Social issues, AI, and climate change are just a few of the disruptive focuses impacting science. The field of GIScience is well positioned to respond to accelerating disruptions due to the interdisciplinary nature of the field and the ability of GIScience approaches to be used in support of decision-making. This manuscript aims to start a conversation that will establish a research agenda for GIScience in an age of disruptions. We outline three guiding principles: (1) focusing on the relevance and real-world impact of research, (2) adopting systems-based thinking and contextual approaches and (3) emphasizing inclusive practices. We then outline prioritized research areas organized by what topics are important focal areas (Data and Infrastructure, Artificial Intelligence, and Causality and Generalizability), and what approaches to science we should be attentive to (Impactful Open Science, Collaborative and Convergent Science, and through Diverse Participation and Partnerships). We conclude with a call to increase impact by balancing slow science with practical and policy-oriented research. We also recognize that while broad adoption of spatial approaches is a signal of GIScience's success, we should continue to work together to advance core knowledge centered on spatial thinking and approaches.

Stan Openshaw (https://en.wikipedia.org/wiki/Stan_Openshaw) was a prolific British geographer, holding positions first at the University of Newcastle and then at the University of Leeds. He was a c...

A cornerstone of the scientific method, the ability to reproduce and replicate the results of research has gained widespread attention across the sciences in recent years. A corresponding burst of energy into how to make research more reproducible and replicable has led to numerous innovations. This article outlines some of the opportunities for geospatial researchers to contribute to and learn from the broader reproducibility literature. We review practices developed in related disciplines to improve the reproducibility and replicability of research and outline current efforts to adapt those practices to geospatial analyses. The article then highlights the open questions, opportunities, and potential new directions in geospatial research related to R&R. We stress that the path ahead will likely require a mixture of computational, geospatial, and behavioral research that collectively addresses the many sides of reproducibility and replicability issues.

Evidence suggests that the proportion of the human population living in cities will continue to grow, to the point where over 90% of the world’s population will be living in one form of city or ano...

A driverless or autonomous vehicle requires significant support from information technology, both from central databases and from local sensors. The requirements for route guidance are in many ways more demanding than those of current guidance technologies, especially in the ‘last mile’ of a route. Significant extensions are needed for both street-centerline and point-of-interest databases. How these should be captured and structured present significant research challenges for GIScience. The societal and longer term impacts of such extensions are perhaps even more in need of focused research by the GIScience community.

Many techniques have been proposed for visualizing uncertainty in geospatial data. Previous empirical research on the effectiveness of visualizations of geospatial uncertainty has focused primarily on user intuitions rather than objective measures of performance when reasoning under uncertainty. Framed in the context of Google’s blue dot, we examined the effectiveness of four alternative visualizations for representing positional uncertainty when reasoning about self-location data. Our task presents a mobile mapping scenario in which GPS satellite location readings produce location estimates with varying levels of uncertainty. Given a known location and two smartphone estimates of that known location, participants were asked to judge which smartphone produces the better location reading, taking uncertainty into account. We produced visualizations that vary by glyph type (uniform blue circle with border vs. Gaussian fade) and visibility of a centroid dot (visible vs. not visible) to produce the four visualization formats. Participants viewing the uniform blue circle are most likely to respond in accordance with the actual probability density of points sampled from bivariate normal distributions and additionally respond most rapidly. Participants reported a number of simple heuristics on which they based their judgments, and consistency with these heuristics was highly predictive of their judgments.

"Mastering iron: the struggle to modernize an American industry, 1800–1868." International Journal of Geographical Information Science, 27(12), pp. 2533–2534

A measure of shape compactness is a numerical quantity representing the degree to which a shape is compact. Ways to provide an accurate measure have been given great attention due to its application in a broad range of GIS problems, such as detecting clustering patterns from remote-sensing images, understanding urban sprawl, and redrawing electoral districts to avoid gerrymandering. In this article, we propose an effective and efficient approach to computing shape compactness based on the moment of inertia (MI), a well-known concept in physics. The mathematical framework and the computer implementation for both raster and vector models are discussed in detail. In addition to computing compactness for a single shape, we propose a computational method that is capable of calculating the variations in compactness as a shape grows or shrinks, which is a typical application found in regionalization problems. We conducted a number of experiments that demonstrate the superiority of the MI over the popular isoperimetric quotient approach in terms of (1) computational efficiency; (2) tolerance of positional uncertainty and irregular boundaries; (3) ability to handle shapes with holes and multiple parts; and (4) applicability and efficacy in districting/zonation/regionalization problems.

CyberGIS – defined as cyberinfrastructure-based geographic information systems (GIS) – has emerged as a new generation of GIS representing an important research direction for both cyberinfrastructure and geographic information science. This study introduces a 5-year effort funded by the US National Science Foundation to advance the science and applications of CyberGIS, particularly for enabling the analysis of big spatial data, computationally intensive spatial analysis and modeling (SAM), and collaborative geospatial problem-solving and decision-making, simultaneously conducted by a large number of users. Several fundamental research questions are raised and addressed while a set of CyberGIS challenges and opportunities are identified from scientific perspectives. The study reviews several key CyberGIS software tools that are used to elucidate a vision and roadmap for CyberGIS software research. The roadmap focuses on software integration and synthesis of cyberinfrastructure, GIS, and SAM by defining several key integration dimensions and strategies. CyberGIS, based on this holistic integration roadmap, exhibits the following key characteristics: high-performance and scalable, open and distributed, collaborative, service-oriented, user-centric, and community-driven. As a major result of the roadmap, two key CyberGIS modalities – gateway and toolkit – combined with a community-driven and participatory approach have laid a solid foundation to achieve scientific breakthroughs across many geospatial communities that would be otherwise impossible.

Li and Zhang (2012b Li, W. and Zhang, C. 2012b. Comments on ‘Combining spatial transition probabilities for stochastic simulation of categorical fields’ with communications on some issues related to Markov chain geostatistics. International Journal of Geographical Information Science, 26(10): 1725–1739. [Taylor & Francis Online], [Web of Science ®] , [Google Scholar], Comments on ‘Combining spatial transition probabilities for stochastic simulation of categorical fields’ with communications on some issues related to Markov chain geostatics) raised a series of comments on our recent paper (Cao, G., Kyriakidis, P.C., and Goodchild, M.F., 2011 Cao, G., Kyriakidis, P.C. and Goodchild, M.F. 2011. Combining spatial transition probabilities for stochastic simulation of categorical fields. International Journal of Geographical Information Science, 25(11): 1773–1791. [Taylor & Francis Online], [Web of Science ®] , [Google Scholar]. Combining spatial transition probabilities for stochastic simulation of categorical fields. International Journal of Geographical Information Science, 25 (11), 1773–1791), which include a notation error in the model equation provided for the Markov chain random field (MCRF) or spatial Markov chain model (SMC), originally proposed by Li (2007b Li, W. 2007b. Markov chain random fields for estimation of categorical variables. Mathematical Geology, 39(3): 321–335. [Crossref] , [Google Scholar], Markov chain random fields for estimation of categorical variables. Mathematical Geology, 39 (3), 321–335), and followed by Allard et al. (2011 Allard, D., D'Or, D. and Froideveaux, R. 2011. An efficient maximum entropy approach for categorical variable prediction. European Journal of Soil Science, 62: 381–393. [Crossref], [Web of Science ®] , [Google Scholar], An efficient maximum entropy approach for categorical variable prediction. European Journal of Soil Science, 62, 381–393) about the misinterpretation of MCRF (or SMC) as a simplified form of the Bayesian maximum entropy (BME)-based approach, the so-called Markovian-type categorical prediction (MCP) (Allard, D., D'Or, D., and Froideveaux, R., 2009 Allard, D., D'Or, D. and Froidevaux, R. 2009. Estimating and simulating spatial categorical data using an efficient maximum entropy approach, Avignon: Unité Biostatistique et Processus Spatiaux Institut National de la Recherche Agronomique. Technical Report No. 37 [Google Scholar]. Estimating and simulating spatial categorical data using an efficient maximum entropy approach. Avignon: Unite Biostatisque et Processus Spatiaux Institute National de la Recherche Agronomique. Technical Report No. 37; Allard, D., D'Or, D., and Froideveaux, R., 2011 Allard, D., D'Or, D. and Froideveaux, R. 2011. An efficient maximum entropy approach for categorical variable prediction. European Journal of Soil Science, 62: 381–393. [Crossref], [Web of Science ®] , [Google Scholar]. An efficient maximum entropy approach for categorical variable prediction. European Journal of Soil Science, 62, 381–393). Li and Zhang (2012b Li, W. and Zhang, C. 2012b. Comments on ‘Combining spatial transition probabilities for stochastic simulation of categorical fields’ with communications on some issues related to Markov chain geostatistics. International Journal of Geographical Information Science, 26(10): 1725–1739. [Taylor & Francis Online], [Web of Science ®] , [Google Scholar], Comments on ‘Combining spatial transition probabilities for stochastic simulation of categorial fields’ with communication on some issues related to Markov chain geostatistics. International Journal of Geographical Information Science) also raised concerns regarding several statements Cao et al. (2011 Cao, G., Kyriakidis, P.C. and Goodchild, M.F. 2011. Combining spatial transition probabilities for stochastic simulation of categorical fields. International Journal of Geographical Information Science, 25(11): 1773–1791. [Taylor & Francis Online], [Web of Science ®] , [Google Scholar], Combining spatial transition probabilities for stochastic simulation of categorical fields. International Journal of Geographical Information Science, 25 (11), 1773–1791) had made, which mainly include connections between permanence of ratios and conditional independence, connections between MCRF and Bayesian networks and transiograms as spatial continuity measures. In this response, all of the comments and concerns will be addressed, while also communicating with Li and other colleagues on general topics in Markov chain geostatistics.

Wikipedia is a free encyclopedia that anyone can edit and a popular example of user-generated content that includes volunteered geographic information (VGI). In this article, we present three main contributions: (1) a spatial data model and collection methods to study VGI in systems that may not explicitly support geographic data; (2) quantitative methods for measuring distance between online authors and articles; and (3) empirically calibrated results from a gravity model of the role of distance in VGI production. To model spatial processes of VGI contributors, we use an invariant exponential gravity model based on article and author proximity. We define a proximity metric called a ‘signature distance’ as a weighted average distance between an article and each of its authors, and we estimate the location of 2.8 million anonymous authors through IP geolocation. Our study collects empirical data directly from 21 language-specific Wikipedia databases, spanning 7 years of contributions (2001–2008) to nearly 1 million geotagged articles. We find empirical evidence that the spatial processes of anonymous contributors fit an exponential distance decay model. Our results are consistent with the prior results on information diffusion as a spatial process, but run counter to theories that a globalized Internet neutralizes distance as a determinant of social behaviors.

This article presents a new approach to automatically measure semantic similarity between spatial objects. It combines a description logic based knowledge base (an ontology) and a multi-layer neural network to simulate the human process of similarity perception. In the knowledge base, spatial concepts are organized hierarchically and are modelled by a set of features that best represent the spatial, temporal and descriptive attributes of the concepts, such as origin, shape and function. Water body ontology is used as a case study. The neural network was designed and human subjects' rankings on similarity of concept pairs were collected for data training, knowledge mining and result validation. The experiment shows that the proposed method achieves good performance in terms of both correlation and mean standard error analysis in measuring the similarity between neural network prediction and human subject ranking. The application of similarity measurement with respect to improving relevancy ranking of a semantic search engine is introduced at the end.

Categorical spatial data, such as land use classes and socioeconomic statistics data, are important data sources in geographical information science (GIS). The investigation of spatial patterns implied in these data can benefit many aspects of GIS research, such as classification of spatial data, spatial data mining, and spatial uncertainty modeling. However, the discrete nature of categorical data limits the application of traditional kriging methods widely used in Gaussian random fields. In this article, we present a new probabilistic method for modeling the posterior probability of class occurrence at any target location in space-given known class labels at source data locations within a neighborhood around that prediction location. In the proposed method, transition probabilities rather than indicator covariances or variograms are used as measures of spatial structure and the conditional or posterior (multi-point) probability is approximated by a weighted combination of preposterior (two-point) transition probabilities, while accounting for spatial interdependencies often ignored by existing approaches. In addition, the connections of the proposed method with probabilistic graphical models (Bayesian networks) and weights of evidence method are also discussed. The advantages of this new proposed approach are analyzed and highlighted through a case study involving the generation of spatial patterns via sequential indicator simulation.

In this article, the prediction problem of categorical spatial data, that is, the estimation of class occurrence probability for (target) locations with unknown class labels given observed class labels at sample (source) locations, is analyzed in the framework of generalized linear mixed models, where intermediate, latent (unobservable) spatially correlated Gaussian variables (random effects) are assumed for the observable non-Gaussian responses to account for spatial dependence information. Within such a framework, a spatial multinomial logistic mixed model is proposed specifically to model categorical spatial data. Analogous to the dual form of kriging family, the proposed model is represented as a multinomial logistic function of spatial covariances between target and source locations. The associated inference problems, such as estimation of parameters and choice of the spatial covariance function for latent variables, and the connection of the proposed model with other methods, such as the indicator variants of the kriging family (indicator kriging and indicator cokriging) and Bayesian maximum entropy, are discussed in detail. The advantages and properties of the proposed method are illustrated via synthetic and real case studies.

Areal interpolation is the procedure of using known attribute values at a set of (source) areal units to predict unknown attribute values at another set of (target) units. Geostatistical areal interpolation employs spatial prediction algorithms, that is, variants of Kriging, which explicitly incorporate spatial autocorrelation and scale differences between source and target units in the interpolation endeavor. When all the available source measurements are used for interpolation, that is, when a global search neighborhood is adopted, geostatistical areal interpolation is extremely computationally intensive. Interpolation in this case requires huge memory space and massive computing power, even with the dramatic improvement introduced by the spectral algorithms developed by Kyriakidis et al. (2005 Kyriakidis, P.C., Schneider, P. and Goodchild, M.F. 2005. “Improving spatial data interoperability using geostatistical support-to-support interpolation”. In Proceedings of geoComputation, Ann Arbor, MI: University of Michigan. [Google Scholar]. Improving spatial data interoperability using geostatistical support-to-support interpolation. In: Proceedings of geoComputation. Ann Arbor, MI: University of Michigan) and Liu et al. (2006 Liu, Y., Jiang, Y. and Kyriakidis, P. 2006. Calculation of average covariance using fast Fourier transform (FFT), Menlo Park, CA: Stanford Center for Reservoir Forecasting, Petroleum Engineering Department, Stanford University. [Google Scholar]. Calculation of average covariance using fast Fourier transform (FFT). Menlo Park, CA: Stanford Center for Reservoir Forecasting, Petroleum Engineering Department, Stanford University) based on the fast Fourier transform (FFT). In this study, a parallel FFT-based geostatistical areal interpolation algorithm was developed to tackle the computational challenge of such problems. The algorithm includes three parallel processes: (1) the computation of source-to-source and source-to-target covariance matrices by means of FFT; (2) the QR factorization of the source-to-source covariance matrix; and (3) the computation of source-to-target weights via Kriging, and the subsequent computation of predicted attribute values for the target supports. Experiments with real-world datasets (i.e., predicting population densities of watersheds from population densities of counties in the Eastern Time Zone and in the continental United States) showed that the parallel algorithm drastically reduced the computing time to a practical length that is feasible for actual spatial analysis applications, and achieved fairly high speed-ups and efficiencies. Experiments also showed the algorithm scaled reasonably well as the number of processors increased and as the problem size increased.

It is hard to believe that 10 years have passed since we wrote our guest editorial for IJGIS (Sui and Goodchild 2001 Sui, D.Z. and Goodchild, M.F. 2001. Are GIS becoming new media?. International Journal of Geographical Information Science, 15(5): 387–390. [Taylor & Francis Online], [Web of Science ®] , [Google Scholar]). Using the nascent evidence that emerged in the late 1990s, we speculated back in 2001 that geographic information systems (GIS) were rapidly becoming part of the mass media. On the basis of the proposition of GIS as media, we were able to link GIScience with theories in media studies such as Marshall McLuhan's law of the media, which considers modern media as modifiable perceptive extensions of human thought (Sui and Goodchild 2003 Sui, D.Z. and Goodchild, M.F. 2003. A tetradic analysis of GIS and society using McLuhan's law of media. Canadian Geographers, 47(1): 5–17. [Crossref], [Web of Science ®] , [Google Scholar]). Remarkable conceptual and technological advances in GIS have been made during the past 10 years. The goal of this review is to provide an update on the ‘GIS as media’ argument we made 10 years ago and to discuss the new challenges for GIScience posed by the growing convergence of GIS and social media.

Contemporary GIS can handle static spatial data for querying and visual representation, but the temporal dimension remains a challenge. This paper addresses the need for a dynamic GIS capable of managing complex data types. The design relies on a representation of the theoretical spatiotemporal primitive known as the ‘geo-atom’. This paper proposes a novel and implemented data structure called the space time point (STP) built on this theory. With the STP representation, spatiotemporal data queries can be posed to return useful results about dynamic geographic phenomena and their interaction. Two key challenges addressed in this research are (1) data structures to represent hybrid (object and field) spatiotemporal phenomena and (2) the design of a dynamic GIS interface. These challenges are addressed by the implementation of the system, referred to as ‘Extended Dynamic GIS (EDGIS)’, that uses the proposed STPs. The EDGIS system is described from theory to its implementation in Java™ and a series of application examples are described followed by performance metrics. The paper concludes with a discussion of areas for further research such as integration of the system with geo-sensor networks, hazards, transportation, and location-based services (LBS).

The investigation of spatial patterns implied in categorical spatial data, such as land use and land cover (LULC) classes and socio-economic statistics data, is involved in many aspects of geographical information science, such as spatial uncertainty modeling and spatial data mining. The discrete nature of categorical fields limits the application of traditional analytical methods, such as kriging-type algorithms, widely used in Gaussian random fields. This paper presents a new probabilistic method for modeling the posterior probabilities of class occurrence at any location in space given known class labels at data locations within a neighborhood around that prediction location. In the proposed method, the conditional or posterior (multi-point) probabilities are approximated by weighted combinations of pre-posterior (two-point) transition probabilities (rather than indicator covariances or vari-ograms) while accounting for spatial interdependencies that most of current approaches often ignore. Using sequential indicator simulation based on the properties of a truncated multi-variate Gaussian field as reference, the advantages and disadvantages of this new proposed approach are analyzed and highlighted.

An abundance of geospatial information is flourishing on the Internet but mining and disseminating these data is a daunting task. With anything published on the web available to the public it has become a grand repository of volunteered geographic information (VGI). Internet users often provide location information for videos, pictures, travel destinations, or other events. All of these data can be gathered by a web crawling geospatial agent that later performs geospatial data mining. The discovered geoinformation can be stored, analyzed, queried, and visualized as the agent creates a data repository of what it discovered. This paper presents the design and prototypical implementation of the GEDMWA (Geospatial Exploratory Data Mining Web Agent). It reads webpage data and follows links to acquire knowledge in order to add value to geoinformation usable in a GIS. The agent creates a database of webpage text, mines it for location information, and then converts it to proper geospatial data format. The data is quickly visualized and analyzed after GEDMWA converts it into proper GIS and virtual globe formats. This provides diverse user communities a tool that utilizes a variety of distributed sources to discover additional knowledge about their fields of interest.

Digital gazetteers provide information on named features, linking the feature's name with its location and its type. They have been growing in importance recently as the basis of a range of services, including way‐finding, georeferencing, and intelligence. This introduction to the following collection of five research papers expands on contemporary applications of digital gazetteers, explores the issues associated with each of the three types of information, and defines three broad areas of research: the components of gazetteers; the process by which places are named and evolve; and the issues of interoperability between digital gazetteers. Each area is represented by at least one paper in the collection. Digital gazetteers increasingly form the interface between the informal discourse of humans and the formal world of geographic information science.

Geospatial data modelling is dominated by the distinction between continuous‐field and discrete‐object conceptualizations. However, the boundary between them is not always clear, and the field view is more fundamental in some respects than the object view. By viewing a set of objects as an object field and unifying it with conventional field models, a new concept, the General Field (G‐Field) model, is proposed. In this paper, the properties of G‐Field models, including domain, range, and categorization, are discussed. As a summary, a descriptive framework for G‐Field models is proposed. Then, some common geospatial operations in geographic information systems are reconsidered from the G‐Field perspective. The geospatial operations are classified into order‐increasing operations and non‐order‐increasing operations, depending on changes induced in the G‐Field's order. Generally, the order can be viewed as an indicator of the level of information extraction of geospatial data. It is thus possible to integrate the concept of order with a geo‐workflow management system to support geographic semantics.

Census population data are associated with several analytical and cartographic problems. Regression models using remote‐sensing covariates have been examined to estimate urban population density, but the performance may not be satisfactory. This paper describes a kriging‐based areal interpolation method, namely area‐to‐point residual kriging, which can be used to disaggregate the residuals remaining from regression. Compared with conventional cokriging, the area‐to‐point residual kriging is much simpler in that only a semivariogram model for the point residuals is required, as opposed to a set of auto‐ and cross‐semivariogram models involving the dependent variable and all the covariates. In addition, area‐to‐point residual kriging explicitly accounts for any scale differences between source data and target values. The method is illustrated by disaggregating population from census units to the land‐use zones within them. Comparative results for regression with and without area‐to‐point residual kriging show that area‐to‐point residual kriging can substantially improve interpolation accuracy.

Geographic representation has become more complex through time as researchers have added new concepts, leading to apparently endless proliferation and creating a need for simplification. We show that many of these concepts can be derived from a single foundation that we term the atomic form of geographic information. The familiar concepts of continuous fields and discrete objects can be derived under suitable rules applied to the properties and values of the atomic form. Fields and objects are further integrated through the concept of phase space, and in the form of field objects. A second atomic concept is introduced, termed the geo‐dipole, and shown to provide a foundation for object fields, metamaps, and the association classes of object‐oriented data modelling. Geographic dynamics are synthesized in a three‐dimensional space defined by static or dynamic object shape, the possibility of movement, and the possibility of dynamic internal structure. The atomic form also provides a tentative argument that discrete objects and continuous fields are the only possible bases for geographic representation.

Three forms of linear interpolation are routinely implemented in geographical information science, by interpolating between measurements made at the endpoints of a line, the vertices of a triangle, and the vertices of a rectangle (bilinear interpolation). Assuming the linear form of interpolation to be correct, we study the propagation of error when measurement error variances and covariances are known for the samples at the vertices of these geometric objects. We derive prediction error variances associated with interpolated values at generic points in the above objects, as well as expected (average) prediction error variances over random locations in these objects. We also place all the three variants of linear interpolation mentioned above within a geostatistical framework, and illustrate that they can be seen as particular cases of Universal Kriging (UK). We demonstrate that different definitions of measurement error in UK lead to different UK variants that, for particular expected profiles or surfaces (drift models), yield weights and predictions identical with the interpolation methods considered above, but produce fundamentally different (yet equally plausible from a pure data standpoint) prediction error variances.

Improved techniques are needed to characterize complex fluvial systems and monitor ecologically important, yet highly vulnerable riverine environments. This paper explores potential alternatives to traditional mapping of in‐stream habitat and presents fuzzy set theory as a means of departing from the rigid, Boolean, object‐based framework. We utilize hydrodynamic modeling, remotely sensed data, and fuzzy clustering to obtain classifications that allow for continuous partial membership and gradual transitions among habitat types. Methods of assessing cluster validity are available, but data quality is a crucial consideration. Crisp, vector‐based representations can be derived from raster fuzzy classifications by applying a threshold to maximum membership values. This process results in conditional objects separated by ambiguous transition zones, and a compromise must be reached between the proportion of the channel assigned to polygons and the certainty with which this assignment can be made. Spatial patterns of classification uncertainty can also be used to identify areas of confusion, infer boundaries of variable width, and highlight areas of increased habitat diversity. Hydraulic modeling and remote sensing complement one another and, together with field work, could provide a more realistic representation of the fluvial environment.

Geographic information systems are designed to solve problems of practical importance in the real world, but do so based almost entirely on the contents of their databases, rather than the real wor...

This paper describes a means for linking field and object representations of geographical space. The approach is based on a series of mappings, where locations in a continuous field are mapped to discrete objects. An object in this context is a modeler's conceptualization, as in a viewshed, highway corridor or biological reserve. An object can be represented as a point, line, polygon, network, or other complex spatial type. The relationship between locations in a field and spatial objects may take the form of one-to-one, one-to-many, many-to-one, or many-to-many. We present a typology of object fields and discuss issues in their construction, storage, and analysis. Example applications are presented and directions for further research are offered.

This paper identifies analytical and empirical methods for determining the probability that lines and areas intersect tiles in a regular tessellation. Such intersections are common in geographical information systems (GIS) applications. Knowledge of intersection probabilities is valuable in many instances, including estimating complexity and time required to process a distance or viewshed operation, developing optimal tiling schemes for national georeferencing systems, precalculating the number of map sheets a spatial feature may occupy, and identifying appropriate cell resolutions for vector-to-raster conversions. Buffon's Needle-type solutions from the field of geometric probability provide the framework for deriving probabilities for lines. Probabilities for simple areas like rectangles and circles are derived using geometric techniques and illustrated using hypothetical examples. Employing such probabilities in spatial analysis may yield more rigorous and theoretically informed results from GIS analysis, leading to better decisions and greater insight into spatial phenomena.

This paper presents a coupling strategy based on Component Object Model (COM) technology, for performing spatial analysis within a GIS. The strategy involves using a module which simultaneously manipulates software components from the GIS application and the data analysis application. We illustrate the strategy using an extension, written for the proprietary GIS ArcInfo, which performs areal interpolation, a statistical method of basis change commonly required by users of socioeconomic data. The extension creates an instance of a statistics package and uses it to process GIS data stored in ArcInfo, and then passes the resulting information back to ArcInfo where it is stored in a standard attribute table. This coupling strategy can, of course, be used with other COM-compliant GIS and data analysis software. COM-compliant software allows GIS analysts and researchers to create custom-tailored applications using components from many different sources. Because the GIS does not rely on a proprietary macro language for customization there is a potential increase in access to spatial analysis tools which were previously difficult to link with a GIS, and we explore and evaluate the potential of the coupling strategy presented here for the GIS and spatial analysis research community.

The dazzling development of GIS technology in recent years has rendered each of the traditional , mostly instrumental, views of GIS—as spatial database, mapping tool, and spatial analytical tool—inadequate to capture the fundamental essence of this technology and its social implications. Each year brings new software packages from innovative developers that are easier to use, more powerful, and more easily adopted by users with minimal training. GIS and mapping tools are increasingly available on the World Wide Web (WWW), and an increasing number of sites oŒer advanced GIS services such as route Ž nding and geocoding. In-vehicle navigation systems using GIS technology are becoming part of our daily lives (Cowen 1994). In the next two years cellphones in the US will be required to be geographically enabled—to be able to report their current location to an accuracy of 100m—in the interests of accurate response to emergency calls. New imagery is becoming available from commercial sources with spatial resolutions as Ž ne as 1m, and is being distributed through new mechanisms such as distributed geolibraries and spatial data clearinghouses (NRC 1993, 1999). New methods of data documentation are being used to support widespread sharing of spatial data via the Internet. These new trends contrast sharply with the earlier view of GIS that prevailed into the early 1990s, as tools contained within a standalone computing system, serving the needs of their professional users by performing various forms of analysis that were too tedious, time-consuming, or expensive to perform by hand, on data collected and assembled for the purpose. These latest new developments in GIS have convinced us of the need for new conceptualizations (or metaphors, for lack of a better term) for what GIS actually is and will become in the near future. We believe that the complex relationship between GIS and society can be better understood if one conceives of GIS as new media. Media are generally understood as means of sending messages or communicating information to the general public, and mass media are the instruments by which mass communication takes place in modern societies. Mass media are also the most eŒective means of broadcasting information to large numbers of people in a short period of time. In a very general sense, GIS can be understood as a new technological

This paper introduces a special issue of the journal on the subject of Project Varenius, a three-year effort funded by the US National Science Foundation to advance geographical information science. Geographical information is first defined as an abstraction of primitive tuples linking geographical locations to general descriptors. Geographical concepts originate in the human mind, and are instantiated in geographical information. Geographical information technologies apply digital methods to geographical information. Finally, geographical information science is defined as the set of basic research issues arising from these technologies. Three motivations are presented for research in this area: scientific, technological, and societal. Within the project, geographical information science is structured by a three-part framework that includes cognitive, computational, and societal issues. The paper ends with an introduction to these three parts, which define the infrastructure of the project and are discussed at length by the subsequent three papers.

A geostatistical methodology is proposed for integrating elevation estimates derived from digital elevation models (DEMs) and elevation measurements of higher accuracy, e.g., elevation spot heights. The sparse elevation measurements (hard data) and the abundant DEM-reported elevations (soft data) are employed for modeling the unknown higher accuracy (reference) elevation surface in a way that properly reflects the relative reliability of the two sources of information. Stochastic conditional simulation is performed for generating alternative, equiprobable images (numerical models) of the unknown reference elevation surface using both hard and soft data. These numerical models reproduce the hard elevation data at their measurement locations, and a set of auto and crosscovariance models quantifying spatial correlation between data of the two sources of information at various spatial scales. From this set of alternative representations of the reference elevation, the probability that the unknown reference value is greater than that reported at each node in the DEM is determined. Joint uncertainty associated with spatial features observed in the DEM, e.g. the probability for an entire ridge existing, is also modeled from this set of alternative images. A case study illustrating the proposed conflation procedure is presented for a portion of a USGS one-degree DEM. It is suggested that maps of local probabilities for over or underestimation of the unknown reference elevation values from those reported in the DEM, and joint probability values attached to different spatial features, be provided to DEM users in addition to traditionally reported summary statistics used to quantify DEM accuracy. Such a metadata element would be a valuable tool for subsequent decision-making processes that are based on the DEM elevation surface, or for targeting areas where more accurate elevation measurements are required.

In this paper we propose a simple technique for assessing the positional accuracy of digitized linear features. The approach relies on a comparison with a representation of higher accuracy, and estimates the percentage of the total length of the low accuracy representation that is within a specified distance of the high accuracy representation. The approach deals successfully with three deficiencies of other methods: it is statistically based; is relatively insensitive to extreme outliers; and does not require matching of points between representations. It can be implemented using standard functions and a standard scripting language in any raster or vector GIS. We present the results of a test using data from the Digital Chart of the World.

The traditional home of GIS in terms of managing,mapping, modelling and making decisions based on spatial data has been in the land-based sciences and professions. This has resulted in a concentration of GIS on land-based 'application domains' (sets of GIS applications with common properties and data formats), with a relatively homogeneous group of users and applications addressing the solutions of largely related problems. This atmosphere has tended to obscure the essential nature of GIS as an ubiquitous, heterogeneous tool, having utility far beyond land-based problems. We must consider remedying this if we expect GIS to play an increasingly important role in Earth system science or global change research. We therefore propose the expansion of a largely landbased GIS research agenda to the development of systems focusing more on the marine environment, for there are many ways that GIS may be improved by tackling the problems associated with oceanographic data. The discussion is confined largely to deep ocean science which rarely appears in the GIS or geographical literature (as opposed to coastal zone studies). We identify research issues endemic to oceanographic applications of GIS that will advance the body of knowledge in GIS design and architecture, as well as the body of knowledge in the broader field of geographical information science. They include the development of spatial data structures with the ability to vary their relative positions and values over time, geostatistical interpolation of data that are sparse in one dimension but abundant in the others, and new data models that make the feature-based query, search and retrieval of objects and continuous fields in very large spatial databases more efficient.

. Research papers at conferences such as EGIS and the International Symposia on Spatial Data Handling address a set of intellectual and scientific questions which go well beyond the limited technical capabilities of current technology in geographical information systems. This paper reviews the topics which might be included in a science of geographical information. Research on these fundamental issues is a better prospect for long-term survival and acceptance in the academy than the development of technical capabilities. This paper reviews the current state of research in a series of key areas and speculates on why progress has been so uneven. The final section of the paper looks to the future and to new areas of significant potential in this area of research.

This article is an agreed summary of a workshop held in Sheffield between 18-20 March 1991. The focus here is on three of the themes of the workshop: the mutual benefits of closer links between geographical information systems (GIS) and the methods of spatial data analysis (SDA); the specific areas of SDA that should be linked with GIS; how the linkage should be made in practice. Directions for future research are also reviewed. The emphasis throughout is on statistical SDA and principally from the perspective of human rather than physical geography.

Education has been part of the NCGIA's mission from the earliest discussions of the concept of the Center at the National Science Foundation. To respond to the need for short-term solutions to the shortage of adequately trained personnel in GIS, the Center developed a set of teaching materials or core curriculum. The steps in its development are described and an analysis of initial distribution statistics is presented. Current efforts to develop a framework for laboratory materials are outlined. The paper ends with an assessment of the project and comparison with other disciplines.

An error model for spatial databases is defined here as a stochastic process capable of generating a population of distorted versions of the same pattern of geographical variation. The differences between members of the population represent the uncertainties present in raw or interpreted data, or introduced during processing. Defined in this way, an error model can provide estimates of the uncertainty associated with the products of processing in geographical information systems. A new error model is defined in this paper for categorical data. Its application to soil and land cover maps is discussed in two examples: the measurement of area and the measurement of overlay. Specific details of implementation and use are reviewed. The model provides a powerful basis for visualizing error in area class maps, and for measuring the effects of its propagation through processes of geographical information systems.

The field of geographical information systems (GIS) is reviewed from the viewpoint of spatial analysis which is the key component of the familiar four-part model of input, storage, analysis and output Input is constrained by the limits of manual methods and problems of ambiguity in scanning. The potential for developments in output is seen to be limited to the query mode of GIS operation, and to depend on abandoning the cartographic model. Discussion of storage methods is organized around the raster versus vector debate and the need to represent two spatial dimensions in one. A taxonomy of GIS spatial analysis operations is presented together with a generic data model. Prospects for implementation are discussed and seen to depend on appropriate scales of organization in national and international academic research.

Agencies acquiring GIS hardware and software are faced with uncertainty at two levels: over the degree to which the proposed system will perform the functions required, and over the degree to which it is capable of doing so within proposed production schedules. As the field matures the second concern is becoming more significant. A formal model of the process of acquiring a GIS is presented, based on the conceptual level of defining GIS sub-tasks. The appropriateness of the approach is illustrated using performance data from the Canada Land Data System. It is possible to construct reasonably accurate models of system resource utilization using simple predictors and least squares techniques, and a combination of inductive and deductive reasoning. The model has been implemented in an interactive package for MS-DOS systems.