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Geographically oriented search must consider both the thematic and geographic dimensions of relevance when matching documents to queries. We propose a dynamic document ranking scheme to combine the thematic and geographic relevance measures on a per-query basis. Query specificity is introduced to determine the relative weights of different sources of ranking evidence for each query. A preliminary evaluation comparing with human judgment shows that our method to distinguish different types of geo-referenced queries based on query specificity is promising to address the issue of relevance combination in GIR document ranking. In addition, we explore the possibility of using Dempster-Shafer's theory to combine the two different sources of ranking evidence.

Our research focuses on Geographic Information Retrieval for Indonesian documents. We constructed a Geographical Gazeeter for geographic locations based on information that we collected from the Geographic resources. We used the table to identify the locations of events that are mentioned in the documents. We added a location index associated with each document. We also identified the possible locations based on words that indicate the relative position to locations mentioned in the queries. The result of our experiments showed that the location index and the processing of words indicating relative positions improved the retrieval performance of natural language queries. Applying the query expansion techniques by adding keywords and locations from the documents to the queries resulted in significant performance improvement.

This paper presents an approach that helps to discover geographic locations from the recognition, extraction, and geocoding of urban addresses found in Web pages. Experiments that evaluate the presence and incidence of urban addresses in Web pages are described. Experimental results, based on a collection of over 4 million documents from the Brazilian Web, show the feasibility and effectiveness of the proposed method.

A Geographic Information Retrieval (GIR) system for answering geographic queries has to cope with various information needs, which have a wide range of contexts and implicit requirements. A user, for example, who is looking for a place to spend his or her holidays certainly has a different understanding of distance than a user looking for a bar in the city he or she lives in.To get a better understanding of geographic information needs and their implications for GIR systems, we analysed real world (geographic) queries with regard to different facets of geographic references in queries. The results of this analysis are presented in this paper, the aim of which was a classification of the geographic aspects of information needs. We present empirical results and line out possible classification criteria, which could be helpful in designing GIR systems that are able to consider different semantics of geographic references in queries.

In this paper we describe the development of a geographic co-occurrence model and how it can be applied to geographic information retrieval. The model consists of mining co-occurrences of placenames from Wikipedia, and then mapping these placenames to locations in the Getty Thesaurus of Geographical Names. We begin by quantifying the accuracy of our model and compute theoretical bounds for the accuracy achievable when applied to placename disambiguation in free text. We conclude with a discussion of the improvement such a model could provide for placename disambiguation and geographic relevance ranking over traditional methods.

This paper introduces the concept of spatial filters as an approach to increasing the relevance of the information retrieved by users of mobile information systems. This approach applies post-query filters to remove results that are deemed not relevant given some aspect of a mobile individual's spatial behaviour. The aim is to reduce the volume of results returned to users of mobile information systems, to avoid the need for manual filtering. An opportunity for future research is to consider how these filters can be applied to the task of combining rankings based upon thematic and spatial criteria, for documents retrieved from unstructured or semi-structured collections, such as those handled by geographic information retrieval systems.

In this paper, we present our preliminary work on a Geographic Information Retrieval (GIR) system that utilizes loosely coupled web services and Google Earth™(GE) to retrieve, extract, combine, and visualize situation information from multi-scale, open source web documents. Our intent with this work is to support situation assessment in the crisis management domain through tools that link and geographically contextualize information contained in text documents retrieved from multiple sources. In particular, our present work focuses on combining two data sources - The Federal Emergency Management Agency (FEMA) National Situation Updates and Google News"™.

Both geographic information systems and information retrieval have been very active research fields in the last decades. Lately, a new research field called geographic information retrieval has appeared from the intersection of these two fields. The main goal of this field is to define index structures and techniques to efficiently store and retrieve documents using both the text and the geographic references contained within the text. We present in this paper the architecture of a system for geographic information retrieval. It defines a workflow for the extraction of the geographic references in the document. In addition, a new index structure is defined that combines an inverted index, a spatial index, and an ontology. This structure improves the query capabilities of other proposals.

Metadata access to complex information bases through multidimensional dynamic taxonomies (aka faceted search) is rapidly becoming a hot topic both in research and in industry, where e-commerce applications based on this access paradigm are becoming pervasive. The dynamic taxonomy model was the first model to fully exploit multidimensional classification schemes and to integrate query and exploration into a single visual framework. We review a number of research results in data modeling, implementation, user interaction and emerging application areas of dynamic taxonomies and faceted search, originating from the Department of Informatics, University of Torino.

Finding and accessing suitable geographic information in the open and distributed Web environment of current geographic information services (GIServices) is a crucial task. Problems of semantic heterogeneity arise during an exact word matching search for GIServices and spatial database. Moreover, there are only a few services registered in metadata catalogues so that we cannot ignore Web contents which also contain geographic information. This paper introduces an architecture of a spatially aware search engine, for semantic interoperability of distributed and heterogeneous GIS on the Internet. The proposed architecture is based on mediation and ontologies, which help to providing support for query disambiguation, query term expansion and Web resource retrieval. It also gives the structure of the location ontology and service ontology and shows the detail of semantic query process. Based on a real-world scenario of restaurant guide search, the application of our approach shows that the geographic information retrieval can be efficiently supported.

Temporal and spatial information in text documents is often expressed in a qualitative way. Moreover, both are frequently affected by vagueness, calling for appropriate extensions of traditional frameworks for qualitative reasoning about time and space. Our research aims at defining such extensions based on fuzzy set theory, and applying the resulting frameworks to two important kinds of intelligent information retrieval, viz. temporal question answering and geographic information retrieval.

Location-based information contained in publicly available GIS databases is invaluable for many applications such as disaster response, national infrastructure protection, crime analysis, and numerous others. The information entities of such databases have both spatial and textual descriptions. Likewise, queries issued to the databases also contain spatial and textual components, for example, "Find shelters with emergency medical facilities in Orange County," or "Find earthquake-prone zones in Southern California." We refer to such queries as spatial-keyword queries or SK queries for short. In recent times, a lot of interest has been generated in efficient processing of SK queries for a variety of applications from Web-search to GIS decision support systems. We refer to systems built for enabling such applications as Geographic Information Retrieval (GIR) Systems. An example GIR system that we address in this paper is a search engine built on top of hundreds of thousands of publicly available GIS databases. Building a search engine over such large repositories is a challenge. One of the key aspects of such a search engine is the performance. In this paper, we propose a framework for GIR systems and focus on indexing strategies that can process SK queries efficiently. We show through experiments that our indexing strategies lead to significant improvement in efficiency of answering SK queries over existing techniques.

Local cultural heritage document collections are characterized by contents strongly attached to a territory and its land history. Our contribution aims at enhancing such a content retrieval process efficiently each time a query includes geographic criteria. We propose a unified model for a formal representation of geographic information. This geographic model allows spatial features to be described independently of their representation mode (textual, graphical) in the documents. We have developed a prototype implementing geographic information extraction (IE) and geographic information retrieval (IR) processes. We process geographic IE with semantic techniques combined to classic IE approaches. Then, we implemented geographic IR with intersections researching algorithms: these algorithms search for all geocoded entities in the documents collections indexes which intersect any entity in the user's request. This paper focuses on IR and visualization proposals relying on the geospatial characteristics of cultural heritage corpora.

This paper describes the participation of Columbus Project of Microsoft Research Asia (MSRA) in GeoCLEF 2006. We participated in the Monolingual GeoCLEF evaluation (EN-EN) and submitted five runs based on different methods. In this paper, we describe our geographic information retrieval system, discuss the results and draw following conclusions: 1) if we just extract locations from the topics automatically without any expansions as geo-terms, the retrieval performance is barely satisfactory; 2) automatic query expansion weakens the performance; 3) if the queries are expanded manually, the performance is significantly improved.

This paper describes our participation in the GeoCLEF monolingual English task of the Cross Language Evaluation Forum 2006. The main objective of this study is to evaluate the retrieve performance of our geographic information retrieval system. The system consists of four modules: the geographic knowledge base that provides information about important geographic entities around the world and relationships between them; the indexing module that creates and maintains textual and geographic indices for document collections; the document retrieval module that uses the Boolean model to retrieve documents that meet both textual and geographic criteria; and the ranking module that ranks retrieved results based on ranking functions learned using Genetic Programming. Experiments results show that the geographic knowledge base, the indexing module and the retrieval module are useful for geographic information retrieval tasks, but the proposed ranking function learning method doesn't work well.

For our participation in the second edition of GeoCLEF, we have researched the incorporation of geographic knowledge into Geographic Information Retrieval (GIR). Our system is made up of an IR module (IR-n) and a Geographic Knowledge module (Geonames). The results show that the addition of geographic knowledge has a negative impact on the precision. However, the fact that for some topics the obtained results are better, makes us conclude that the addition of this knowledge could be useful but further research is needed in order to determine how.

Geographic Information Retrieval (GIR) systems provide users with functionalities of representation, storage, organisation of and access to various types of electronic information resources based on their textual and geographic context. This thesis explores various aspects of the development, evaluation and application of GIR systems. The first study focuses upon the extraction and grounding of geographic information entities. My approach for this study consists of a hierarchical structure-based geographic relationship model that is used to describe connections between geographic information entities, and a supervised machine learning algorithm that is used to resolve ambiguities. The proposed approach has been evaluated on a toponym disambiguation task using a large collection of news articles. The second study details the development and validation of a GIR ranking mechanism. The proposed approach takes advantage of the power of the Genetic Programming (GP) paradigm with the aim of finding an optimal functional form that integrates both textual and geographic similarities between retrieved documents and a given user query. My approach has been validated by applying it to a large collection of geographic metadata documents. The third study addresses the problem of modelling the GIR retrieval process that takes into account both thematic and geographic criteria. Based on the Spreading Activation Network (SAN), the proposed model consists a two-layer associative network that is used to construct a structured search space; a constrained spreading activation algorithm that is used to retrieve and to rank relevant documents; and a geographic knowledge base that is used to provide necessary domain knowledge for network. The retrieval performance of my model has been evaluated using the GeoCLEF 2006 tasks. The fourth study discusses the publishing, browsing and navigation of geographic information on the World Wide Web. Key challenges in designing and implementing of a GIR user interface through which online content can be systematically organised based on their geospatial characteristics, and can be efficiently accessed and interrelated, are addressed. The effectiveness and the usefulness of the system are shown by applying it to a large collection of geo-tagged web pages.

For the GeoCLEF task of the CLEF campaign 2006, we investigate identifying literal (geographic) and metonymic senses of location names (the location name refers to another, related entity) and indexing them differently. In document preprocessing, location name senses are identified with a classifier relying on shallow features only. Different senses are stored in corresponding document fields, i. e. LOC (all senses), LOCLIT (literal senses), and LOCMET (metonymic senses). The classifier was trained on manually annotated data from German CoNLL- 2003 data and from a subset of the GeoCLEF newspaper corpus. The setup of our GIR (geographic information retrieval) system is a variant of our setup for GeoCLEF 2005. Results of the retrieval experiments indicate that excluding metonymic senses of location names (short: metonymic location names) improves mean average precision (MAP). Furthermore, using topic narratives decreases MAP, and query expansion with meronyms improves the performance of GIR in our experiments.

The two-and-a-half day workshop in Santa Barbara (December 2006) focused on the role of digital gazetteers in geo-referencing applications, starting with overviews of the state-of-the-art and current activities and leading to a consensus on the opportunities and directions for collaboration and the advancement of a research and practice agenda. The workshop was convened by the National Center for Geographic Information & Analysis (NCGIA) and the Redlands Institute, with sponsorship from the National Geospatial-Intelligence Agency.

The two-and-a-half day workshop (Dec 2006, in Santa Barbara CA) focused on the role of digital gazetteers in georeferencing applications, with overviews of the state-of-the-art and current activities and discussions on opportunities and directions for collaboration and the advancement of a research and practice agenda. The workshop was convened by the National Center for Geographic Information & Analysis (NCGIA) at the University of California at Santa Barbara and the Redlands Institute. It was sponsored by the National Geospatial-Intelligence Agency. This document contains the list of participants, submitted position papers, and the meeting agenda.

Local cultural heritage document collections are characterized by contents strongly attached to a territory and its history. Our contribution aims at enhancing such a content retrieval process efficiently each time a query includes geographic criteria. We propose a core model for a formal representation of geographic information. It takes into account the characteristics of different expression modes: written language and captures of drawings, maps, photographs, etc. We have developed a prototype fully implementing geographic information extraction (IE) and geographic information retrieval (IR) processes. We approach geographic IE from semantic processings additionally to classic IE approaches. This paper focuses on IR and information visualization (IV) proposals relying on the geospatial characteristics of documents.

The poster describes a fast, simple, yet accurate method to associate large amounts of web resources stored in a search engine database with geographic locations. The method uses location-by-IP data, domain names, and content-related features: ZIP and area codes. The novelty of the approach lies in building location-by-IP database by using continuous IP blocks method. Another contribution is domain name analysis. The method uses search engine infrastructure and makes it possible to effectively associate large amounts of search engine data with geography on a regular basis. Experiments ran on Yandex search engine index; evaluation has proved the efficacy of the approach. ACM Special Interest Group on Hypertext, Hypermedia, and Web

This paper presents the Virtual Itineraries in Pyrenees (PIV) project. Spatial and temporal unified models are proposed to give a formal representation to geographical information. The aim is to improve the access to local cultural and heritage document collections. The models take into account characteristics of heterogeneous human expression modes: written language and captures of drawings, maps, pictures, etc. Semantic treatments have been built to automatically manage spatial and temporal information from non-structured data. These treatments are added to classical information extraction (IE) approaches. Then, geographical information retrieval processing is based on geographical information systems (GIS) algorithms. These algorithms look for any relations between formal representations of geographic information in documents collections and similar representations in a user query. Finally we propose a prototype implementing such geographic IE and geographic Information Retrieval (IR).

Geographic Information Retrieval (GIR) is concerned with the retrieval of documents based on both thematic and geographic content. An important issue in GIR, as for all IR, is relevance. In this paper we argue that spatial relevance should be considered independently from thematic relevance, and propose an initial scheme. A pilot study to assess this relevance scheme is presented, with initial results suggesting that users can distinguish between these two relevance dimensions, and that furthermore they have different properties. We suggest that spatial relevance requires greater assessor effort and more localised geographic knowledge than judging thematic relevance.

In this paper we will describe the Berkeley (groups 1 and 2 combined) submissions and approaches to the GeoCLEF task for CLEF 2005. The two Berkeley groups used different systems and approaches for GeoCLEF with some common themes. For Berkeley group 1 (Larson) the main technique used was fusion of multiple probabilistic searches against different XML components using both Logistic Regression (LR) algorithms and a version of the Okapi BM-25 algorithm. The Berkeley group 2 (Gey and Petras) employed tested CLIR methods from previous CLEF evaluations using Logistic Regression with Blind Feedback. Both groups used multiple translations of queries in for cross-language searching, and the primary geographically-based approaches taken by both involved query expansion with additional place names. The Berkeley1 group used GIR indexing techniques to georeference proper nouns in the text using a gazetteer derived from the World Gazetteer (with both English and German names for each place), and automatically expanded place names in topics for regions or countries in the queries by the names of the countries or cities in those regions or countries. The Berkeley2 group used manual expansion of queries, adding additional place names.

This paper describes our work for the participation at the GeoCLEF task of CLEF 2005. We employ multilayered extended semantic networks for the representation of background knowledge, queries, and documents for geographic information retrieval (GIR). In our approach, geographic concepts from the query network are expanded with concepts which are semantically connected via topological, directional, and proximity relations. We started with an existing geographic knowledge base represented as a semantic network and expanded it with concepts automatically extracted from the GEOnet Names Server. Several experiments for GIR on German documents have been performed: a baseline corresponding to a traditional information retrieval approach; a variant expanding thematic, temporal, and geographic descriptors from the semantic network representation of the query; and an adaptation of a question answering (QA) algorithm based on semantic networks. The second experiment is based on a representation of the natural language description of a topic as a semantic network, which is achieved by a deep linguistic analysis. The semantic network is transformed into an intermediate representation of a database query explicitly representing thematic, temporal, and local restrictions. This experiment showed the best performance with respect to mean average precision: 10.53% using the topic title and description. The third experiment, adapting a QA algorithm, uses a modified version of the QA system InSicht. The system matches deep semantic representations of queries or their equivalent or similar variants to semantic networks for document sentences.

Similarity measurement theories play an increasing role in GIScience and especially in information retrieval and integration Existing feature and geometric models have proven useful in detecting close but not identical concepts and entities However, until now none of these theories are able to handle the expressivity of description logics for various reasons and therefore are not applicable to the kind of ontologies usually developed for geographic information systems or the upcoming geospatial semantic web To close the resulting gap between available similarity theories on the one side and existing ontologies on the other, this paper presents ongoing work to develop a context-aware similarity theory for concepts specified in expressive description logics such as $\mathcal ALCNR$.

An increasing amount of freely available Geographic Information System (GIS) data on the Internet has stimulated recent research into Geographic Information Retrieval (GIR). Typically, GIR looks at the problem of retrieving GIS datasets on a theme by theme basis. However in practice, themes are generally not analysed in isolation. More often than not multiple themes are required to create a map for a particular analysis task. To do this using the current GIR techniques, each theme is retrieved one by one using traditional retrieval methods and manually added to the map. To automate map creation the traditional GIR paradigm of matching a query to a single theme type must be extended to include discovering relationships between different theme types.Bayesian Inference networks can and have recently been adapted to provide a theme to theme relevance ranking scheme which can be used to automate map creation [2]. The use of Bayesian inference for GIR relies on a manually created Bayesian network. The Bayesian network contains causal probability relationships between spatial themes. The next step in using Bayesian Inference for GIR is to develop algorithms to automatically create a Bayesian network from historical data. This paper discusses a process to utilize conventional Bayesian learning algorithms in GIR. In addition, it proposes three spatial learning Bayesian network algorithms that incorporate spatial relationships between themes into the learning process. The resulting Bayesian networks were loaded into an inference engine that was used to retrieve all relevant themes given a test set of user queries. The performance of the spatial Bayesian learning algorithms were evaluated and compared to performance of conventional non-spatial Bayesian learning algorithms.This contribution will increase the performance and efficiency of knowledge extraction from GIS by allowing users to focus on interpreting data, instead of focusing on finding which data is relevant to their analysis.

This paper presents methods used to extract geospatial information from web pages for use in SPIRIT, a new Geographic Information Retrieval (GIR) system for the web. The resulting geospatial markup tools have been used to annotate around 900,000 web pages taken from a 1TB web crawl, focused on regions in the UK, France, Germany and Switzerland. This paper discusses a versatile geo-parsing tool for extracting spatial metadata based upon the GATE Information Extraction (IE) system, and a simple geo-coding program based on default sense to assign spatial coordinates to extracted locations. A preliminary analysis of markup accuracy for geo-parsing and geo-coding is provided, and an initial statistical and geographical analysis of the SPIRIT collection presented.

The processing steps required for geographic information retrieval include many steps that are common to all forms of information retrieval, e.g. stopword filtering, stemming, vocabulary enrichment, understanding Booleans, and fluff removal. Only a few steps, in particular the detection of geographic entities and the assignment of bounding boxes to these, are specific to geographic IR. The paper presents the results of experiments designed to evaluate the geography-specificity of the GeoCLEF 2005 task, and suggests some methods to increase the sensitivity of the evaluation.

Geographic Information Retrieval is concerned with retrieving documents that are related to some location. This paper addresses the ranking of documents by both textual and spatial relevance. To this end, we introduce distributed ranking, where similar documents are ranked spread in the list instead of consecutively. The effect of this is that documents close together in the ranked list have less redundant information. We present various ranking methods, efficient algorithms to implement them, and experiments to show the outcome of the methods.

We describe Web-a-Where, a system for associating geography with Web pages. Web-a-Where locates mentions of places and determines the place each name refers to. In addition, it assigns to each page a geographic focus --- a locality that the page discusses as a whole. The tagging process is simple and fast, aimed to be applied to large collections of Web pages and to facilitate a variety of location-based applications and data analyses.Geotagging involves arbitrating two types of ambiguities: geo/non-geo and geo/geo. A geo/non-geo ambiguity occurs when a place name also has a non-geographic meaning, such as a person name (e.g., Berlin) or a common word (Turkey). Geo/geo ambiguity arises when distinct places have the same name, as in London, England vs. London, Ontario.An implementation of the tagger within the framework of the WebFountain data mining system is described, and evaluated on several corpora of real Web pages. Precision of up to 82% on individual geotags is achieved. We also evaluate the relative contribution of various heuristics the tagger employs, and evaluate the focus-finding algorithm using a corpus pretagged with localities, showing that as many as 91% of the foci reported are correct up to the country level.

Information retrieval systems in operation today for applications ranging from Digital Libraries to Web Search make very little use of two major dimensions of the data being searched: location and time. For many applications these dimensions can provide an intuitive and understandable visualization of search constraints and results. However, current metadata representations of geographic characteristics and the uses of these metadata are problematic. Most retrieval and database, even those tailored to geographic information, provide only nascent approaches to the technologically and conceptually difficult challenges of Geographic Information Retrieval (GIR). Much of the problem is rooted in the geospatial metadata used by these systems to index and access geographic data. The primary issues are

This demo will presents results from an evaluation of algorithms forranking results by probability of relevance for Geographic Information Retrieval (GIR) applications. We will demonstrate an algorithm for GIR ranking based on logistic regression from samples of the test collection We also show the effects of different representations of the geographic regions being searched, including minimumbounding rectangles, convex hulls, and complex polygons.

Many Web pages are rich in geographic information and primarily relevant to geographically limited communities. However, existing IR systems only recently began to offer local services and largely ignore geo-spatial information. This paper presents our work on automatically identifying the geographical scope of Web documents, which provides the means to develop retrieval tools that take the geographical context into consideration. Our approach makes extensive use of an ontology of geographical concepts, and includes a system architecture for extracting geographic information from large collections of Web documents. The proposed method involves recognising geographical references over the documents and assigning geographical scopes through a graph ranking algorithm. Initial evaluation results are encouraging, indicating the viability of this approach.

This paper presents results from an evaluation of algorithms for ranking results by probability of relevance for Geographic Information Retrieval (GIR) applications. We review the work done on GIR and especially on ranking algorithms for GIR. We evaluate these algorithms using a test collection of 2500 metadata records from a geographic digital library. We present an algorithm for GIR ranking based on logistic regression from samples of the test collection. We also examine the effects of different representations of the geographic regions being searched, including minimum bounding rectangles, and convex hulls.

Place, a concept originating from human geography [1, 2], has recently become a hot topic in GIScience: place is important in human cognition and communication, and hence, is a high priority for human-computer interaction research. But place is also a challenging concept to model, reason with, and analyze in information systems, because of its fluency with context shifts, and its underspecification [3]. Thinking about knowledge acquisition methods---from geographic information retrieval from big data to dedicated user generated content---they seem to involve several disciplines such as computational linguistics, data mining, artificial intelligence, and geographic information science.

A multi-attribute decision model aiming at the selection of an efficient set of alternatives is presented. The major challenge is that the alternatives are not perfectly known, i.e., the associated values may be imprecise. Furthermore, for improved user friendliness, vague query conditions are allowed. Imprecise scores are described by probability distributions, vague query conditions by fuzzy predicates. The philosophy of the presented approach is to create an environment similar to the multidimensional spreadsheet front-end of data warehouses, in which the user can interactively discover the imperfect answers. In particular, numeric measures that allow an aggregated view of alternatives are defined. A geographic information retrieval scenario demonstrates the use of the decision model.

Users of spatial digital libraries normally select documents of interest based on both their geographical and topical relevancy in relation to specified queries. This paper proposes an integrated and flexible geographic information retrieval and browsing tool, GeoVIBE, which combines a geographical model and a vector space model to form document representations that are tightly linked. At the user interface, GeoVIBE consists of two types of browsing windows, GeoView and VibeView, which work in coordination for visual navigation in the document space. GeoView imposes a geographical order to the document space based on the idea of document "footprints" that may be cartographically visualized to reveal the spatial structure of the document space. VibeView is a visual presentation of document space defined by multiple reference points, similar to that of VIBE interface by Olson and colleagues (1993). By smooth integration of two browsing strategies, GeoVIBE enables users to search information using geographic clues as well as thematic clues.

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 coordinate-based 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.