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A great deal of research on information extraction from textual datasets has been performed in specific data contexts, such as movie reviews, commercial product evaluations, campaign speeches, etc. In this paper, we raise the question on how appropriate these methods are for documents related to land-use planning. The kind of information sought concerns the stakeholders, sentiments, geographic information, and everything else related to the territory. However, it is extremely challenging to link sentiments to the three dimensions that constitute geographic information (location, time, and theme). After highlighting the limitations of existing proposals and discussing issues related to textual data, we present a method called OPILAND (OPinion mIning from LAND-use planning documents) designed to semi-automatically mine opinions related to named-entities in specialized contexts. Experiments are conducted on aThaulagoon dataset (France), and then applied on three datasets that are related to different areas in order to highlight the relevance and the broader applications of our proposal. [ABSTRACT FROM AUTHOR]

This article reports on the efforts to establish a research project on a geospatial search engine for the Latin American country of Honduras during the author's research stay at a local university. Honduras is an interesting example of the challenges for information and knowledge management in developing countries as it combines many of the issues that are otherwise encountered more isolated or with less impact. These include low Web coverage in a low-resource country with limited Web infrastructure, and generally, work in challenging circumstances. The specific focus on geospatial information uncovers further issues that need to be addressed, such as informal addressing schemes or landmark-oriented location references, broad or incorrect locations for places, or insufficient ground truth in databases. While the tangible results of the project stay behind the original goals, several interesting results were achieved, which are condensed here as an experience report. [ABSTRACT FROM AUTHOR]

The task of assigning geographic coordinates to textual resources plays an increasingly central role in geographic information retrieval. The ability to select those terms from a given collection that are most indicative of geographic location is of key importance in successfully addressing this task. However, this process of selecting spatially relevant terms is at present not well understood, and the majority of current systems are based on standard term selection techniques, such as $(\chi^2)$ or information gain, and thus fail to exploit the spatial nature of the domain. In this paper, we propose two classes of term selection techniques based on standard geostatistical methods. First, to implement the idea of spatial smoothing of term occurrences, we investigate the use of kernel density estimation (KDE) to model each term as a two-dimensional probability distribution over the surface of the Earth. The second class of term selection methods we consider is based on Ripley's K statistic, which measures the deviation of a point set from spatial homogeneity. We provide experimental results which compare these classes of methods against existing baseline techniques on the tasks of assigning coordinates to Flickr photos and to Wikipedia articles, revealing marked improvements in cases where only a relatively small number of terms can be selected. [ABSTRACT FROM AUTHOR]

In this paper, we review the current literature on geographic information retrieval based on place names. We focus on the positional uncertainties and the extent of vagueness frequently associated with place names in linguistic place descriptions and on the differences between common users’ perception and the way the geographic information services interpret place names. We argue that, despite some notable efforts from the scientific community, geographic information services still cannot unambiguously recognize and sufficiently perform spatial reasoning with place names used in linguistic expressions. We focus on three interrelated research areas: (1) the use of place names in gazetteers, (2) the use of formal models to reason with spatial relations and with the spatial extent of place names in linguistic place descriptions, and (3) Web-harvesting and crowd-sourcing techniques for identifying place names and their spatial extension from public and volunteer sources, such as social networks and photo-sharing sites. We identify some opportunities for synthesizing existing approaches that would expedite the process of intelligent communication about place names between services and users. We discuss the shortcomings of the current state of affairs in locating place names from place descriptions and identify new areas of importance for future research. [ABSTRACT FROM AUTHOR]

The location of the author of a social media message is not invariably the same as the location that the author writes about in the message. In applications that mine these messages for information such as tracking news, political events or responding to disasters, it is the geographic content of the message rather than the location of the author that is important. To this end, we present a method to geo-parse the short, informal messages known as microtext. Our preliminary investigation has shown that many microtext messages contain place references that are abbreviated, misspelled, or highly localized. These references are missed by standard geo-parsers. Our geo-parser is built to find such references. It uses Natural Language Processing methods to identify references to streets and addresses, buildings and urban spaces, and toponyms, and place acronyms and abbreviations. It combines heuristics, open-source Named Entity Recognition software, and machine learning techniques. Our primary data consisted of Twitter messages sent immediately following the February 2011 earthquake in Christchurch, New Zealand. The algorithm identified location in the data sample, Twitter messages, giving an F statistic of 0.85 for streets, 0.86 for buildings, 0.96 for toponyms, and 0.88 for place abbreviations, with a combined average F of 0.90 for identifying places. The same data run through a geo-parsing standard, Yahoo! Placemaker, yielded an F statistic of zero for streets and buildings (because Placemaker is designed to find neither streets nor buildings), and an F of 0.67 for toponyms. [ABSTRACT FROM AUTHOR]

The aim of this article is to provide a basis in evidence for (or against) the much-quoted assertion that 80% of all information is geospatially referenced. For this purpose, two approaches are presented that are intended to capture the portion of geospatially referenced information in user-generated content: a network approach and a cognitive approach. In the network approach, the German Wikipedia is used as a research corpus. It is considered a network with the articles being nodes and the links being edges.The Network Degree of Geospatial Reference(NDGR) is introduced as an indicator to measure the network approach. We define NDGR as the shortest path between any Wikipedia article and the closest article within the network that is labeled with coordinates in its headline. An analysis of the German Wikipedia employing this approach shows that 78% of all articles have a coordinate themselves or are directly linked to at least one article that has geospatial coordinates. The cognitive approach is manifested by thecategories of geospatial reference(CGR): direct, indirect, and non-geospatial reference. These are categories that may be distinguished and applied by humans. An empirical study including 380 participants was conducted. The results of both approaches are synthesized with the aim to (1) examine correlations between NDGR and the human conceptualization of geospatial reference and (2) to separate geospatial from non-geospatial information. From the results of this synthesis, it can be concluded that 56–59% of the articles within Wikipedia can be considered to be directly or indirectly geospatially referenced. The article thus describes a method to check the validity of the ‘80%-assertion’ for information corpora that can be modeled using graphs (e.g., the World Wide Web, the Semantic Web, and Wikipedia). For the corpus investigated here (Wikipedia), the ‘80%-assertion’ cannot be confirmed, but would need to be reformulated as a ‘60%-assertion’. [ABSTRACT FROM AUTHOR]

Abstract: It is a useful premise to assume that every document in a collection and every query issued to an information retrieval (IR) system are geography-dependent. If one can determine what area an article is about (i.e., its geographical scope), this information can be used to improve the accuracy with which people, places and organisations named in the article can be located. More importantly, geographical scopes of documents may be exploited to improve the performance of IR systems against geography-dependent user queries by tuning relevance ranking and query expansion strategies with scope metadata. We want to answer the following pertinent questions to ascertain the usefulness of geographical information in improving retrieval accuracy: (1) how far can geographical information in queries and documents improve retrieval accuracy of IR systems when answering geography-dependent queries; and, (2) how effectively can geographical information in queries and documents be utilised to improve the quality of relevance ranking in geographical IR domain. This paper outlines strategies to determine the geographical scope of documents, and describes methods to utilise scope information to improve the performance of toponym resolution, relevance ranking and query expansion. [Copyright &y& Elsevier]

Abstract: The topic of automatically assigning geographic coordinates to Web 2.0 resources based on their tags has recently gained considerable attention. However, the coordinates that are produced by automated techniques are necessarily variable, since not all resources are described by tags that are sufficiently descriptive. Thus there is a need for adaptive techniques that assign locations to photos at the right level of granularity, or, in some cases, even refrain from making any estimations regarding location at all. To this end, we consider the idea of training language models at different levels of granularity, and combining the evidence provided by these language models using Dempster and Shafer’s theory of evidence. We provide experimental results which clearly confirm that the increased spatial awareness that is thus gained allows us to make better informed decisions, and moreover increases the overall accuracy of the individual language models. [Copyright &y& Elsevier]

Abstract: Geographic information retrieval (GIR) is nowadays a hot research issue that involves the management of uncertainty and imprecision and the modeling of user preferences and context. Indexing the geographic content of documents implies dealing with the ambiguity, synonymy and homonymy of geographic names in texts. On the other side, the evaluation of queries specifying both content based conditions and spatial conditions on documents’ contents requires representing the vagueness and context dependency of spatial conditions and the personal user''s preferences. The spatial condition can be specified linguistically in the query through vague terms such as “close to the North East of Milan’’, whose semantic depends on the user''s context and perception of distance. Further, users may want to express queries in which the content condition and the spatial condition have a distinct preference and are combined with a distinct semantics. In this paper, we propose a geographic information retrieval model and a system implementing it that represents both the uncertainty in indexing the geographic documents’ content and the user''s context and preferences in evaluating flexible spatial queries. It extracts the geographic content from documents’ text by applying heuristic knowledge coded by bipolar rules which evaluate positive hints and negative hints for the recognition of geographic names in text. Thus, it represents the geographic content of documents by fuzzy footprints, i.e., distinct locations on the earth associated with the text with a distinct degree of significance. Finally, the system allows evaluating two types of queries flexibly combining the content based condition with the spatial condition. The spatial condition is interpreted as the soft constraint “close’’ on the user''s perceived distance between the documents’ footprint and query''s footprint. For each retrieved document, two relevance scores are computed with respect to the two query conditions that are flexibly combined to generate an overall ranked list of documents. The user can choose the semantic for the combination that can be either an asymmetric “and possibly’’ aggregation between the mandatory content condition and the optional spatial condition, or a compensative “average’’ aggregation, defined as a linear combination of the two conditions; further, a relative preference between the conditions can be specified to achieve personalization and effectiveness. A prototypal geographic information retrieval system, named Geo-Finder, based on this model is described, and its evaluations are discussed. [Copyright &y& Elsevier]

The volume of spatial information on the Web grows daily, both in the form of online maps and as references to places embedded in documents and pages. Considering the spatial information needs of users, it is often necessary to recognize, within a document's text, the places to which it refers. This article presents a next-generation gazetteer, a toponymic dictionary which expands from the traditional cataloguing of place names and includes geographic elements such as spatial relationships, concepts and terms related to places. As such, we call it an OntoGazetteer, i.e., a gazetteer which also records semantic connections among places. The ontological gazetteer provides factual and semantic support to solving several common problems in geographic information retrieval. This paper presents the OntoGazetteer and demonstrates its applicability to a place name disambiguation problem. Along with other problem solutions to which the OntoGazetteer can contribute, we present a case study on recognizing and disambiguating place names within news sources. [ABSTRACT FROM AUTHOR]

When users need to find something on the Web that is related to a place, chances are place names will be submitted along with some other keywords to a search engine. However, automatic recognition of geographic characteristics embedded in Web documents, which would allow for a better connection between documents and places, remains a difficult task. We propose an ontology-driven approach to facilitate the process of recognizing, extracting, and geocoding partial or complete references to places embedded in text. Our approach combines an extraction ontology with urban gazetteers and geocoding techniques. This ontology, called OnLocus, is used to guide the discovery of geospatial evidence from the contents of Web pages. We show that addresses and positioning expressions, along with fragments such as postal codes or telephone area codes, provide satisfactory support for local search applications, since they are able to determine approximations to the physical location of services and activities named within Web pages. Our experiments show the feasibility of performing automated address extraction and geocoding to identify locations associated to Web pages. Combining location identifiers with basic addresses improved the precision of extractions and reduced the number of false positive results. [ABSTRACT FROM AUTHOR]

Abstract: This paper reports on the development and application of strategies and tools for geographic information seeking and knowledge building that leverages unstructured text resources found on the web. Geographic knowledge building from unstructured web sources starts with web document foraging during which the quantity, scope and diversity of web-based information create incredible cognitive burdens on an analyst’s or researcher’s ability to judge information relevancy. Determining information relevancy is ultimately a process of sensemaking. In this paper, we present our research on visually supporting web document foraging and sensemaking. In particular, we present the Sense-of-Place (SensePlace) analytic environment. The scientific goal of SensePlace is to visually and computationally support analyst sensemaking with text artifacts that have potential place, time, and thematic relevance to an analytical problem through identification and visual highlighting of named entities (people, places, times, and organizations) in documents, automated inference to determine document relevance using stored knowledge, and a visual interface with coupled geographic map, timeline, and concept graph displays that are used to contextualize the contexts of potentially relevant documents. We present the results of a case study analysis using SensePlace to uncover potential population migration, geopolitical, and other infectious disease dynamics drivers for measles and other epidemics in Niger. Our analysis allowed us to demonstrate how our approach can support analysis of complex situations along (a) multi-scale geographic dimensions (i.e., vaccine coverage areas), (b) temporal dimensions (i.e., seasonal population movement and migrations), and (c) diverse thematic dimensions (effects of political upheaval, food security, transient movement, etc.). [Copyright &y& Elsevier]

The map as a tool for accessing data has become very popular in recent years, but a lot of data do not have the necessary spatial meta-data to allow for that. Some data such as photographs however have spatial information in their captions and if this could be extracted, then they could be made available via map-based interfaces. Towards this goal, we introduce a model and spatio-linguistic reasoner for interpreting the spatial information in image captions that is based upon quantitative data about spatial language use acquired directly from people. Spatial language is inherently vague, and both the model and reasoner have been designed to incorporate this vagueness at the quantitative level and not only qualitatively. [ABSTRACT FROM AUTHOR]

Abstract: This paper presents research on the use of virtual globes to support the development of disaster event situation awareness in humans via open source information analysis and visualization. The key technology used for this research is the Context Discovery Application (CDA), which is a geovisual analytic environment designed to integrate implicit geographic information with Google Earth™. A case study of humanitarian disaster management is used to demonstrate the unique abilities of the CDA and Google EarthTM to support situation awareness. The paper provides some of the first empirical evidence on the utility of the virtual globes to support situation awareness for disaster management using implicit geographic information. The evidence presented was derived from evaluations by disaster management practitioners at the United Nations (UN) ReliefWeb project, an extremely relevant, yet difficult group to access for conducting academic disaster management research. Finally, ideas for future research on developing virtual globe applications to support situation awareness are described. [ABSTRACT FROM AUTHOR]

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 two contributions to this research field. First, we propose a new index structure that combines an inverted index and a spatial index based on an ontology of geographic space. This structure improves the query capabilities of other proposals. Then, we describe the architecture of a system for geographic information retrieval that defines a workflow for the extraction of the geographic references in documents. The architecture also uses the index structure that we propose to solve pure spatial and textual queries as well as hybrid queries that combine both a textual and a spatial component. Furthermore, query expansion can be performed on geographic references because the index structure is based in an ontology. [ABSTRACT FROM AUTHOR]

Search engines for Digital Libraries allow users to retrieve documents according to their contents. They process documents without differentiating the manifold aspects of information. Spatial and temporal dimensions are particularly dismissed. These dimensions are, however, of great interest for users of search engines targeting either the Web or specialized Digital Libraries. Recent studies reported that nearly 20% queries convey spatial and temporal information in addition to topical information. These three dimensions were referred to as parts of 'geographic information.' In the literature, search engines handling those dimensions are called 'Geographic Information Retrieval (GIR) systems.' Although several initiatives for evaluating GIR systems were undertaken, none was concerned with evaluating these three dimensions altogether. In this article, we address this issue by designing an evaluation framework, usefulness of which is highlighted through a case study involving a test collection and a GIR system. This framework allowed the comparison of our GIR system to state-of-the-art topical approaches. We also performed experiments for measuring performance improvement stemming from each dimension or their combination. We show that combining the three dimensions yields improvement in effectiveness (+73.9%) over a common topical baseline. Moreover, rather than conveying redundancy, the three dimensions complement each other. [ABSTRACT FROM AUTHOR]

Many kinds of text documents (e.g. newspapers, reports and letters) provide a potential source of geo-referenced information that is often underutilized. In interaction design, the use of dynamic icons and animation plays an important role in creating a sense of interactivity and feedback with virtual worlds. This paper shall focus on developing an interaction design approach for improving information retrieval from text sources as well as their interpretation. To this end, the Gertrude Mapper prototype is presented as a particle system based on an interaction design approach developed by a designer and a cartographer. The resulting prototype may be contemplated in the context of computer art, but it also has a practical aim: to help historians enhance their intuition through the visual exploration of unstructured text in a set of business letters. These letters were obtained from Simón Ruiz's archive. The prototype is available at http://www.dyncoopnet.eu/gertrude [ABSTRACT FROM AUTHOR]

Geographic Information Retrieval (GIR) has emerged as a new and promising tool for representation, storage, organisation of and access to geographic information. One of the current issues in GIR research is ranking of retrieved documents by both textual and geographic similarity measures. This paper describes an approach that learns GIR ranking functions using Genetic Programming (GP) methods based on textual statistics and geographic properties derived from documents and user queries. Our proposed approach has been applied to a large collection of geographic metadata documents. The experimental results show that the ranking functions learned using our method achieved significant improvement over existing ranking mechanisms in retrieval performance. [ABSTRACT FROM AUTHOR]

Geo-historical context, or GHC, is a contextual setting based on the interconnectedness of phenomena, events, and place across multiple spatial and temporal scales. GHC allows for situations to be understood and reasoned with, often with aid of visual representations such as maps. This paper introduces a conceptual model of GHC that theoretically motivates a Geovisual Analytics application called the Context Discovery Application (CDA), which is also presented. The CDA is designed to aid in the production of geo-historical context by using computational processes to identify and extract potentially relevant context information from heterogeneous, implicit situation information. This information can then be explored through visual interfaces to help users explain and understand the information. A hypothetical humanitarian context analysis case study is used to show how the CDA can be applied to real world problems. [ABSTRACT FROM AUTHOR]

As background knowledge of geographic information retrieval (GIR), the gazetteers have their limitations. In this paper we propose to develop and implement a common sense geographic knowledge base (CSGKB) instead of the gazetteers. We define that CSGKB is concerned with the representation of geographic knowledge in human brain and the simulation of geographic reasoning in daily life. Traditional geographic information system (GIS) is based on the model of map with its data based on geographic coordinates and its computation based on geometry. However, CSGKB, which is made up of geographic features and relationships and is based on qualitative spatio-temporal reasoning, can be viewed as the direct model of geographic world. This paper also discusses the characters of CSGKB and presents its structure which is composed of knowledge base, inference engine, geographic ontology and learner. The applications using CSGKB include geographic information retrieval (GIR), natural language processing (NLP), named entity recognition (NER), Semantic Web, etc. At present, our work focuses on the design of geographic ontology and the implementation of the CSGKB knowledge base. In this paper we describe the CSGKB ontology structure, top ontology, geographic location ontology, spatial relationship ontology, and domain ontologies. Finally, we introduce the current state of implementation of CSGKB and give an outlook on our future researches. [ABSTRACT FROM AUTHOR]

This research compares the geographic information retrieval (GIR) performance of a set of logistic regression models with those of five non-probabilistic methods that compute a spatial similarity score for a query-document pair. All methods are applied to a test collection of queries and documents indexed spatially by two convex conservative geometric approximations: the minimum bounding box (MBB) and the convex hull. In the comparison, the tested logistic regression models outperform, in terms of standard information retrieval recall and precision measures, all of the non-probabilistic methods. The retrieval performance achieved by the logistic regression models on MBB approximations is similar to that achieved by the use of the non-probabilistic methods on convex hulls. Although these results are valid only for the test collection used in this study, they suggest that a logistic regression approach to GIR provides an alternative to the use of higher-quality geometric representations that are more difficult to obtain, implement, and process. Additionally, this research demonstrates the ability of a probabilistic approach to effectively incorporate information about geographic context in the spatial ranking process. [ABSTRACT FROM AUTHOR]

This paper describes the generation of a model capturing information on how placenames co-occur together. The advantages of the co-occurrence model over traditional gazetteers are discussed and the problem of placename disambiguation is presented as a case study. We begin by outlining the problem of ambiguous placenames. We demonstrate how analysis of Wikipedia can be used in the generation of a co-occurrence model. The accuracy of our model is compared to a handcrafted ground truth; then we evaluate alternative methods of applying this model to the disambiguation of placenames in free text (using the GeoCLEF evaluation forum). We conclude by showing how the inclusion of placenames in both the text and geographic parts of a query provides the maximum mean average precision and outline the benefits of a co-occurrence model as a data source for the wider field of geographic information retrieval (GIR). [ABSTRACT FROM AUTHOR]

Natural language processing (NLP) techniques, such as toponym detection and resolution, are an integral part of most geographic information retrieval (GIR) architectures. Without these components, synonym detection, ambiguity resolution and accurate toponym expansion would not be possible. However, there are many important factors affecting the success of an NLP approach to GIR, including toponym detection errors, toponym resolution errors and query overloading. The aim of this paper is to determine how severe these errors are in state-of-the-art systems, and to what extent they affect GIR performance. We show that a careful choice of weighting schemes in the IR engine can minimize the negative impact of these errors on GIR accuracy. We provide empirical evidence from the GeoCLEF 2005 and 2006 datasets to support our observations. [ABSTRACT FROM AUTHOR]

Metonymically used location names (toponyms) refer to other, related entities and thus possess a meaning different from their literal, geographic sense. Metonymic uses are to be treated differently to improve the performance of geographic information retrieval (GIR). Statistics on toponym senses show that 75.06% of all location names are used in their literal sense, 17.05% are used metonymically, and 7.89% have a mixed sense. This article presents a method for disambiguating location names in texts between literal and metonymic senses, based on shallow features. The evaluation of this method is two-fold. First, we use a memory-based learner (TiMBL) to train a classifier and determine standard evaluation measures such as F-score and accuracy. The classifier achieved an F-score of 0.842 and an accuracy of 0.846 for identifying toponym senses in a subset of the CoNLL (Conference on Natural Language Learning) data. Second, we perform retrieval experiments based on the GeoCLEF data (newspaper article corpus and queries) from 2005 and 2006. We compare searching location names in a database index containing both their literal and metonymic senses with searching in an index containing their literal senses only. Evaluation results indicate that removing metonymic senses from the index yields a higher mean average precision (MAP) for GIR. In total, we observed a significant gain in MAP: an increase from 0.0704 to 0.0715 MAP for the GeoCLEF 2005 data, and an increase from 0.1944 to 0.2100 MAP for the GeoCLEF 2006 data. [ABSTRACT FROM AUTHOR]

This paper describes research that aims to define the information needs of mobile individuals, to implement a mobile information system that can satisfy those needs, and finally to evaluate the performance of that system with end-users. First a review of the emerging discipline of geographic information retrieval (GIR) is presented as background to the more specific issue of mobile information retrieval. Following this, a user needs study is described evaluating the requirements of potential users of a mobile information system; the study finds that there is a strong geographic component to users' information needs. Next, four geographic post-query filters are described which attempt to represent the region of space associated with an individual's query made at some specific spatial location. These filters are spatial proximity (distance in space), temporal proximity (travel time), speed-heading prediction surfaces (likelihood of visiting locations) and visibility (locations that can be seen). Two of these filters — spatial proximity and speed-heading prediction surfaces — are implemented in a mobile information system and subsequently evaluated with users in an outdoor setting. The results of evaluation suggest that retrieved information to which post-query geographic filters have been applied is considered more relevant than unfiltered information, and that users find information sorted by spatial proximity to be more relevant than that sorted by a prediction surface of likely future locations. The paper closes with a discussion of the wider implications of these results for developers of mobile information systems and location-based services. [ABSTRACT FROM AUTHOR]

Abstract: 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. [Copyright &y& Elsevier]

The ever‐increasing number of spatial data sets accessible through spatial data clearinghouses continues to make geographic information retrieval and spatial data discovery major challenges. Such challenges have been addressed in the discipline of Information Retrieval through ranking of data according to inferred degrees of relevance. Spatial data, however, present an additional challenge as they are characteristically made up of geometry, attribute and, optionally, temporal components. As these components are mutually independent of one another, this paper suggests that they be ranked independently of one another. The representation of the results of the independent ranking of these three components of spatial data suggests that representation of the results of the ranking process requires an alternative approach to currently used textual ranked lists: visualisation of relevance in a three‐dimensional visualisation environment. To illustrate the possible application of such an approach, a prototype browser is presented. [ABSTRACT FROM AUTHOR]

Geographic Information Retrieval is concerned with retrieving documents in response to a spatially related query. This paper addresses the ranking of documents by both textual and spatial relevance. To this end, we introducemulti-dimensional scattered ranking, where textually and spatially 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 of this type, efficient algorithms to implement them, and experiments to show the outcome of the methods. [ABSTRACT FROM AUTHOR]