Spatial data mining is a process to discover interesting, potentially useful and high utility patterns embedded in spatial databases. Efficient tools for extracting information from spatial data sets can be of importance to organizations which own, generate and manage large spatial data sets. The current approach towards solving spatial data mining problems is to use classical data mining tools after "materializing" spatial relationships. However, the key property of spatial data is that of spatial autocorrelation. Like temporal data, spatial data values are influenced by values in their immediate vicinity. Ignoring spatial autocorrelation in the modeling process leads to results which are a poor-fit and unreliable. In this chapter we will first review spatial statistical techniques which explictly model spatial autocorrelation. Second, we will propose PLUMS(Predicting Locations Using Map Similarity), a new approach for supervised spatial data mining problems. PLUMS searches the space of solutions using a map-similarity measure which is more appropriate in the context of spatial data. We will show that compared to state-of-the-art spatial statistics approaches, PLUMS achives comparable accuracy but at a fraction of the computational cost. Furthermore, PLUMS provides a general framework for specializing other data mining techniques for mining spatial data.

Humans think and talk about regions and spatial relations imprecisely, in terms of vague concepts that are fuzzy or probabilistic (e.g., downtown, near). The functionality of geographic information systems will be increased if they can interpret vague queries. We discuss traditional and newer approaches to defining and modeling spatial queries. Most of the research on vague concepts in information systems has focussed on mathematical and computational implementation. To complement this, we discuss behavioral-science methods for determining the referents of vague spatial terms, particularly vague regions. We present a study of the empirical determination of downtown Santa Barbara. We conclude with a discussion of prospects and problems for integrating vague concepts into geographic information systems.

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All aspects of human activity are rooted in geographic space in some respect. As a consequence, many web resources include references to geographic context. In order to assist in recognising spatial terms employed in a query, it is proposed to use a geographical ontology. A geo-ontology play a key role in the development of spatially-aware search engine, with regards to providing support for query disambiguation, query expansion, relevance ranking and web resource annotation. This paper describes the geo-ontology designed for the SPIRIT system, before focussing on the problem of integrating multiple datasets for constructing such an ontology. Similarity checking of datasets is an essential step in the process of integration. The validity and effect of the different measures are studied by building a prototype geo-ontology utilising different datasets. The experimental results obtained confirmed the effect of quality of the datasets and the importance of the flexibility of the technique proposed for adjusting the similarity measures to handle such an effect.

An iconic image database is a collection of symbolic images where each image is a collection of labeled point features called icons. A method is presented to support fast positionindependent similarity search in an iconic database for symbolic images where the similarity condition involves finding icon pairs that satisfy a specific spatial relationship. This is achieved by introducing an index data structure based on r-θ space, which corresponds to the Cartesian product of separation (i.e., inter-icon distance) and (some representation of) relative spatial orientation. In this space, each pairing of two icons is represented by a single point, and all pairs with the same separation and relative orientation (regardless of absolute position) map to the same point. Similarly, all icon pairs with the same separation but different relative orientations map to points on a line parallel to the θ axis, while all pairs with different separations but the same relative orientation map to points on a line parallel to the r axis. Using such an index, database search for icon pairs with a given spatial relationship or range is accomplished by examining the subarea of the index space into which desired pairs would map. This r-θ index space can be organized using well-known spatial database techniques, such as quadtrees or R-trees. Although the size of such an index grows only linearly with respect to the number of images in the collection, it grows quadratically with the average number of icons in an image. A scheme is described to reduce the size of the index by pruning away a subset of the pairs, at the cost of incurring additional work when searching the database. This pruning is governed by a parameter φ, whose variation provides a continuous range of trade-offs between index size and search time.

A common approach in database queries involves the multidimensional representation of objects by a set of features. These features are compared to the query representation and then combined together to produce a total similarity metric. In this paper we introduce a novel technique for similarity learning within features (attributes) by manipulating fuzzy membership functions (FMFs) of different complexity. Our approach is based on a gradual complexity increase adaptable to problem requirements. The underlying idea is that less adaptable functions will act as approximations for more complex ones.

. In some spatial applications the objects of interest are fields, caused by spatially distributed sources, and one of the central questions is to find neighborhood relations between these fields. The motivating example for this paper is a cellular network: base transceiver stations transmit signals with continuous distribution, the signal strength, in an urban environme nt. In order to avoid interference, neighbored transceivers must not use the same frequency, so that neighborhood knowledge is one key to frequency planning. In this paper we define a concept of neighborhood for fields, and we propose a vector-based model to determine neighborhoods between given fields. In contrast to this vector-based model, the commonly used raster-based models suffer in urban areas from their resolution as well as from the prediction of signal propagation. 1 Introduction In this paper we focus on how to define and to determine neighborhood relations between fields. We deal with fields of phenomena ...

In this paper, we present a database model and a prototype system which provide a general format for multimedia presentations with spatio-temporal aspects. This representation is independent of any description language and media type and can be used to interpret and to integrate heterogeneous multimedia data coming from different distributed sources. We put a special emphasis on spatial aspects and we provide both qualitative and quantitative relationships to compose but also to query multimedia presentations. The originality of our approach is that presentations can be specified, stored as database objects, queried and executed. We extended an Object-Oriented DBMS (the O 2 system) with classes, methods and modules for implementing our approach. Such extensions allow also to build presentations from existing ones and to specify spatio-temporal characteristics for query results. R'esum'e Dans ce document, nous pr'esentons un environnement de gestion de pr'esentations multi...

Database search for images containing icons with specific mutual spatial relationships can be facilitated by an appropriately structured index. For the case of images containing subsets each of which consist of three icons, the oneto-one correspondence between (distinct) point triples and triangles allows the use of such triangle attributes as position, size, orientation, and “shape ” in constructing a pointbased index, in which each triangle maps to a single point in a resulting hyperdimensional index space. Size (based on the triangle perimeter) can be represented by a single linear dimension. The abstract “shape ” of a triangle induces a space that is inherently two-dimensional, and a number of alternative definitions of a basis for this space are examined. Within a plane, orientation reduces to rotation, and (after assignment of a reference direction for the triangle) can be represented by a single, spatially closed dimension. However, assignment of a reference direction for triangles possessing a k-fold rotational symmetry presents a significant challenge. Methods are described for characterizing shape and orientation of triangles, and for mapping these attributes onto a set of linear axes to form a combined index. The shape attribute is independent of size, orientation, and position, and the characterization of shape and orientation is stable with respect to small variations in the indexed triangles. 1

This paper presents Gaia's interface and match mechanism. Techniques for combining query results to retain the strengths of structured text-based queries are also considered