GeoVSM: An Integrated Retrieval Model for Geographic Information

Transcription

1 GeoVSM: An Integrated Retrieval Model for Geographic Information Guoray Cai School of Information Sciences and Technology The Pennsylvania State University 002K Thomas Building, University Park, PA GEM$MWXTWYIHY Abstract. Geographic information exists in multiple forms, such as cartographical maps, images, and texts. Effective retrieval systems for geographic information are currently studied by both geospatial information scientists and library/information scientists. The two groups take quite different approaches, which have rarely been explicitly compared and connected. This paper discusses the advantages and limitations of current geographic information retrieval (GIR) and textual information retrieval (IR) systems in dealing with multimedia geographic information, and proposes a new retrieval model, called GeoVSM (Geographic + Vector Space Model), which integrates coordinatebased geographic indexing with the keyword-based vector space model in representing information space. Document ranking by relevance is supported by document-query similarity measures, taking into account the degree of relevance in both the spatial domain and the thematic domain. To support visual querying and browsing, the GeoVSM model recognizes the fundamental differences in the dimensionality and configuration of geographic space and thematic space, and demands multi-view visual interfaces based on a world metaphor and a desktop metaphor. As an example of such interfaces, GeoVIBE is presented, which supports a coordinated GeoView and a VibeView for smooth integration of two browsing strategies using geographic clues as well as thematic clues provided by users. 1 Introduction Finding relevant geographic information over the network is critical to many digital library users, ranging from scientists involving global change studies to families preparing for relocation. The nature and importance of building a digital library for geographic information is well reflected in the recent National Research Council s report on distributed geolibraries [25]. The report envisioned an information service that allows anyone to locate and integrate information quickly and easily about any place on the Earth s surface. Such services are not available yet, but the problem has been approached by researchers from geographic information science (GIScience) and information science from their individual discipline perspectives. However, the complexity and challenges of geolibraries may require integrating multi-discipline efforts. This paper explores the possibilities of integrating information retrieval models used in M.J. Egenhofer and D.M. Mark (Eds.): GIScience 2002, LNCS 2478, pp , Springer-Verlag Berlin Heidelberg 2002

2 66 Guoray Cai GIScience and vector-space models used in textual-based information retrieval (IR) systems for searching and browsing digital geolibraries. 1.1 Document Space and Representation Geographic information refers to any documents that reference some part of the Earth s surface. In a digital geolibrary, some documents exist in visual forms (such as maps, remote sensing images, and aerial photographs) and other documents in textual forms (such as field survey descriptions, technical papers, and reports). Common to all these documents is that each has some form of geographic footprint. Cartographic maps and geo-referenced images have geographic footprints defined by bounding coordinates of their edges. Textual documents also have footprints, but they are defined by geographic terms such as place names. The two types of footprints can be made compatible by converting one type to the other with the help of a gazetteer (a form of index that relates place names to coordinates of locations and extents). Each document may also be associated with a number of thematic subjects (non-spatial attributes) assigned by human catalogers or derived from automated document analysis. Users may search geolibraries by geographic location, by geographic place name, or by thematic subjects. Alternative information retrieval models distinguish themselves by the ways they structure document space, matching documents to queries, and by the ways they allow users to interact with the document collection. Here a document space defined as a high-dimensional space within which each dimension represents an attribute that is a potential discriminator of documents in a collection. For geographic information, the document space can be divided into two kinds of subspaces: a geographic subspace and a thematic subspace. Geographic subspace is a two dimensional space. Documents may be considered similar or different based on the spatial relationships (inclusion, overlapping, adjacency, etc) of their footprints in geographic space. Thematic subspace is a multidimensional space, where dimensions represent different thematic concerns of a document collection. The number of dimensions in theme space may vary depending on how specific the concepts in the document are categorized into themes. 1.2 Vector Space Model of Document Space One of the most popular models of document space developed in textual-based information retrieval research is arguably the vector space model [29, 30]. Using a vector space model, the content of each geographic document can be approximately described by a vector of (content-bearing) terms, which are a combination of thematic subjects and place names. An information retrieval system stores a representation of a document collection using a document-by-term matrix, where the element at position (i, j) corresponds to the frequency of occurrence of term j in the ith document. In the vector space model, all the objects (terms, documents, queries, concepts, etc) can be similarly represented as vectors. The model can support selecting and ranking of documents by computing a similarity measure between a document and a query or another document [11, 17, 29, 36].

3 GeoVSM: An Integrated Retrieval Model for Geographic Information 67 There are obvious advantages and disadvantages of using the vector space model in retrieving geographic information. The vector space model is well accepted as an effective approach in modeling the thematic subspace, and it allows geographic information to be handled the same way as non-geographic information. However, the vector space model has some serious problems when used for modeling the geographic subspace. First, the geographic space is inherently continuous and cannot be adequately approximated using a set of place names (which are discrete in nature). Second, the vector space model assumes that indexing terms are pair-wise orthogonal, which is severely violated by the interdependencies among geographic terms within a document. As an example, if a document mentions four place names Pittsburgh, Philadelphia, Harrisburg, and Hagerstown the four place names will be treated as four independent dimensions in a vector space model, whereas in fact, they are points (or regions) in a two-dimensional geographic space. Additional concerns of using locational terms as geographic indexes include: ambiguity in meaning, nonunique place names, place name might change over time, and spelling variations [1, 10, 13]. Some of the above problems can be partially alleviated by integrating proper ontologies of places [15] into keyword-based search methods. 1.3 Geographic Model of Document Space Since location-based search is the primary search mode for geolibraries, geographic footprints of documents are best represented explicitly and formally. Geographic information systems implement formal models of geographic space, which are capable of managing geographic footprints of documents more precisely than geometric shapes (points, lines, and polygons) defined in coordinate systems. Thematic space is represented as layers of hypermaps, with each layer representing documents of the same or similar thematic types. A geographic model of a document space as exemplified in any Geographic Information System (GIS) is capable of processing arbitrarily complex spatial queries, but the most common spatial queries to geographic digital library are believed to be of three types: point, region query, and buffer zone [19, 20]. Coordinate-based indexing and access has many advantages in dealing with geographic information. Spatial indexing based on coordinates generates persistent indexes for documents, since it is well defined and is immune to any changes in place names, political boundaries, and linguistic variations. Indexing texts and other georeferenced objects (such as photographs, videos, remote sensing datasets, etc.) by coordinates also permits the use of the hypermap concept [19]. A geographic model, as is currently implemented in GISs, is specially designed and optimized for handling maps, images, and other visual geographic data with clearly defined boundaries or extents. However, there is a considerable amount of geographic information that exists in textual forms that are not easily integrated into GIS for mapping and spatial analysis, due to the difficulties of natural-language understanding for geo-referencing text. Although approaches for georeferencing textual documents has been investigated [18, 35], there has been little impact on the way that textual documents and geographic documents are separately managed. GISs provide special utilities for matching the geographic locations of a query to the geographic index of documents, but the layered organization of themes in a GIS, although working well with maps and images, is too restrictive for handling free-text documents. Recent

4 68 Guoray Cai efforts have been made to unify the retrieval models and user interfaces to either geographic, coordinate-based retrieval systems [18] or keyword-based systems [4], but have met with limited success. 1.4 Towards a Joint Geographic and Vector Space Model The vector space model and the geographic model have obvious advantages and disadvantages for handling geographic information. Current practices make it easier to search textual geographic documents in traditional IR systems, and it is easier to search cartographic maps and images with the geographic model. This forces us to manage geographic information in two separate types of systems: using a GIS for maps and images, and using traditional catalog methods and keyword indexing for textual documents. The decision for how to organize geographic information in digital geolibraries is currently determined, to a large extent, by the ease of indexing, rather than by the goal of supporting natural user interaction with geographic documents. From a user perspective, it would be far more valuable if cartographic, imagery, and textual forms of geographic information can be retrieved in an integrated fashion from one system environment according to user-specified geographic and thematic scopes. To this end, neither a GIS nor a textual IR system is adequate. Research in cognitive science and linguistics has pointed to the fact that there appear to be two separate cognitive facilities: one that deals with space, and one that deals with language and other symbols. These two separate facilities have separate ways of representing knowledge: spatial representation and conceptual representation. Both facilities participate in the understanding of the structure and contents of an information space, but they do so in rather different ways one generates a spatial structure of the information space, and the other generates a conceptual structure of the information space. However, the two ways of representing information are likely to be interlinked and mutually enhancing. Traditional approaches to information seeking and information retrieval have used primarily the conceptual facility of the mind. In other words, they are linguistic in nature, requiring the use of vocabulary and of syntax. Spatial representations of information can be used in addition to conceptual representations to provide a basis for finding information. Recent developments in data visualization and similar spatial representations of information in digital libraries attempt to balance conceptual approaches with spatial approaches to information. Finding the right balance between conceptual and spatial approaches to information raises questions about the interaction between the two underlying cognitive facilities. Understanding the kinds of interaction will reveal the potential challenge as well as opportunities for enhanced information retrieval. Here we mention two cognitive principles that seem to be most relevant in the design of interactive IR systems for geographic information. Principle I: The principle of isomorphism between the dimensionality of representation and the information space. The existence of two fundamentally different cognitive facilities (spatial and conceptual) calls for dual representation of information. However, in the design of an IR system, the dual representation will enhance information seeking only if the dimensionality of the representation space and the dimensionality of the information space match with little ambiguity [2]. It was mentioned previously that geographic informa-

5 GeoVSM: An Integrated Retrieval Model for Geographic Information 69 tion space has two subspaces: a geographic subspace and a thematic subspace. The geographic subspace is inherently a two-dimensional space, and it matches with the dimensionality of geographic model of the document space, but it seems to be incompatible with the vector space model of geographic space. The thematic subspace has (variably) high dimensions, and is compatible with a vector-space representation, but does not fit well with a layered structure of the geographic model. Although there are extensive evidences that geographic or spatial representations of non-geographic information may potentially facilitate learning and memory [27], Allen [2] has correctly pointed out the danger of an arbitrary mapping of dimensionality. Principle I suggests that a unified IR model for geographic information is not likely to be either the vector space model or the geographic model, but can be somewhat integrating both geographic and thematic models in a complementary fashion. Principle II: The principle of supporting interactions between spatial and conceptual cognitive facilities in information retrieval. Information retrieval is increasing being treated as a process of sense making, knowledge acquisition, and problem solving [21] through interacting with the information space presented by a system. The cognition of geographic information space is inherently more complex, and involves actively reconstructing knowledge about the document collection by multi-dimensional (spatial and semantic) navigation, pattern reading, and reasoning. Researchers in digital libraries have started to recognize the existence of two cognitive facilities (spatial and conceptual), and people may choose to use spatial, conceptual, or some combination of both facilities to construct their mental models of information space [2], depending on the types of information they are dealing with. For geographic information, the two cognitive facilities are combined in a highly interactive and interwoven way in making sense of a document space and judging relevance. Unfortunately, the detailed mechanisms on how the two cognitive facilities combine and interact are less understood and are likely to be dependent on different tasks. Recently, Fabrikant and Buttenfield [8] suggested that there are three frames of references for indexing and visualizing geographic information: geographic space, cognitive space, and Benediktine space. A Benedektine space exhibits well-formed semantic properties that help users to establish relationships between features of physical space formed by interface objects and abstract information space. By using proper metaphors, Fabrikant and Buttenfield demonstrated that geographic visualization can be extended to visualize conceptual space that has the properties of Benediktine space. In the view of this author, perhaps the biggest challenge for geographic information retrieval is that the geographic information encompasses both geographic space and thematic space, and it is almost impossible to select any interface metaphor so that the visualization space exhibits the properties of Benediktine space. The author believes that effective information services must accommodate the fact that people usually have only fragmented and vague clues of where and what they are looking for, and they search relevant documents using both geographic search and thematic search and switching their strategies and viewing perspectives from time to time. Users of geographic information should be able to navigate through the information space at will, using all the knowledge they possibly have, finding new clues, and eventually narrowing the search space down to relevant documents [23]. The overarching argument of this paper is that effective support for the retrieval of multimedia geographic information requires a complementary perspective on the retrieval

6 70 Guoray Cai models proposed in GIS and textual IR systems. The lack of integrated research between GIS and IR fields has recently prompted the birth of a new research area geographic information retrieval (GIR) [18]. Based on the above principles, this paper proposes a new information retrieval model, GeoVSM, for geographic information. Figure 1 compares the GeoVSM approach with two existing paradigms. Figure 1a shows that GIS tends to index only that portion of the information about an entity that is encoded spatially (such as cartographic maps, diagrams, images) and leaves out information encoded in textual form. In contrast, Figure 1b shows that keyword-based IR systems have focused on indexing and retrieval of textual document, even though the techniques can be applied to visual geographic documents if they are adequately described by metadata. The problem with these two paradigms is that their indexing strategy was largely determined by the information encoding in the source documents, whereas a more logical way should be based on the type of information contents encoded in the source documents. If the information content is about geographic properties of some entities, then it should be indexed in geographic space, otherwise it should go to thematic space indexing. This is the philosophy suggested by GeoVSM model (Figure 1c). The rest of the paper is organized as follow. Section 2 describes the principles of GeoVSM in indexing, searching and browsing geographic information. The model resulted from our theoretical insights and known facts from spatial cognition and human information searching behaviors. The dual encoding (coordinate indexing and keywords indexing) of documents enables the visualization of the document space by both geography and concepts. In section 3, it is argued that searching geographic information is best supported by an integrated multi-view that allows users to navigate and judge relevance of documents in a visual and interactive manner. We describe an example of such an interface and a prototype system, called GeoVIBE, that demonstrates our design concepts. It uses a combination of a desktop metaphor and a world metaphor for information organization and visualization, and can be readily supported by GeoVSM. The system suggests a joint research agenda for relevant fields including geographic information science, information retrieval, and information visualization. 2 GeoVSM: A New Information Retrieval Model In the previous section, we suggested that an ideal retrieval model should combine the advantages of both the geographic model and the vector space model. Here we formally define such a model, calling it GeoVSM. We address the way that documents and queries are represented and associated in the new model. 2.1 Document Representation Each document will be indexed both by footprint (in geographic coordinate space) and by a term vector (in vector space). These two indexes are linked by the IDs of the documents. In particular, geographic indexes only represent the geographic scope of the document, and term vectors only represent thematic scope of documents. Accordingly, there is no need to use multi-layered index maps to differentiate between

7 GeoVSM: An Integrated Retrieval Model for Geographic Information 71 Visual documents Spatial Spatial Encoding Geo Indexing Map View Thematic Verbal Encoding Textual documents (1a) The information retrieval paradigm used in traditional GISs Visual documents Spatial Spatial Encoding Thematic Verbal Encoding Keyword Indexing Theme View Textual documens (1b) The information retrieval paradigm used in traditional IR systems Visual documents Spatial Spatial Encoding Geo Indexing Map View Thematic Verbal Encoding Keyword Indexing Theme View Textual documents (1c) The information retrieval paradigm used in GeoVSM Fig. 1. Comparisons of three different IR paradigms different themes, and there is no need to include any geographic terms in the term vector representation. The net result is that the two indexes of a document will be truly complementary and non-redundant. The rule can be applied to representing queries.

8 72 Guoray Cai Geographic Scope Document Thematic Scope Geographic model Œ Coordinate indexing Œ Spatial similarity Œ Map-like retrieval interface Œ Œ Œ Vector space model Keyword indexing in term vector Vector space similarity Textual or visual interface Fig. 2. A proposal for a joint geographic and vector space model 2.2 Document Similarity Measures In this paper, we assume that any document has a limited geographic scope, GS d, and a thematic scope, TS d. Similarly, a query on a document collection also has a geographic scope, GS q and a thematic scope, TS q. The degree of relevance of a document to a query can be determined by the following measure: Rel(d, q) = ƒ(simg(gs d, GS q ), SimT(TS d, TS q ) ) (1) where SimG( ) measures the similarity (i.e., the degree of overlapping) between the geographic scopes of the document and the query; SimT( ) measures the degree of overlapping between the thematic scopes of the document and the query; and ƒ(*) is a function for combining relevance measures of geographic dimensions and thematic dimensions. The use of a common representation of the geographic scopes of documents and queries as geometric shapes in a geographic coordinate system allows automatic spatial reasoning techniques to be incorporated for measuring spatial similarity SimG(GS d, GS q ). Spatial similarity can be measured in a number of different ways, depending on what spatial properties are considered important by the users. From a data integration perspective, Abdelmoty and El-Geresy [1] proposed a system of spatial equivalence classes based on similarities in positions and spatial extents, spatial relationships between objects (such as topological and directional relationships), and object properties (such as shape, size, dimension, and representation details). In the context of spatial information retrieval, the first two types of similarity measures (positional similarity and topological similarity) are likely to be the most effective measures for document similarity in geographic space, and hence will be the focus of the subsequent discussion. Positional similarity refers to the degree of match between the position and extent of the geographic index shapes of documents. Papadias et al. [28] discussed two general computational schemes. One scheme combines the minimum bounding rectangle (MBR) representation of index shape extents with the projection-based encoding of spatial overlapping relationships to allow efficient reasoning of qualitative neighborhood relationships. The other scheme uses spatial join operations (available from most GIS) to detect the degree of intersection between the spatial scopes of the two documents. Topological similarity among basic shapes of points, lines, and polygons can be derived using the topological reasoning approaches [5, 6] or the adjacency matrix approach [1].

9 GeoVSM: An Integrated Retrieval Model for Geographic Information 73 Thematic similarity of documents can be computed using various similarity measures in the vector space model, such as cosine measures, distance and angular measures [11, 17, 29, 36], and probabilistic measures of similarity based on term frequencies. 3 Visualizing Document Space With the increasing complexity of information retrieval mechanisms and the recognition that information search is to a large extent a dynamic interactive process, visual interfaces to IR system have been explored as an alternative to textual infterfaces. Zhang [36] presented a number of advantages of visualization, including (1) making visible the relationships between query and documents, (2) making visible the information retrieval process, (3) making the results more interpretable by providing visual context, and (4) facilitating explore and recognition in discovering relevant information. When dealing with the geographic information in digital libraries, the selection of appropriate interface metaphors and the definition of visual query languages becomes an especially challenging task. This is due to the double nature of geographic data, which has a geometric component (needed to define the spatial relations) and a thematic component (referring to a real-world entity or concept) [31]. Though different, the two components turn out to be complementary for the description of geographic data. On the user side, people seem to have two separate and inter-related cognitive facilities in dealing with spatial information: spatial and conceptual [14, 16]. People also maintain two separate representations of the same object (e.g., a room). A previous study has revealed that human subjects tend to draw a geographic object by providing its geometric representation, while referring to its meaning in the real world, namely to the theme that the object describes [22]. This means that the two parts of representations of geographic data are intrinsically related in human minds. Based on the above principles, we may conclude that geographic information libraries should ideally be equipped with two separate sets of interface metaphors and query languages (spatial and thematic), which are internally linked by an integrated indexing and retrieval mechanism. Digital libraries have explored both spatial and conceptual approaches for accessing geographic information, but have not achieved the right balance in utilizing both in an integrated fashion. Next, we review existing interface metaphors and query languages for visualizing the document space in spatial and thematic dimensions, with the intention of formulating new ways to integrate them. 3.1 Geography as Information Space The view of geography as an information space emphasizes the use of the abstract sense of the world (places and locations) in judging relevance and browsing large numbers of documents. When documents are put into the context of the geographic world, the potential spatial interactions between places (diffusion, movement of information through space) and the spatial patterns of document distribution provide rich clues for judging relevance of a document in its associated geographic context.

10 74 Guoray Cai Information browsing by geography is best facilitated by a map-based graphical user interface. This allows visual inspection of the document space with contextual interpretation of relevance implicit in spatial relationships. A map-based graphical interface tends to be intuitive and comprehensible to anyone who is familiar with maps [18]. Morris [24] suggests that when users are given a choice between menu (text-based) and map-based graphical interfaces to a geographic database, they prefer the maps. There are a number of variations of map-based interfaces in existing library systems. The Alexandria digital library (ADL) [32] uses the idea of a geographic footprint to represent the location on the Earth s surface associated with maps and image objects or with user queries. Users can specify an arbitrary query area and retrieve all information items whose footprints overlap with the query area. ADL s map interface is currently not integrated with term-based search methods, such as gazetteer and catalog search, which are anticipated improvements [12]. Other systems use a tiles metaphor as a simplified way to provide geographic access in visual forms, where a tile is the smallest regular division of the space that has indexing capability. For example, Microsoft TerraServer [3] is a multimedia geographic data warehouse that serves aerial, satellite, and topographic imagery. It indexes source images and photos by scenes and tiles. It also use multi-layered index maps, which categorize imagery into themes by data source, projection system, and image style. A user may query images in three styles: coverage map, place query, and coordinate query. A related line of research is the use of Hypermaps as access interface to hypermedia or multimedia spatial information [19, 20, 34]. In a hypermap, the links to documents are represented by an icon or footprint (a polygon that outlines the area described by the object linked to the footprint), and selection brings up the document referenced by the link. A hypermap interface is dynamic, because the view is made up of a collection of map layers, each of which may be turned on/off independently of other layers. This allows users to control what is shown on the display at any situation. 3.2 Desktop as Information Space In visualizing document space based on simple Boolean and vector space models, a real challenge is to support users in visually exploring the structure of the highdimensional document space and visually formulating queries with ease and efficiency. In particular, complex queries usually involve multiple user-defined concepts (also called reference points ), against which all the documents are judged for their relevancy. There have been visual interfaces that facilitate the visualization of complex relationships among documents and support specifying arbitrarily complex queries graphically. For a review of these interfaces, see [17, 36]. Two of them are of most interest to this study and are reviewed here. InfoCrystal [33] provides a spatially compact interface for complex Boolean or vector space queries. An InfoCrystal interface has two sets of icons: criteria icons represent user-defined criteria and are placed at the surrounding edges with even space; interior icons representing unique types of queries are placed according to proximity and ranking principles. Queries can be specified by selecting individual icons or a group of interior icons in a graphical manner. InfoCrystal is most commonly used

11 GeoVSM: An Integrated Retrieval Model for Geographic Information 75 with a Boolean retrieval model, but it also has been extended to handle th evector space model. The VIBE system, originally proposed by Olsen et al. [26], has a similar goal as InfoCrystal. A user can select an locations. Then the positions of visualized documents are determined according to the desktop-pile metaphor. A pile metaphor encourages the thinking of an office desk as a number of piles of documents, each pile holding similar documents. If a document is related to more than one pile, it is put between those, closest to the most relevant pile. The pile metaphor is based on the notion that people often use piles for casual arrangement of documents. This pile metaphor has been developed into a content awareness information retrieval interface and implemented in the VIBE system [26]. VIBE is most appropriate for vector space models handling queries involving multiple reference points, but has also been extended to handle Boolean queries. One of the common characteristics of InfoCrystal and VIBE is that they both use the spatial layout of icons to indicate semantic closeness, much like organizing files on a desktop. 4 GEOVIBE Architecture Based on the principles of a joint geographic and vector space model for information retrieval, we present GeoVIBE as a new interface for browsing geographic information from digital libraries. The main feature of GeoVIBE is that it supports visual interaction with the document space utilizing the user s common-sense geographic knowledge as well as thematic concepts. Figure 3 shows a snapshot of the GeoVIBE system. The display consists of two opened views of the document space. The subwindow at the left is the GeoView, which shows a map with clickable icons of different shapes and sizes linked to document items. The right-side window is the VibeView, where all the documents are presented in a coordinate system defined by Points of Interests (POI) on the display. In the following, we show how the two views of the document space work together. 4.1 GeoVIEW GeoView employs the world metaphor to organize interface objects on the screen [9]. The world metaphor is implemented as a dynamic hypermap [20]. In designing a map-based interface, a set of reference maps must be chosen for use in organizing and presenting information. Special care must be taken since most maps in existence were designed to support the work of cartographers, city planners, and as such they are illsuited for the purpose of browsing a variety of geo-referenced information. It will be the best to have these maps specially redesigned to match the commonsense view of geographic world [7]. 4.2 VibeVIEW The VibeView is similar to the interface of th evibe system [26]. First, there is a coordinate system that is established by defining a set of points of interests (POI) on the display. Each POI consists of a vector of key values describing a subject of interest to the user, and a unique icon placed on a position within the VibeView window. POIs

12 76 Guoray Cai may be chosen to be any of the following: (1) user queries expressed in the vector space model; (2) personal interest profiles; and (3) some known documents. The choice and use of POIs is completely left to the user s discretion. After POIs are defined, the placement of a document icon is determined by the relative similarity of a document to the POIs. The position of a document icon gives an indication of the contents of the related document. The size, color, and shape of a document icon may vary according to a user-defined function (e.g., the length of the document). Document visualization through the VibeView is especially useful for identifying groups of documents in a collection that does not fit a hierarchical structure. Fig. 3. A prototype system - GeoVIBE 4.3 Coordination between GEOVIEW and VIBEVIEW Initially, all documents are presented in both views. The user may decide to browse in either view, and the result of the browsing will be immediately reflected in the other view. For example, a user may narrow the search by drawing an Area-of-Interest in GeoVIEW, which results in a reduced search space by adding a geographic constraint. Then, the user may circle a pile of documents in VibeVIEW to further narrow the search. The system will keep track of the changes of a user s interest and modify the two views accordingly. The browser will allow for a series of possible actions, such as modifying the color or shape of icons, removing icons, adding trace lines as icons move, and so on.

13 5 Conclusions GeoVSM: An Integrated Retrieval Model for Geographic Information 77 This paper proposed the new information retrieval model GeoVSM, which integrates the vector space model and the geographic model in spatial digital libraries. The model was explained using cognitive principles. A prototype system, GeoVIBE, has been developed to test the principles of the GeoVSM model. We have made some informal user studies on GeoVIBE, and the result was quite promising. However, it is recognized much more work is needed in order to establish GeoVSM as an information retrieval model. In particular, the following work is planned in future studies: (1) a more detailed specification of document similarity measures that combines similarity measures in both geographic subspace and thematic subspace of a document space; (2) computational approaches for automated generation of a geo-reference index and a thematic vector index for textual, image, and cartographic documents; and (3) usability tests of GeoVIBE for different user groups and tasks to examine the advantages and disadvantages of such a visual browsing and retrieval system. Acknowledgments The author would like to thank Frank Ritter and Amanda Spink for proofreading an earlier draft of this paper. This research is based on work supported by the National Science Foundation under Grant No. BCS ; PI: Alan M. MacEachren, CoPIs: Rajeev Sharma and Guoray Cai. References 1. Abdelmoty, A. and B. El-Geresy (2000). Assessing Spatial Similarity in Geographic Databases In: GIS and Geocomputation. P. Atkinson and D. Martin (eds.), Taylor & Francis: Allen, B. (1998). Information Space Representation in Interactive Systems: Relationship to Spatial Abilities. Proceedings of the Third ACM Conference on Digital Libraries, Pittsburgh, ACM Press Barclay, T., D. Slutz, and J. Gray (2000). Microsoft TerraServer: A Spatial Data Warehouse. Proceedings of the 2000 ACM SIGMOD on Management of Data, Dallas, TX, ACM Buttenfield, B. and M. Kumler (1996). Tools for Browsing Environmental Data: The Alexandria Digital Library Interface. Third International Conference on Integrating Geographic Information Systems and Environmental Modeling. conf/santa_fe_cdrom/sd_papers/buttenfield_babs/babs_paper.html, Santa Fe 5. Egenhofer, M. and D. Mark (1995). Modeling Conceptual Neighborhoods of Topological Line-Region Relations. International Journal of Geographical Information Systems 9(5): Egenhofer, M. and R. Franzosa (1995). On the Equivalence of Topological Relations. International Journal of Geographical Information Systems 9(2): Egenhofer, M. and D. Mark (1995). Naive Geography. In. Spatial Information Theory: A Theoretical Basis for GIS. A. Frank and W. Kuhn (eds.). Berlin, Springer-Verlag: 1-15.

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