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67
STING: A statistical information grid approach to spatial data mining
, 1997
"... Spatial data mining, i.e., discovery of interesting characteristics and patterns that may implicitly exist in spatial databases, is a challenging task due to the huge amounts of spatial data and to the new conceptual nature of the problems which must account for spatial distance. Clustering and regi ..."
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Cited by 290 (10 self)
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Spatial data mining, i.e., discovery of interesting characteristics and patterns that may implicitly exist in spatial databases, is a challenging task due to the huge amounts of spatial data and to the new conceptual nature of the problems which must account for spatial distance. Clustering and region oriented queries are common problems in this domain. Several approaches have been presented in recent years, all of which require at least one scan of all individual objects (points). Consequently, the computational complexity is at least linearly proportional to the number of objects to answer each query. In this paper, we propose a hierarchical statistical information grid based approach for spatial data mining to reduce the cost further. The idea is to capture statistical information associated with spatial cells in such a manner that whole classes of queries and clustering problems can be answered without recourse to the individual objects. In theory, and confirmed by empirical studies, this approach outperforms the best previous method by at least an order of magnitude, especially when the data set is very large.
Discovering Spatial Co-location Patterns: A Summary of Results
- Lecture Notes in Computer Science
, 2001
"... Given a collection of boolean spatial features, the co-location pattern discovery process finds the subsets of features frequently located together. For example, the analysis of an ecology dataset may reveal the frequent co-location of a fire ignition source feature with a needle vegetation type fea ..."
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Cited by 74 (8 self)
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Given a collection of boolean spatial features, the co-location pattern discovery process finds the subsets of features frequently located together. For example, the analysis of an ecology dataset may reveal the frequent co-location of a fire ignition source feature with a needle vegetation type feature and a drought feature. The spatial co-location rule problem is different from the association rule problem. Even though boolean spatial feature types (also called spatial events) may correspond to items in association rules over market-basket datasets, there is no natural notion of transactions. This creates difficulty in using traditional measures (e.g. support, confidence) and applying association rule mining algorithms which use support based pruning. We propose a notion of user-specified neighborhoods in place of transactions to specify groups of items. New interest measures for spatial co-location patterns are proposed which are robust in the face of potentially infinite overlapping neighborhoods. We also propose an algorithm to mine frequent spatial co-location patterns and analyze its correctness, and completeness. We plan to carry out experimental evaluations and performance tuning in the near future.
Spatial Contextual Classification and Prediction Models for Mining Geospatial Data
- IEEE Transactions on Multimedia
, 2002
"... Modeling spatial context (e.g., autocorrelation) is a key challenge in classification problems that arise in geospatial domains. Markov Random Fields (MRFs) is a popular model for in-corporating spatial context into image segmentation and land-use classification problems. The spatial autoregression ..."
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Cited by 52 (16 self)
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Modeling spatial context (e.g., autocorrelation) is a key challenge in classification problems that arise in geospatial domains. Markov Random Fields (MRFs) is a popular model for in-corporating spatial context into image segmentation and land-use classification problems. The spatial autoregression model (SAR) which is an extension of the classical regression model for incorporating spatial dependence, is popular for prediction and classification of spatial data in regional economics, natural resources, and ecological studies. There is little literature comparing these alternative approaches to facilitate the exchange of ideas (e.g., solution procedures). We argue that the SAR model makes more restrictive assumptions about the distribution of feature values and class boundaries than MRF. The relationship between SAR and MRF is analogous to the relationship between regression and Bayesian classifiers. This paper provides comparisons between the two models using a probabilistic and an experimental framework.
MiddleWhere: a middleware for location awareness in ubiquitous computing applications", The 5th
- ACM Middleware
"... Abstract. Location awareness significantly enhances the functionality of ubiquitous computing services and applications, and enriches the way they interact with users and resources in the environment. Many different alternative or complementary location sensing technologies are available. However, ..."
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Cited by 36 (2 self)
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Abstract. Location awareness significantly enhances the functionality of ubiquitous computing services and applications, and enriches the way they interact with users and resources in the environment. Many different alternative or complementary location sensing technologies are available. However, these technologies give location information in different formats and with different resolution and confidence. In this paper we introduce "MiddleWhere" a distributed middleware infrastructure for location that separates applications from location detection technologies. MiddleWhere enables the fusion of different location sensing technologies and facilitates the incorporation of additional location technologies on the fly as they become available. MiddleWhere utilizes probabilistic reasoning techniques to resolve conflicts and deduce the location of people given different sensor data. Besides, it allows applications to determine various kinds of spatial relationships between mobile objects and their environment, which is key in enabling a strong coupling between the physical and virtual world, as emphasized by ubiquitous computing. We have integrated MiddleWhere with our ubiquitous computing infrastructure, and have verified its flexibility and usefulness by incorporating various location sensing technologies and building a number of location-sensitive applications on top of it.
Modeling Spatial Dependencies for Mining Geospatial Data: An Introduction
- Geographic data mining and Knowledge Discovery (GKD
, 2000
"... 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 ..."
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Cited by 33 (8 self)
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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.
Mining association rules in spatio-temporal data
- in: Proceedings of the Seventh International Conference on GeoComputation
, 2003
"... This research demonstrates the application of association rule mining to spatiotemporal data. Association rule mining seeks to discover associations among transactions encoded in a database. An association rule takes the form A? B where A (the antecedent) and B (the consequent) are sets of predicate ..."
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Cited by 30 (0 self)
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This research demonstrates the application of association rule mining to spatiotemporal data. Association rule mining seeks to discover associations among transactions encoded in a database. An association rule takes the form A? B where A (the antecedent) and B (the consequent) are sets of predicates. A spatiotemporal association rule occurs when there is a spatio-temporal relationship in the antecedent or consequent of the rule. As a case study, association rule mining is used to explore the spatial and temporal relationships among a set of variables that characterize socioeconomic and land cover change in the Denver, Colorado, U.S.A. region from 1970 – 1990. Geographic Information Systems (GIS)-based data pre-processing is used to integrate diverse data sets, extract spatio-temporal relationships, classify numeric data into ordinal categories, and encode spatiotemporal relationship data in tabular format for use by conventional (non-spatiotemporal) association rule mining software. Multiple level association rule mining is supported by the development of a hierarchical classification scheme (concept hierarchy) for each variable. Further research in spatio-temporal association rule mining should address issues of data integration, data classification, the representation and calculation of spatial relationships, and strategies for finding ‘interesting ’ rules. 1.
Mining co-location patterns with rare events from spatial data sets
- Geoinformatica
"... Abstract A co-location pattern is a group of spatial features/events that are frequently co-located in the same region. For example, human cases of West Nile Virus often occur in regions with poor mosquito control and the presence of birds. For colocation pattern mining, previous studies often empha ..."
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Cited by 24 (2 self)
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Abstract A co-location pattern is a group of spatial features/events that are frequently co-located in the same region. For example, human cases of West Nile Virus often occur in regions with poor mosquito control and the presence of birds. For colocation pattern mining, previous studies often emphasize the equal participation of every spatial feature. As a result, interesting patterns involving events with substantially different frequency cannot be captured. In this paper, we address the problem of mining co-location patterns with rare spatial features. Specifically, we first propose a new measure called the maximal participation ratio (maxPR) and show that a co-location pattern with a relatively high maxPR value corresponds to a colocation pattern containing rare spatial events. Furthermore, we identify a weak monotonicity property of the maxPR measure. This property can help to develop an efficient algorithm to mine patterns with high maxPR values. As demonstrated
Discovering associations between spatial objects: An ilp application
- In Proceedings of the 11th International Conference on Inductive Logic Programming, volume 2157 of LNCS
, 2001
"... Abstract. In recent times, there is a growing interest in both the extension of data mining methods and techniques to spatial databases and the application of inductive logic programming (ILP) to knowledge discovery in databases (KDD). In this paper, an ILP application to association rule mining in ..."
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Cited by 21 (4 self)
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Abstract. In recent times, there is a growing interest in both the extension of data mining methods and techniques to spatial databases and the application of inductive logic programming (ILP) to knowledge discovery in databases (KDD). In this paper, an ILP application to association rule mining in spatial databases is presented. The discovery method has been implemented into the ILP system SPADA, which benefits from the available prior knowledge on the spatial domain, systematically explores the hierarchical structure of taskrelevant geographic layers and deals with numerical aspatial properties of spatial objects. It operates on a deductive relational database set up by selecting and transforming data stored in the underlying spatial database. Preliminary experimental results have been obtained by running SPADA on geo-referenced census data of Manchester Stockport, UK. 1