Transductive Learning for Spatial Data Classification

Learning classifiers of spatial data presents several issues, such as the heterogeneity of spatial objects, the implicit definition of spatial relationships among objects, the spatial autocorrelation and the abundance of unlabelled data which potentially convey a large amount of information. The first three issues are due to the inherent structure of spatial units of analysis, which can be easily accommodated if a (multi-)relational data mining approach is considered. The fourth issue demands for the adoption of a transductive setting, which aims to make predictions for a given set of unlabelled data. Transduction is also motivated by the contiguity of the concept of positive autocorrelation, which typically affect spatial phenomena, with the smoothness assumption which characterize the transductive setting. In this work, we investigate a relational approach to spatial classification in a transductive setting. Computational solutions to the main difficulties met in this approach are presented. In particular, a relational upgrade of the nave Bayes classifier is proposed as discriminative model, an iterative algorithm is designed for the transductive classification of unlabelled data, and a distance measure between relational descriptions of spatial objects is defined in order to determine the k-nearest neighbors of each example in the dataset. Computational solutions have been tested on two real-world spatial datasets. The transformation of spatial data into a multi-relational representation and experimental results are reported and commented

A COMPREHENSIVE GEOSPATIAL KNOWLEDGE DISCOVERY FRAMEWORK FOR SPATIAL ASSOCIATION RULE MINING

Continuous advances in modern data collection techniques help spatial scientists gain access to massive and high-resolution spatial and spatio-temporal data. Thus there is an urgent need to develop effective and efficient methods seeking to find unknown and useful information embedded in big-data datasets of unprecedentedly large size (e.g., millions of observations), high dimensionality (e.g., hundreds of variables), and complexity (e.g., heterogeneous data sources, space–time dynamics, multivariate connections, explicit and implicit spatial relations and interactions). Responding to this line of development, this research focuses on the utilization of the association rule (AR) mining technique for a geospatial knowledge discovery process. Prior attempts have sidestepped the complexity of the spatial dependence structure embedded in the studied phenomenon. Thus, adopting association rule mining in spatial analysis is rather problematic. Interestingly, a very similar predicament afflicts spatial regression analysis with a spatial weight matrix that would be assigned a priori, without validation on the specific domain of application. Besides, a dependable geospatial knowledge discovery process necessitates algorithms supporting automatic and robust but accurate procedures for the evaluation of mined results. Surprisingly, this has received little attention in the context of spatial association rule mining. To remedy the existing deficiencies mentioned above, the foremost goal for this research is to construct a comprehensive geospatial knowledge discovery framework using spatial association rule mining for the detection of spatial patterns embedded in geospatial databases and to demonstrate its application within the domain of crime analysis. It is the first attempt at delivering a complete geo-spatial knowledge discovery framework using spatial association rule mining

Geographic information system (GIS) integration of geological, geochemical and geophysical data from the Aggeneys base metal province, South Africa

Geographic Information System (GIS) technology aids in storage, manipulation, processing, analysis and presentation of spatial data sets. GIS can effectively interrogate large multidisciplinary exploration data sets in the search for new mineral exploitation targets. A spatial database, the AGGeneys Exploration Database (AGGED), has been created, comprising exploration data gathered during two decades of exploration for base-metals in the Aggeneys area, Bushmanland, South Africa. AGGED includes data extracted from analog maps, as well as digital remotely sensed sources, stored in vector and raster data structures, respectively. Vector data includes field based observations such as the extent of outcropping geological units, litho- and chrono-stratigraphic data; structural data; laboratory data based on regional geochemical stream sediment and traverse sampling; cadastral data and known mineral occurrences. Raster data includes Landsat satellite TM imagery and airborne magnetic data. Spatial variation within single data maps are examined. Spatial correlation between three different data maps are facilitated using colour analysis of hue, saturation and value components in a perceptual colour model. Simultaneously combining lead and zinc data with Landsat TM and geophysical magnetic data spatially delineates four new "geoscience" anomalies in the area under investigation. Two distinctive anomalies occur on the farms Aroams and Aggeneys. The Aroams anomaly (GSAl) has not been previously recognised, whereas the Aggeneys anomaly (GSA2) has been located before. The two other "geoscience" anomalies, on the farm Haramoep (GSA3 and GSA4 ), are slightly less distinct. Overlaying fold axial trace patterns and anomalies on the farm Haramoep, indicate that F2 and F3 fold structures are closely associated with these two anomalies. The location of the Aroams anomaly occurs along the same east-west trend of the four known major ore-deposits viz. Big Syncline, Broken Hill, Black Mountain and Gamsberg. Extrapolating F2 and F3 fold patterns using magnetic data locates this Aroams anomaly along the F3 axial trace extending from Big Syncline through to Gamsberg. The elevated Pb-Zn geochemical anomaly and structural data associated with the Aroams anomaly makes it a promising future exploitation target. The AGGED database can be expanded both in geographic extent to include surrounding areas, and to allow for inclusion of future surveys. Analytical processing of data in AGGED can also be continued and expanded. GIS is a burgeoning field and developments in GIS technology will impact on the explorationist. Developments in object-oriented and knowledge-based database technologies, visualisation techniques and artificial intelligence, incorporated in future GIS need to be closely monitored and evaluated by geoscience explorationists

Geological and structural analysis of the Hwange area-Northwest Zimbabwe: using remotely sensed data and geographic information systems (GIS)

There is a continuous need to locate more targets for coal exploration and evaluation of geological structures in the north-west coalfields in Zimbabwe. Conventional methods of analysing geological structures and field mapping are being hindered by inaccessibility of some areas and thick covers of Recent sediments. Remote sensing has been found to be a valuable method of identifying lithologic units and geological structures in the· area. Integration of the remotely sensed data in a 2D GIS resulted in recognition of spatial relationships between lithologic units, geological structures , coal seams and vegetation patterns. The Hwange area constitutes the western part of the Mid-Zambezi Karoo basin. The area consist of a wide spectrum of rocks ranging from Precambrian gneisses, Proterozoic schists and granulites, Karoo sediments to Tertiary and Recent sands. The area has been affected by a number of faults and shears some of which post date the Karoo sediments. These faults are an expression of the major tectonic events associated with this area. Some of the faults have been attributed to the effects of the Zambezi Rift System. Fault zones in the area, such as the Deka, Entuba and Inyantue Zones have been recognised as part of this system and these divide the Lower Karoo rocks into different coalfields. To try and evaluate the outcrop patterns and geological structures in the Hwange area, all the available geological and structural data were captured in a spatial database. The diversity of data incorporated in the spatial database demanded the need for a structured database design approach. The Entity-Relationship model was used to conceptualise the geological data of the ' Hwange area This model was transformed into the Relational Model that formed the implementation model of the database. Landsat 5 TM data covering the area from the Zimbabwean winter (20 June 1984) path 172, row 73 were also analysed for the information required to locate Karoo rift faults and the distribution of lithologic units associated with coal. The use of directional filters in the E-W and NE-SW directions and vegetation reflection characteristics during the dry season (June 1984) proved very effective in mapping fractures in the Karoo rocks. Landsat TM image enhancement techniques such as principal components analysis, edge enhancement, decorrelation stretching, band ratios; and colour composites made following these techniques, allowed mapping of different lithological units and discrimination between Karoo rocks and the crystalline basement rocks. Lineament analysis defined E-W, ENE-WSW, NE-SW and NW-SE conjugate sets of lineaments. The first three sets are related to the regional fracture zones of the Zambezi rift system The Entuba fault zone was found to be associated with most of the fractures affecting the Hwange coalfields. These have a dominant NE-SW and ENE-WSW trend in the Western Areas, Wankie Concession, Chaba, Entuba and Sinamatella coalfields. The E-W trending fracture set is dominated by joint sets in the Karoo basalt covering the north-west portion of the Hwange Coalfields. These show no relationship with the linear features of the Zambezi Rift system The NW-SE trending lineaments are dominantly developed on tilted bedding planes in the Karoo rocks as well as a few sparse joints in the Karoo basalt. Overlaying enhanced Landsat TM images on mapped faults and lithology data in a GIS revealed a number of features along the Entuba zone which were not previously known. The south-western part of the Entuba inlier was shown to consist of a synformal fold plunging to the south and bound on both sides by strike slip faults. Several kinematic indicators such as displacement of sedimentary strata have shown that the Entuba fault displays right lateral strike-slip coupled with dipslip movement. Proximity analysis using borehole data (depth to top and bottom of a coal seam) showed that most of the lineaments in the area are normal faults which have caused considerable displacements of the main coal seam Comparison of seam depth across most of these faults within coalfields and from one field to another shows that local and regional variations in depths of the main seam is primarily a function of vertical displacements along the faults over and above variations in the morphology of the pre-Karoo floor. The Entuba field was found to have greatest vertical variations over very short distances across faults, with depths varying from 60m to 520m from west to east over distances of less than 500m This part of the field has been partly affected by extensive normal faults, some of which can be traced for more than 10km. In the Hwange area, the Karoo rocks have been down faulted into a rift margin which is in turn divided into smaller fault blocks by intra-rift faulting. The shape of the fault blocks are further controlled by the orientation of the post-Karoo faults which have also down faulted the main coal seam Exploration activity in the area should also seek to establish the locations of these faults to help further decipher variations in depths of coal seams

A 3d geoscience information system framework

Two-dimensional geographical information systems are extensively used in the geosciences to create and analyse maps. However, these systems are unable to represent the Earth's subsurface in three spatial dimensions. The objective of this thesis is to overcome this deficiency, to provide a general framework for a 3d geoscience information system (GIS), and to contribute to the public discussion about the development of an infrastructure for geological observation data, geomodels, and geoservices. Following the objective, the requirements for a 3d GIS are analysed. According to the requirements, new geologically sensible query functionality for geometrical, topological and geological properties has been developed and the integration of 3d geological modeling and data management system components in a generic framework has been accomplished. The 3d geoscience information system framework presented here is characterized by the following features: - Storage of geological observation data and geomodels in a XML-database server. According to a new data model, geological observation data can be referenced by a set of geomodels. - Functionality for querying observation data and 3d geomodels based on their 3d geometrical, topological, material, and geological properties were developed and implemented as plug-in for a 3d geomodeling user application. - For database queries, the standard XML query language has been extended with 3d spatial operators. The spatial database query operations are computed using a XML application server which has been developed for this specific purpose. This technology allows sophisticated 3d spatial and geological database queries. Using the developed methods, queries can be answered like: "Select all sandstone horizons which are intersected by the set of faults F". This request contains a topological and a geological material parameter. The combination of queries with other GIS methods, like visual and statistical analysis, allows geoscience investigations in a novel 3d GIS environment. More generally, a 3d GIS enables geologists to read and understand a 3d digital geomodel analogously as they read a conventional 2d geological map

Mining and Filtering Multi-level Spatial Association Rules with ARES

In spatial data mining, a common task is the discovery of spatial association rules from spatial databases. We propose a distributed system, named ARES that takes advantage of the use of a multi-relational approach to mine spatial association rules. It supports spatial database coupling and discovery of multi-level spatial association rules as a means for spatial data exploration. We also present some criteria to bias the search and to filter the discovered rules according to user's expectations. Finally, we show the applicability of our proposal to two different real world domains, namely, document image processing and geo-referenced analysis of census data