Handbook of Research on Geoinformatics - Hassan A. Karimi Part 8 potx

328 Virtual Environments for Geospatial Applications Critical Issues in the Design and Implementation of Geospatial Virtual environments This section concisely discusses some limitations and constraints typically experienced in several virtual world generations as well. One noteworthy issue is that in visualizing real-world scenarios, there is an inevitable trade-off amid performance and resolution. Exploiting the complete capabili- ties of virtual environments over the Web contin- ues to pose problems. As the number of objects in a virtual environment increases, online hosting becomes an issue as spontaneous rendering of numerous objects is no easy task. Scenes with a greater number of polygons decelerate the system and make the interactivity poor. Several factors need to be considered during visualization such as the type and volume of data to be visualized, memory constraints, and system performance. Table 2 presents a summary of the signicant issues concerning geo-virtual environments. In their work on information visualization, Robert- son et al. (1993) have presented a terse compilation of the important issues. In his work on dynamic and interactive web- based visualizations, Huang and Lin (1999, 2001, and 2002) discuss in detail some of these concerns and also address some critical issues concerning online hosting of interactive visualizations. The Java-3D based hybrid method that Huang and Lin (1999, 2001) propose offers a standard framework Figure 8. a) 3D virtual environment depicting geospatial processes (1 picture of a series) such as land- scape change over time etc.; b) 3D virtual environment depicting water ow in a reservoir Table 2. A summary of critical issues in designing and implementing 3D virtual worlds Photo-realistic scene generations Generating 3D virtual e nvironments with adequate photo-realism Bandwidth Limitations 3D Scenes with n umerous objects, rendering difficulty, and transmission speed Browser and Plug-in Compatibility Compatibility among various browsers as well as plug-ins User Navigation capabilities Users need skills to navigate and situate themselves within immersive virtual worlds. Lag in real-time interaction Complex Scenes n ot o nly take time to render, but also cause delays/lags during navigation/interaction Data Integrity and online security issues Sensitive data must be p rotected, and the data represented by such 3D worlds should be up-to-date Online hosting Server load must be balanced to handle multiple simultaneous requests Spatio-temporal representations The issue of representing spatio-temporal aspects as dimensions within virtual worlds must be resolved 329 Virtual Environments for Geospatial Applications for visualizing dynamic environmental processes. Figure 9 illustrates a 3-tier conguration that Huang and Lin (1999) proposed in GeoVR. The visualization server that is interlinked to the spatial database accesses the geospatial information from the data repository and the web server accesses the visualization server for 3D information. This framework efciently handles requests for visual- izing dynamic processes and based on the client requests, the web server provides the appropriate information in the conventional HTML or 3D VRML format. dIscuss Ion And conc Lus Ion Over the past several decades, information presen- tation has inspired the development of several new tools and techniques. The information revolution has resulted in vast amounts of data that are far too complex, both in quality and quantity, to be handled by conventional tools and techniques. Recent technological advances in the realm of remote sensing have dramatically increased the amount of geospatial data available. Virtual en- vironments are an efcient means of visualizing voluminous geospatial data and are efcient in elucidating the intricate patterns as well as hidden and associated information. Such virtual environ- ments facilitate understanding of the complex relationships among the various components of a multi-level scenario. This paper discussed the design and imple- mentation of virtual worlds that can be used to generate both static representations depicting real-world settings and dynamic representations that can simulate geospatial processes and en- vironmental phenomena. The paper discussed the generation of such geo-virtual environments with examples and provided explanations as to how such geo-visualization applications facilitate understanding of various geospatial phenomena and environmental processes. The fundamental principles underlying the generation of virtual worlds, both static and dynamic, were elaborated and the common issues involved in the generation of such 3D virtual worlds were discussed. Further- more, the issues related to the online hosting of such virtual environments were tersely delineated and possible solutions to frequently encountered problems were provided. Figure 9. Online hosting of interactive visualization (From Huang et al.,1999) 330 Virtual Environments for Geospatial Applications r eferences Ames, L. A., Nadeau, D. R., & Moreland, J. L. (1996). VRML 2.0 Sourcebook. Bonham-Carter, G. F. (1994). Geographic Infor- mation Systems for Geoscientists: Modeling with GIS. Pergemon: Oxford (p. 398). Boyd, D. S., Lansdown, J., & Huxor, A. (1996). The Design of Virtual Environments. SIMA. Chandramouli, M., Lan-Kun, C., Tien-Yin, C., &vChing-Yi, K. (2004). Design and Implementa- tion of Virtual Environments for Visualizing 3D Geospatial Data. TGIS Conference, Oct 28-29 2004. Chandramouli, M., Huang, B., Yin Chou, T., K un C hung, L., & Wu, Q. (2006). Design and Implementation of Virtual Environments for plan- ning and Building Sustainable Railway Transit Systems, COMPRAIL July 2006, Prague. Colin, W. (2000). Information Visualization: Perception for Design. Morgan Kaufmann Series in Interactive Technologies GeoVRML, (www. geovrml.org) Huang, B., & Lin, H. (1999). GeoVR: A Web- based tool for virtual reality presentation from 2D GIS data. Computers & Geosciences, 25(10), 1167-1175. Huang, B., Jiang, B., & Lin, H. (2001). An inte- gration of GIS, virtual reality and the Internet for spatial data exploration. International Journal of GIS, 15(5): 439-456. Huang, B., & Lin, H. (2002). A Java/CGI approach to developing a geographic virtual reality toolkit on the Internet. Computers & Geosciences, 28(1), 13-19. Karel, C., & Jiri, Z. (n/d). Using VRML for creat- ing interactive demonstrations of physical models. Department of computer science and Engineering. Czech Technical University. Robertson, G., Card, S., & Mackinlay, J. D. (1993). Information Visualization Using 3D Interactive Animation. Communications of the ACM, 36, 57-71. Shiode, N. (2001) 3D urban models: recent developments in the digital modeling of urban environments in three-dimensions. GeoJournal, 52(3), 263-269. SGILICGF, UW Sea Grant Institute and Land Information and Computer Graphics Facility http://coastal.lic.wisc.edu/visualization/Visual- ization.htm Sutherland, I. E. (1965). The ultimate display. In the proceedings of the IFIPS Congress, 2, 506- 508. New York City, NY. key t er Ms Immersion: A Sense of being present within the virtual world and a ‘sense’ being able to visu- alize objects by being amidst their surroundings and navigating through the world. Node: An entity within the hierarchical scene structure that represents a group of objects. OpenSource: Source code or computer soft- ware that is freely offered and is available to the public for building software applications. Scene-Hierarchy: The organization of the elements of a 3D virtual scene into successive levels, in such a way that the object under which other objects are grouped is called the parent and the grouped objects are called its children. When a parent object is transformed, the children are also transformed. SCRIPT: Program scripts that are used to perform calculations and return values to the calling programs. Transformation: Operations such as transla- tion, rotation, or scaling involving objects in a virtual environment. 331 Virtual Environments for Geospatial Applications Virtual Reality: A three-dimensional visual immersive setting that facilitates user to navigate within the scene and perform operations in real time. 332 Chapter XLI Managing Uncertainty in Geospatial Predictive Models Iftikhar U. Sikder Cleveland State University, USA Copyright © 2009, IGI Global, distributing in print or electronic forms without written permission of IGI Global is prohibited. Abstr Act Geospatial predictive models often require mapping of predened concepts or categories with various conditioning factors in a given space. This chapter discusses various aspects of uncertainty in predictive modeling by characterizing different typologies of classication uncertainty. It argues that understanding uncertainty semantics is a perquisite for efcient handling and management of predictive models. 1. spAt IAL pred Ict Ion And cLAss If IcAt Ion Geospatial predictive models entail an array of analytical techniques of data mining, classical statistical and geostatistical models that attempt to predict spatial states and behavior of objects from a ne set of observations. The process of pre- diction presupposes a set of spatial concepts and categories to which objects are to be mapped. For example, spatial processes, such as classication of land cover from satellite image, modeling for- est re, propagation of epidemics, and prediction of urban sprawl require a unifying and common reference of “space” or location where the multiple features of spatial attributes are to be mapped to predened class labels. The prediction of spatial features can be conceived as a process of driving classication schemes in relation to certain spa- tial properties such as neighborhood, proximity, dependency, as well as similarity of non-spatial attributes (Han & Kamber, 2006; Shekhar & Chawla, 2003). In data mining, a classication function is often dened as a mapping function: 333 Managing Uncertainty in Geospatial Predictive Models C: →Af , where A is the domain of function, f represents attribute space and C is the set of class categories. 2. uncert AInty In spAt IAL cLAss If IcAt Ion Uncertainty may emerge from ontological con- straints in classication i.e., from the lack of specication of what kind of spatial objects ex- ist, as well as from epistemic limitations which concern whether such objects are knowable to subjective schemes, and if so, to what extent they can be represented in the subjective frame- work, given the limited empirical evidences. Epistemic uncertainty in spatial classication emerges due to inadequate representation of spatial knowledge which is often incomplete, imprecise, fragmentary, and ambiguous. The at- tributes of spatial objects or evidences suggesting various conceptual or thematic classes may often suggest conicting categories. Moreover, clas- sication labels are dependent on the resolution of observation and the extent of granularity. For example, the observation of coarser granularity offers less detail while the clumping of informa- tion into pixels in remotely sensed images may prevent sub-pixel entities being distinguished (Fisher, 1997). The classication of land cover from satellite image depends not only on a specic spatial resolution, radiometric resolution and the corresponding spectral signatures limit predictive accuracy. Therefore, spatial characteristics of a given observation are indiscernible with respect to attributes associated with it. For example, the number of vegetation types that can be identied from an NDVI (Normalized Difference Vegetation Index) image signicantly increases when a very high radiometric resolution is used. Moreover, in a specic case, a multispectral image may provide more accuracy than a hyperspectral image, but such accuracy is of little value if it is achieved at the cost of less specicity or higher imprecision. 3. t ypo Log Ies of cLAss If IcAt Ion uncert AInty While there is increasing awareness of un- certainty, and its aspects and dimensions in predictive as well as classicatory schemes, little agreement exists among experts on how to characterize them. Many typologies of uncer- tainty have been suggested from risk analysis perspective, which often overlaps and builds on each other (Ferson & R. Ginzburg, 1996; Linkov & Burmistrov 2003; Regan et al., 2002). These typologies make distinctions between variability and lack of knowledge at the parameter and model level. However, from the geographic information perspective, the ontological specication of im- perfection of geographic data provides some key vocabularies and taxonomies to deal with spatial uncertainties (Duckham et al., 2001; Worboys & Clementini, 2001). Such ontology distinguishes between inaccuracy (i.e., errors or commission or omission) and imprecision, which arises from limitations on the granularity of the schema or levels of detail obtainable for an observation under which the observation is made (Worboys, 1998). The concept “vagueness” refers to indeterminate boundary-line cases or “inexact concepts”. Classication of geographic objects with in- determinate boundaries offers many challenges (Burrough & Frank, 1996) which emerge from the boundary of many real entities representing natural, social, or cultural phenomena (for exam- ple, forests, mountains, areas ethnic distribution etc.). Since many common geographical concepts are vague (Fisher, 2000), the explicit specica- tion of vagueness is essential to characterize the classication performance. As a special type of vagueness, nonspecity originates due to our inability to discern the true alternatives among several alternatives in a given context. It implies 334 Managing Uncertainty in Geospatial Predictive Models cardinality of undiscerned alternatives (Klir & Yuan, 1995). The larger the set of alternatives, the higher is the nonspecity. For example, in a remotely sensed image, a pixel with class type “forest” and the mean annual temperature > 30 C has less nonspecity than the pixel labeled only with “forest” type. This is because in the latter case a pixel can have a large number of possible variations of “forest” type. Broadly, three major categories of uncertainty can be identied in dealing with predictive and classicatory problems: ontological uncertainty, epistemological uncertainty, and deontological or normative uncertainty. The typology illustrated in Figure 1 is relevant to mainly geospatial data and includes many important components and concept provided in Morgan & Henrion (1998), Finkel (1990), and Cullen & Frey (1999) and Haimes (2004). The types presented here is in by no means mutually exclusive, i.e., some concepts may subtly overlap each other in a specic context. Ontologically, variability, also known as aleatory or objective uncertainty, occurs when the object that needs to be classied actually exhibits multi- plicity across space, time and scale. An empirical quantity measured in a single point may objec- tively manifest multiple aspects in a collective process. For example, land cover classes are not only inuenced by seasonal and spatial extent, but also the topographic formation due to self-similar features of geological objects requires specicity of fractal dimension of a classication scheme. The spurious correlation representing the so called of ecological fallacy resulting from modiable areal unit problem (MAUP) (Openshaw, 1984) indicates the requirement of adequate disaggregation in spatial data to be analyzed. In image processing, uncertainty often arises due to the assignment of more than one class to a pixel. This specialized type of pixel, often known as mixel, indicates uncertainty resulting from variability. Similarly, variability or the degree of spatial heterogeneity Types of Uncertainty Ontological Epistemological Decision / Value Variability Parameter Model • Metric error • Sampling error • Commission/omission error • • Monte Carlo • heterogeneity • Process uncertainty • Self-similarit y • • Choice of models • • Imprecise Probability- Interval valued probability, general upper & lower probability, belief / plausibility, Rough, Fuzzy, hybrid , Evidence reasoning) • -Spohn calculus and ka pp a-calculus • M • , value measures • Range of risk tolerance • Deontological/ Figure 1. Types of uncertainty in dealing with geospatial predictive and classicatory systems 335 Managing Uncertainty in Geospatial Predictive Models is also reected in the measures of fragmentation of a landscape. The uncertainty stemming from variability can not be handled by a reductionist approach, but needs to be managed by a process of disaggregation of data. Measures often used to manage this kind of uncertainty are: estimating space-time frequency distribution, disaggrega- tion by pixel unmixing or decoupling, estimating entropy as indicator of fragmentation, computing self-similarity and fractal dimension (Kallimanis et al., 2002), and multiscale and multiresolution analysis using wavelet (Kolaczyk et al.,2005; Nychka et al., 2001). While the origin of uncertainty due to vari- ability is objective and ontological in nature, parameter uncertainty and model uncertainty reect the epistemic state or lack of knowledge in a classicatory scheme. Parameter is an empirical quantity that is measurable in principle, and is part of the system components or construct of a denition. Parameter uncertainty is mainly due to the result of measurement error and sampling error. For example, the misclassication rate of land cover classication, measured by the so called error of commission or omission is as good as the choice of sampling scheme, the systematic bias introduced by the selection of space-time boundary conditions, level of precision, and other parameters internal to the system. Moreover, the selection of parameters may depend on the degree of variability. A high degree of spatial heterogeneity requires an intensive sampling scheme across multiple scales. Quantitatively, parameter uncertainty can be modeled by using probability distribution based on statistical vari- ance of observed error e.g., Gaussian distribution can be used to predict the relative abundances of different magnitudes of error or perform Monte Carlo simulation to estimate the effect of error on a digital elevation model (Heuvelink, 1998; Longley et al., 2001). Model uncertainty, or sometimes called in- f orm ative uncertainty (van Asselt, 1999) is due to limitation in the ability to represent or model real-world processes with the given data. Although both parameter uncertainty and model uncertainty represent the epistemic or subjective aspect of the state of our knowledge, the line between these two types of uncertainties can not be sharply divided, because the choices of the model form have impli- cations for a parameter, and the parameter itself can be the output of complex models (Krupnick et al., 2006). Many schemes have been developed to formalize the uncertainty due to limitation of models. The probabilistic intolerance to impreci- sion of classical probability theory has led to many alternative formations of uncertainty models. For example, traditional classication models such as multi-source classication (Lee & Swain, 1987) or the so-called maximum likelihood classica- tion (Tso & Mather, 2001) allows no room for expressing modeler’s ignorance in the model construct. This has led to new model constructs such as, interval representation in Dempster- Shafer’s evidence theory (Shafer, 1976) where the numbers of all possible subsets of the frame of discernment are candidate classes of belief func- tion. The belief is extracted from the sum of the probability of all the attributes that an object has, and the plausibility is the sum of the probabilities of all the attributes that the object does not have. The uncommitted belief is assigned to the frame of discernment, thus allowing representation of modeler’s ignorance. The evidential reasoning ap- proach has been adopted for multi-source remotely sensed images (Lee & Swain, 1987; Srinivasan & Richards, 1990; Wilkinson & Megier, 1990). Rough set theory (Pawlak, 1992), a variant from multivalued logic is recently being used to model vagueness and imprecision by using an upper and a lower approximation. Ahlqvist et al. (2000) used a rough set-based classication and accuracy assessment method for constructing rough con- fusion matrix. In the integration model of rough set theory and evidence theory, the “belief” is extracted from the lower approximation of a set and the “plausibility” from the upper approximation (Skowron & Grzymalla-Busse, 1994). In spatial 336 Managing Uncertainty in Geospatial Predictive Models prediction, this approach was further extended by introducing evidences from spatial neighborhood contexts (Sikder & Gangapadhayay, 2007). Using rough–fuzzy hybridization and cognitive theory of conceptual spaces a parameterized representation of classes are modeled as a collection of rough- fuzzy property where an attribute itself can be treated as a special case of a concept. In spatial classication, the fuzzy approach is mainly used to provide a exible way to represent categorical continua (Foody, 1995). In this approach instead of explicitly dening concept hierarchies, different conceptual structures emerge through measures of concept inclusion and similarity, and fuzzy categorical data is presented in terms of fuzzy membership (Cross & Firat, 2000; Robinson, 2003; Yazici & Akkaya, 2000). Deontological or normative uncertainty is associated with consequentiality paradigm of decision or value judgments, e.g., in multicri- teria classication, risk perception, preference elicitation. There has been extensive research from behavioral decision theoretic perspective to understand human judgment under uncertainty ( Tve rsky et al., 1974). The heuristics that decision makers use (Kahneman et al., 1982) can lead to biases in many spatial decision making scenarios, such as watershed prioritization, location or facility planning, habitat suitability modeling. Uncertainty may also spring from conicting value-laden terms or preference-ordered criteria (Li et al., 2004; 2005). It could be possible that preference order induced from a set of attributes may contradict the assignment of the degree of risk classes, resulting in potential paradoxical inference. Pöyhönen & Hämäläinen (2001) showed that the use of weights based on the rank order of attributes can only easily lead to biases when the structure of a value tree is changed. While it is difcult to extract complete preferential information, research is going on to work with information-gap uncertainty in preferences by using graph model for conict resolution (Ben- Haim & Hipel, 2002) 4. conc Lus Ion Uncertainty in spatial predictive and classica- tory system is an endemic and multi-faceted aspect. Recognition and agreement of appropriate characterization and denition of typologies of uncertainty semantics are prerequisite to efcient handling and management. This article charac- terizes the objective, subjective and normative aspect of uncertainty. It specically differentiates uncertainty resulting from lack of knowledge and objective variability or intrinsic properties of spatial systems. Various new directions of uncertainty handling mechanism are discussed. While currently there are many promising direc- tions of research in managing different types of uncertainty, a new paradigm is required in spatial analysis that is fundamentally driven by the consideration of uncertainty. 5. r eferences Ahlqvist, O., Keukelaar, J., & Oukbir, K. (2000). Rough classication and accuracy assessment. International Journal of Geographical Informa- tion Science, 14(5), 475–496. Ben-Haim, Y., & Hipel, K. W. (2002). The Graph Model for Conict Resolution with Information- Gap Uncertainty in Preferences. Applied Math- ematics and Computation, 126, 319-340. Burrough, P., & Frank, A. (1996). Geographic Objects with Indeterminate Boundaries. 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[...]... non-spatial region content contains non-spatial data of geographical query results These region contents are associated with the geographical region descriptor It contains information about each geographical query stored in cache The cache is then divided into two parts (see Figure 1): (i) the non-spatial part of the cache composed of the non-spatial region content and it associated description contained... identifier We simply use the centroïde of the object defined as a point 354 Figure 1 General cache structure Geographical Region Descriptor non-spatial region content spatial region content non-spatial part of cache spatial part of cache Figure 2 Cache organization non-spatial region spatial region content content identifier Legend: spatial data Figure 3 Spatial part of the cache Hash table ID Counter... divided into two parts (Chidlovskii, Roncancio, & Schneider, 1999): the region descriptor describing each query result stored in cache, and the region content where the data are stored In the case of geographical queries, we introduce two kinds of region content: the non-spatial region content, and the spatial region content The spatial region content contains nonredundant spatial data of geographical... 6(3), 28 5-3 16 Geographic Visual Query Languages and Ambiguities Treatment Egenhofer, M J (1997) Query Processing in Spatial-Query-by-Sketch Journal of Visual Languages and Computing, 8( 4), 40 3-4 24 Erwig, M., & Schneider, M (2003) A visual language for the evolution of spatial relationships and its translation into a spatio-temporal calculus Journal of Visual Languages and Computing Elsevier, 14, 181 –211... Applications (DEXA 19 98) , LNCS 1460, SpringerVerlag Publications (pp 29 0-2 99) Lbath, A., Aufaure-Portier, M., & Laurini, R (1997) Using a Visual Language for the Design and Query in GIS Customization International IEEE Conference on Visual Information Systems San Diego, CA (pp 19 7-2 04) Lee, Y C., & Chin, F (1995) An Iconic Query Language for Topological Relationship in GIS International Journal of geographical... International Centre for Integrative Studies (ICIS) Wilkinson, G G., & Megier, J (1990) Evidential reasoning in a pixel classification hierarchy - a potential method for integrating image classifiers and expert system rules based on geographic context International Journal of Remote Sensing, 11(10), 196 3-1 9 68 Worboys, M (19 98) Imprecision in Finite Resolution Spatial Data Geoinformatica, 2(3), 25 7-2 80 Worboys,... Integration of imperfect spatial information Journal of Visual Languages and Computing, 12, 6 1 -8 0 Yazici, A., & Akkaya, K (2000) Conceptual modeling of geographic information system applications In G Bordogna & G Pasi (Eds.), Recent Issues on Fuzzy Databases (pp 129–151) Heidelberg, New York key T er ms Frame of Discernment: The set of all the possible sets of the hypotheses or class categories Imprecision:... Spatial-Query-By-Sketch language (Egenhofer, 1997; Blaser and Egenhofer, 2000), similarly to Sketch!, is based on a formal model for topological spatial relations and a computational model for the constraints relaxation Each query produces a set of candidate interpretations as result and the user selects the correct one Another language based on the second approach is VISCO (Wessel and Haarslev, 19 98) ... languages on the grounds of methodologies they adopt to resolve the problem of ambiguity A first group of languages handles the ambiguity by allowing the use of few operators or spatial relationships, such as Pictorial Query By Example and SVIQUEL Considering only limited kinds of spatial relations (directional relations) PQBE avoids multiple interpretations of the query but reduces the possibility of formulating... (2005) Query-By-Object Interface for Information Requirement Elicitation Implementation Fourth International Conference on Mobile Business (ICMB2005) IEEE Computer Society, Sydney, Australia, (pp 66 7-6 70) Szmurlo, M., Gaio, M., & Madelaine, J (19 98) The geographical Anteserver: a Client/Server Architecture for GIS EOGEO’ 98 workshop Salzburg, Austria, (pp 7 8- 8 5) Wessel, M., & Haarslev, V (19 98) VISCO: . 1. spAt IAL pred Ict Ion And cLAss If IcAt Ion Geospatial predictive models entail an array of analytical techniques of data mining, classical statistical and geostatistical models that attempt. theory of conceptual spaces a parameterized representation of classes are modeled as a collection of rough- fuzzy property where an attribute itself can be treated as a special case of a concept permission of IGI Global is prohibited. Abstr Act The main issues of spatial databases and Geographic Information System (GIS), concern the represen- tation, the management and the manipulation of a