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Space and time frame all aspects of the discipline of geography. Integration is normally interpreted by geographers as a straddling of the environmental–social divide, but a more profound interpretation stresses the issues involved in coupling environmental and social processes: a science of integration rather than an integration of sciences. Seven examples of distinct data types and scientific questions are examined, leading to the conclusion that a space–time geographic information system is unlikely to emerge in the near future. Instead, attention should focus on the systematic study of the issues involved in integration through the formal environment of a computational system. [ABSTRACT FROM PUBLISHER]

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. [ABSTRACT FROM AUTHOR]

Li and Zhang (2012b, 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. 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, Markov chain random fields for estimation of categorical variables. Mathematical Geology, 39 (3), 321–335), and followed by Allard et al. (2011, 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. 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. An efficient maximum entropy approach for categorical variable prediction. European Journal of Soil Science, 62, 381–393). Li and Zhang (2012b, 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, 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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM PUBLISHER]

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. [ABSTRACT FROM AUTHOR]

The concept of Digital Earth as a digital replica of the planet has its roots in science fiction, in the writings of environmental visionaries, and in a speech prepared for delivery by Vice President Gore in 1998. Five use cases are identified that together comprise the vision of Digital Earth: a geoportal, a visualization service, a platform for simulation and prediction, a source of unprecedented spatial and temporal resolution, and a technology fully integrated into human activities. Progress to date is reviewed for each use case, along with the research that would be needed to achieve each aspect of the overall vision. [ABSTRACT FROM PUBLISHER]

The convergence of newly interactive Web-based technologies with growing practices of user-generated content disseminated on the Internet is generating a remarkable new form of geographic information. Citizens are using handheld devices to collect geographic information and contribute it to crowd-sourced data sets, using Web-based mapping interfaces to mark and annotate geographic features, or adding geographic location to photographs, text, and other media shared online. These phenomena, which generate what we refer to collectively as volunteered geographic information (VGI), represent a paradigmatic shift in how geographic information is created and shared and by whom, as well as its content and characteristics. This article, which draws on our recently completed inventory of VGI initiatives, is intended to frame the crucial dimensions of VGI for geography and geographers, with an eye toward identifying its potential in our field, as well as the most pressing research needed to realize this potential. Drawing on our ongoing research, we examine the content and characteristics of VGI, the technical and social processes through which it is produced, appropriate methods for synthesizing and using these data in research, and emerging social and political concerns related to this new form of information. [ABSTRACT FROM AUTHOR]

Issues of heterogeneity and incompatibility in geospatial data become increasingly important as data sources become more abundant. Scientific research and decision-making usually require geospatial data from a variety of sources, since it is not realistic to collect all data directly; therefore, it is important to effectively utilise data created by various agencies using different methodologies under different circumstances. The term conflation here refers to the problem of combining incompatible geospatial data. One crucial component in conflation is feature matching, which is a prerequisite for the subsequent steps such as feature transformation. Although previous research has provided different methods of feature matching for specific applications, most of them have relied on a greedy strategy to execute the matching process. This article develops a new optimisation model to improve linear feature matching in situations with one-to-one, one-to-many and one-to-none correspondences by extending the optimised feature matching method proposed by Li and Goodchild (Automatically and accurately matching objects in geospatial datasets. In: Proceedings of theory, data handling and modelling in geospatial information science. Hong Kong, 26–28 May, 2010). Considering all possible matched pairs simultaneously, this new model achieves a high percentage of correctly matched features by maximising the total similarity between all matched pairs. When autocorrelated distortions exist in the datasets, an affine transformation can be integrated into the feature matching to improve the matching results. In addition, this study takes advantage of the asymmetry of a dissimilarity metric – directed Hausdorff distance – to address one-to-many correspondences. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. 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. 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. [ABSTRACT FROM PUBLISHER]

Geographic data and tools are essential in all aspects of emergency management: preparedness, response, recovery, and mitigation. Geographic information created by amateur citizens, often known as volunteered geographic information, has recently provided an interesting alternative to traditional authoritative information from mapping agencies and corporations, and several recent papers have provided the beginnings of a literature on the more fundamental issues raised by this new source. Data quality is a major concern, since volunteered information is asserted and carries none of the assurances that lead to trust in officially created data. During emergencies time is the essence, and the risks associated with volunteered information are often outweighed by the benefits of its use. An example is discussed using the four wildfires that impacted the Santa Barbara area in 2007-2009, and lessons are drawn. [ABSTRACT FROM AUTHOR]

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). [ABSTRACT FROM AUTHOR]

Great strides have been made in geographic information systems and science over the past 14 years: through the development of spatial data infrastructures and the infrastructure of data sharing; through advances in the technologies of positioning, data acquisition, data dissemination, and data analysis; and through advances in the science that lies behind the technology. Five future scenarios are examined: a world in which it is possible to know where everything is at all times; a world of positioning, representation, and wayfinding that is fully three dimensional; a world that involves the citizen as both consumer and producer of geographic information; a world of ready access to predictions of future landscapes; and a world of real-time dynamic information. [ABSTRACT FROM AUTHOR]

Humans have always exchanged geographic information, but the practice has grown exponentially in recent years with the popularization of the Internet and the Web, and with the growth of geographic information technologies. The arguments for sharing include scale economies in production and the desire to avoid duplication. The history of sharing can be viewed in a three-phase conceptual framework, from an early disorganized phase, through one centered on national governments as the primary suppliers of geographic information, to the contemporary somewhat chaotic network of producers and consumers. Recently geolibraries and geoportals have emerged as mechanisms to support searches for geographic information relevant to specific needs. We review the design of the Geospatial One-Stop (GOS), a project sponsored by the U.S. Federal Government to provide a single portal to geographic information, and reflecting the current state of the art. Its design includes a portal to distributed assets, accessible through a simple Web browser, a catalog based on the widely used Federal Geographic Data Committee metadata standard, services to assess and validate potential accessions, directories to available geographic information services, and automated metadata harvesting from registered sites. GOS represents a significant technological advance, however, its potential to provide a general marketplace for geographic information beyond government data has not been realized. Its future will be driven in part by technological advances in areas such as searching and automated metadata harvesting, as well as by clearer definition of its domain, either as the geoportal for U.S. data or as a broader geoportal with appropriate international or private partners. Incorporation of informal and heuristic search methods used by humans appears to offer the best direction for improvement in search technologies. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Reports on the function of maps as humanities primary method for storing and communicating knowledge of the earth's surface. Different kinds of maps; Several functions of maps; Discussions on Geographic Information Science.

Discusses the validity and usefulness of laws in geographic information science (GIScience) and geography. Value of Tobler's First Law in GIScience; Prospects of additional laws regarding geography.