustively well-known data sets and there is still much which remains unknown. The current state of GIS technology in dealing with uncertainty falls short of the goal described by Goodchild (1993 p. 98) in which: 1) each object in a GIS database would carry information describing its accuracy; 2) every operation or process within a GIS would track and report error; and 3) accuracy measures would be a standard feature of every product generated by a GIS. The research proposed here calls for a major systematic effort to attack this deficiency in GIS research.

Benefits

Societal benefit· With a thorough understanding of uncertainty in spatial data, and of the availability of tools

for quantifying and visualizing this uncertainty, the quality and proper uses of large amounts of existing spatial data can be identified. The tremendous amount of energy and dollars invested in collecting these data will then become more cost-effective.

· With the ready availability of uncertainty information about spatial data, decision makers would be able to make better evaluations of decision alternatives in terms of risk involved by using the uncertainty information about the supporting spatial data.

Benefit to the development of geographic information science· Geographic data are the fuel of GIS. The future well-being of GIS in society largely

depends on the quality and the proper use of this "fuel". The proposed research will directly contribute to the understanding and the documentation of the quality and the proper use of geographic data, and therefore substantially enrich the general knowledge pool in geographic information science.

· An inter-institutional and inter-disciplinary approach (the UCGIS approach) will facilitate communication among the different parties dealing with spatial data. New needs and demands in GIS can be identified and advancements in the field can be quickly transferred into daily operations of GIS. This interaction will certainly advance the healthy development of geographic information science as a field.

Benefit to the field of spatial data uncertainty research· A systematic effort on attacking the uncertainty problem in spatial data will help to

streamline the existing research efforts and findings, will enrich the knowledge on uncertainty associated with spatial data, and will invent and perfect the strategies for managing uncertainty in GIS analyses and in decision-making processes.

· By streamlining the existing research efforts and findings, isolated research findings can be transferred out of laboratory settings into daily GIS operations making investments in

these research efforts more cost-effective.

Priority Areas for Research

Short term research areas· We will develop extensions to existing models of spatial data representation to allow the

representation of the spatial variation of uncertainty within a geographic data set. Under the new extensions, the spatial variation of uncertainty about geographic features or phenomena represented in a data set is an integral part of that data set.

· With the new extensions in place, we will develop and test quantification techniques for measuring or estimating uncertainty associated with each geographic feature or phenomenon present in the data so that every geographic data set at its creation is accompanied by detailed information on the spatial variation of its uncertainty.

· We will study and develop effective tools for presenting uncertainty in spatial data, particularly visualization techniques including visual and audio methods. Effective means of visualizing uncertainty in spatial data must be able to show the variation of the three uncertainty components (typological, locational, spatial dependence) over space.

Medium term research areas· We will study the effects of data processing on the uncertainty present in a single geographic

data set, will develop methods to quantify these effects and integrate these effects into the uncertainty reported in that data set.

· We will study and model the uncertainty due to the incompatibilities among spatial data from different scales which are to be analyzed together, and will quantify and report this uncertainty as part of uncertainty propagating through overlay processes.

· We will conduct applied research on linking research findings on uncertainty from various stages of data creation and data analyses via case studies. The purpose is to streamline the research findings in terms of routine GIS applications and to demonstrate how these research findings can be used in daily GIS applications.

· We will study how the uncertainty information on spatial data and analyses can be properly used in decision making in terms of risk analyses. The impact of reporting uncertainty on decision-making will also be thoroughly examined.

Long term research area· Current representation schemes take a slice of geographic reality in the spatial, temporal,

and attribute domains. This simple, static and limited representation of the complex, dynamic geographic reality is the major source of errors in spatial data. We will investigate new schemes for representing geographic reality as a complex, dynamic whole. These new schemes will ultimately allows us to minimize the uncertainty present in the spatial data.

· Propagation of uncertainty from different data sources, together with uncertainty due to scale incompatibility, into final results through integrated spatial analyses is the most

important aspect of our proposed research. When all spatial data are integrated in the decision deriving process using GIS, the interaction of all uncertainty present in the spatial data should be understood. The relationships between the uncertainty in the source data and that in the final results should be established so that the uncertainty in the final result can be estimated and reported.

ReferencesAbler, R.F., 1987. The National Science Foundation National Center for Geographic

Information and Analysis. International Journal of Geographical Information Systems 1: 303-326.

Couclelis, H., 1992. People manipulate objects (but cultivate fields): Beyond the raster-vector debate. In A.U. Frank, I. Campari, and U. Formentini, editors, Theories and Methods of Spatio-Temporal Reasoning in Geographic Space. Lecture Notes in Computer Science 639. Berlin: Springer-Verlag, pp. 65-77.

Goodchild, M.F., and S. Gopal, editors, 1989. Accuracy of Spatial Databases. New York: Taylor and Francis.

Goodchild, M.F., 1992. Closing Report, Research Initiative 1: Accuracy of Spatial Databases. Santa Barbara, CA: National Center for Geographic Information and Analysis.

Goodchild, M.F., 1993. Data models and data quality: problems and prospects. In M.F. Goodchild, B.O. Parks, and L.T. Steyaert, editors, Environmental Modeling With GIS. New York: Oxford University Press, pp. 94-103.

Goodchild, M.F., B.P. Buttenfield, and J. Wood, 1994. Introduction to visualizing data quality. In H.M. Hearshaw and D.J. Unwin, editors, Visualization in Geographical Information Systems. New York: John Wiley and Sons, pp. 141-149.

Hunter, G.J., M. Caetano, and M.F. Goodchild, 1995. A methodology for reporting uncertainty in spatial database products. Journal of the Urban and Regional Information Systems Association 7: 11-21.

NCGIA, 1989. The research plan for the National Center for Geographic Information and Analysis. International Journal of Geographical Information Systems 3(2): 117-136.

A-Xing Zhu, University of Wisconsin, 29 August 1996.

GIS AND SOCIETY

ObjectiveThe geographic information system (GIS) is becoming a routine analysis and display tool for spatial data, and used extensively in applications such as land use mapping (for urban planning purposes), transportation mapping and analysis (for determining efficient transportation routes for deliveries and emergency response), geodemographic analysis (for facilities location), utilities infrastructure mapping (for precise gas, water, and electric line mapping), and multiple applications in natural resource assessment (including water quality assessment and wildlife habitat studies). GIS allows efficient and flexible storage, display, and exchange of spatial data, as well as use in models of all kinds. Users include county and city governments, state and federal agencies, non-governmental organizations such as conversation groups, universities, and research institutes.

The overarching concern in the societal use of GIS is how this technology will influence the political, economic, legal, and institutional structures of society, and how those may influence GIS development. Access to information technology is seen by many as a potential for enormous improvement in the lifestyle choices of most Americans (Gingrich, 1995, p. 60). Research on the interplay between GIS and society addresses the relationships that exist between GIS practice and the understanding of the social and physical process of alteration, use, and perception of land and water; the resultant cultural and natural spaces created by such processes; and the means to represent human understanding of such spaces.

BackgroundThe theories and methods of observation for studying issues in GIS and society are not well developed and thus present a research challenge. However, there are several theories and methods that can or may provide a basis for meeting this challenge. Two of these are discussed here.

(1) Critical social theory approaches (e.g., Pickles et al., 1995)

(2) Economic, political, legal, and institutional approaches (Kishor et al., 1990; Ventura, 1995)

Critical Social Theory PerspectiveThree fundamental issues may be identified from a social theory perspective on GIS. The first concerns the limits of representation of populations, locational conflict, and natural resources to be found within current GISs, and the extent to which these limits are extendible by evolving technologies of data capture and storage, manipulation, visualization and functionality. Investigation of representations inherent to GIS involves first analyzing the current situation: the mode(s) of reasoning utilized within GIS hardware and software, its functionality, and the influence of data availability and digital representation on system design and output.

The second issue concerns the impacts of these storage and representational limits and impediments, particularly in the context of other societies (both societies of developing nations and low-income groups and minorities in the United States), and of the potential inequalities in

access to necessary software, hardware, and technical skills, on the outcomes that result from the application of GIS to decision making. This issue becomes vital when the groups affected by such decision-making possess opposing interests, unequal access to political or financial resources, or very different ways of perceiving and making sense of the world. It is necessary to examine what types of knowledge and forms of reasoning are not well represented within a GIS as used in decision-making situations, the importance of such knowledge and reasoning to a decision-making process in which all viewpoints and social groups are democratically represented, and the consequences of its exclusion from GIS.

The third issue concerns the need to pay attention to the developing potential of spatial technologies. There must be a broadening of access to GIS, which addresses impediments to its uses in decision-making situations involving competing interests and forms of knowledge, as well as making recommendations about the legal and ethical issues posed by GIS.

The Institutional PerspectiveThe considerable investment, both public and private, in spatial information technologies is accompanied by various levels of uncertainty surrounding the value and impact of this investment. The value of this investment needs to be justified in terms of benefits to society and there is ample opportunity to more carefully investigate these benefits (Goodchild, 1995, p. 41). Our current understanding of benefits and impacts is primarily in the realm of measures of efficiency—financial benefits to implementors arising from improved means to produce information. Measures of benefits must be developed that incorporate an understanding of the role of the technology and the information it provides in decision-making about land and resources. Assessment of technological impacts must include issues of equity, including the distribution of costs and benefits among individuals and between components of society.

Previous research in this general area has been directed toward implementation processes and benefit measures in terms of efficiency. For example, we have measured the status of implementation on a state-wide basis and assessed the impact of GIS and land information systems (GIS/LIS) in terms of efficiencies that the technology brings to traditional activities. However, societal implications cannot be fully understood without studying the impact of systems' products on expectations arising from the broader economic, legal, political, and cultural context. We need to develop theories—as well as measurement tools and techniques—for determining how spatial information influences land and policy decisions. They should incorporate concepts of the effectiveness and equity of decisions.

The products of spatial information technologies are changing (and will continue to change) the economic, legal, political, and cultural status of adopting agencies, decision-makers using the products, and the people and organizations affected by the decisions. While early impacts of GIS/LIS are becoming evident, little is known or understood about the long-term effects that the products of these technologies will have on the communities and organizations that implement them. We should observe, and ultimately be able to predict, how spatial information technology and products alter decision-making processes within organizations, interactions between agencies, the citizen's relationships with government agencies, and people's beliefs and actions in regard to the use and management of land and resources. One can also point to the importance of GIS's current and potential application in epidemiological studies and in the increasing use of GIS in restructuring political districts, each with tremendous potential

impact on societal concerns.

The UCGIS ApproachResearch on the topic of GIS and society requires the involvement of those disciplines that understand human cognition and perception; those that understand the means by which cultural and natural spaces can be or should be represented; and those who use this information for social, political, legal and economic purposes. In essence, UCGIS needs to facilitate interest and involvement in the topic of GIS and society research by many disciplines. Without a firm understanding of the consequences of GIS use, much effort may be wasted or lost on more technology and good intentions with little benefit and possibly misunderstanding.

Through a research agenda the UCGIS will seek answers to the following questions about GIS and society:

· In what ways will GIS actually affect and/or alter the society it is intended to represent and analyze?

· How can various conceptions and representations of space, not based on traditional map (Euclidean) views, be embedded within a GIS? Is GIS more or less appropriate for some cultures versus others? Can GIS be developed to reflect complex and ambiguous perceptions of social and physical space?

· How will GIS affect the relationships amongst and within government agencies, government agencies and individuals, and non-governmental groups?

· What are the interpersonal implications of GIS? Interaction at the individual level underpins all other relationships.

· Can GIS provide citizens with an increased ability to monitor and hold government accountable for proposals and actions?

· Will GIS provide citizens with a better understanding of their rights and interests in land?

· How accessible will spatial data and related GIS analysis tools be to all aspects of society?

· Can GIS be used to increase participation in public decision-making?

· Can GIS be developed to reflect complex and ambiguous perceptions of social and physical space?

The Importance to National Research NeedsBasic research in the effect of GIS on society, and relationship between them, is of significance to the national research agenda for a multitude of reasons. The technology is now found in nearly all Federal and state government agencies, and is now rapidly being used by local governments, environmental organizations, and even neighborhood organizations. Increasingly spatial data are being shared amongst these organizations. The technology has metamorphosed beyond a simple mapping tool to a methodology that is used for urban planning, environmental monitoring/analysis, and understanding complex spatial problems. Most importantly, it is increasingly being used for human / natural resource policy formulation. It is thus timely to now gain a clearer understanding of the societal link to the technology and science of GIS. Some

specific goals and projects include:

· The use of GIS to assist in assessing environmental justice issues, including the relationship amongst Toxic Release Inventory (TRI) sites, toxic dumps, Superfund sites, and human populations.

· Determining the cost effectiveness of GIS technology to various local governments in comparison with other non-spatial approaches.

· Promote data sharing amongst various government agencies.

· Provide an understanding of both the type and rate of GIS investment and the forces and factors in Multi-Purpose Land Information Systems (MPLIS) development.

· Assess the potential for developing a GIS which combines conventional socio-economic, environmental, and infrastructure data with non-conventional behavioral and cognitive information. For instance, how can the geography of Los Angeles gangs be represented in GIS?

· How might spatial data be used by environmental and community groups for empowerment and conflict resolution?

· Determine what parts of society will have access to spatial information.

Based on these general goals / projects, several, more detailed, potential projects may be used to articulate the importance of GIS and society basic research.

Project 1. Community Investment and Benefits of Multi-Purpose Land Information Systems in Wisconsin and OhioEven though the GIS industry is recognized to be a one billion dollar industry (Goodchild, 1995, p. 41), little is known about the type and the rate of investment, forces and factors inhibiting or accelerating investments, or the nature and extent of benefits perceived or derived by the various investments in GIS by local US governments. An example of research to better understand these dynamics is now underway for MPLIS development in Wisconsin and Ohio. Results in Wisconsin indicate major investments in MPLIS development are now underway in all 72 of Wisconsin's counties. Even though various impediments exist, such as limited GIS expertise and associated complexities of database development and representation, about $60 million dollars has been invested by local governments since enactment by the Wisconsin legislature of the Wisconsin Land Information Program in 1989 (Tulloch and Niemann, 1996). Even though not to the same extent, initial results in Ohio show major interest in implementing MPLIS development and the beginnings of significant investments (Epstein et al., 1996).

Initial conclusions suggest that type and rate of investment in MPLIS will continue, if not accelerate, as the GIS technology becomes more mature. Also anecdotal evidence begins to suggest that various types of benefits are beginning to accrue in the view of those responsible for MPLIS development. These include saving money or avoiding new costs (efficiency benefits), providing new governmental services (effectiveness benefits), and broad social access to participation in these efficiency and effectiveness gains (equity benefits).

Project 2. Community Use of GIS for Assessing Environmental Risk Making use of a GIS developed for an analysis of Toxic Release Inventory (TRI) sites in the Twin Cities, a group of researchers is examining the different ways that grassroots organizations utilize (or don't utilize) available information in dealing with technological hazards. An examination of a series of neighborhoods in Minneapolis, based on the reactions of community and environmental groups to TRI / geodemographic data in GIS format, has involved four types of questions. First, how available and appropriate is GIS information for grassroots organizations? Second, what impact does access to GIS-based information have on the participation and effectiveness of grassroots organizations in policy discussions and policy making? Third, does utilization of GIS affect the mission and outlook of grassroots groups? And fourth, what difference does availability and utilization of GIS-based information make for the wide variety of different groups affected by environmental hazards?

Priority Areas for ResearchA recent specialist meeting on GIS society (Harris and Weiner, 1996) sponsored by the National Center for Geographic Information and Analysis identified several areas for research:

· In what ways have particular logics and visualization techniques, value systems, forms of reasoning, and ways of understanding the world been incorporated into existing GIS techniques and in what ways do alternative forms of representation remain to be explored and incorporated (Sheppard, 1995)?

· How has the proliferation and dissemination of databases associated with GIS, as well as differential access to these databases, influenced the ability of different social groups to utilize this information for their own empowerment?

· How can the knowledge, needs, desires, and hopes of non-involved social groups adequately be represented as input to a decision-making process, and what are the possibilities and limitations of GIS as a way of encoding and using such representations (Harris et al., 1995; Weiner et al., 1995)?

· What possibilities and limitations are associated with using GIS as a participatory tool for democratic resolution of social and environmental conflicts?

· What implications do the results of GIS and society research have for the types of ethical and legal restrictions that should be placed on the access to, and use of GIS?

ReferencesEpstein, E.F., D.L. Tulloch, B.J. Niemann, S.J. Ventura, and F.W. Limp (1996) Comparative

study of land records modernization in multiple states. Proceedings, GIS/LIS '96.Gingrich, N. (1995) To Renew America. New York: Harper Paperbacks.Goodchild, M.F. (1995) Geographic information systems and geographic research. In J. Pickles,

editor, Ground Truth: The Social Implications of Geographic Information Systems. New York: The Guilford Press.

Harris, T.M., and D. Weiner (1996) GIS and Society: The Social Implications of How People, Space, and Environment are Represented in GIS. Technical Report 96-7. Santa Barbara, CA: National Center for Geographic Information and Analysis.

Harris, T.M., D. Weiner, T.A. Warner, and R. Levin (1995) Pursuing social goals through participatory GIS? Redressing South Africa's historical political ecology. In J. Pickles, editor, Ground Truth: The Social Implications of Geographic Information Systems. New York: Guilford Press.

Kishor, P., B.J. Niemann, Jr., D.D. Moyer, S.J. Ventura, R.W. Martin, and P.G. Thum (1990) Lessons from CONSOIL: evaluating GIS/LIS. Wisconsin Land Information Newsletter 6: 11-13.

Pickles, J., editor (1995) Ground Truth: The Social Implications of Geographic Information Systems. New York: The Guilford Press.

Sheppard, E. (1995) GIS and scoiety: toward a research agenda. Cartography and Geographic Information Systems 22(1): 5-16.

Tulloch, D., and B.J. Niemann Jr. (1996) Evaluating innovation. Geo Info Systems (September).Ventura, S.J. (1995) The use of geographic information systems in local government. Public

Administrative Review 55(5): 461-467.Weiner, D., T.A. Warner, T.M. Harris, and R.M. Levin (1995) Apartheid representations in a

digital Landscape: GIS, remote sensing and local knowledge in Kiepersol, South Africa. Cartography and GIS 22(1): 30-44.

Wisconsin State Interagency Land Use Council (1996) Planning Wisconsin: Report of the State Interagency Land Use Council. Mark Bugher, Chair, Wisconsin State Interagency Land Use Council.

Robert B. McMaster, University of Minnesota; Bernard Niemann, Stephen Ventura, D. David Moyer, David Tulloch, University of Wisconsin - Madison; Earl Epstein, The Ohio State University; Gregory Elmes, University of West Virginia; 6 November 1996.