3D geovisualisation techniques applied in spatial data mining

The increase in the number of spatial data collected has motivated the development of geovisualisation techniques, aiming to provide an important resource to support the extraction of knowledge and decision making. One of these techniques are 3D graphs, which provides a dynamic and flexible increase of the results analysis obtained by the spatial data mining algorithms, principally when there are incidences of georeferenced objects in a same local. This work presented as an original contribution the potentialisation of visual resources in a computational environment of spatial data mining and, afterwards, the efficiency of these techniques is demonstrated with the use of a real database. The application has shown to be very interesting in interpreting obtained results, such as patterns that occurred in a same locality and to provide support for activities which could be done as from the visualisation of results. © 2013 Springer-Verlag.

OntoSDM: an approach to improve quality on spatial data mining algorithms

The increase in new electronic devices had generated a considerable increase in obtaining spatial data information; hence these data are becoming more and more widely used. As well as for conventional data, spatial data need to be analyzed so interesting information can be retrieved from them. Therefore, data clustering techniques can be used to extract clusters of a set of spatial data. However, current approaches do not consider the implicit semantics that exist between a region and an object’s attributes. This paper presents an approach that enhances spatial data mining process, so they can use the semantic that exists within a region. A framework was developed, OntoSDM, which enables spatial data mining algorithms to communicate with ontologies in order to enhance the algorithm’s result. The experiments demonstrated a semantically improved result, generating more interesting clusters, therefore reducing manual analysis work of an expert.

Review of Applied Spatial Data Analysis with R by R. S. Bivand, E. J. Pebesma, and V. Gomez-Rubio

Most authors struggle to pick a title that adequately conveys all of the material covered in a book. When I first saw Applied Spatial Data Analysis with R, I expected a review of spatial statistical models and their applications in packages (libraries) from the CRAN site of R. The authors’ title is not misleading, but I was very pleasantly surprised by how deep the word “applied” is here. The first half of the book essentially covers how R handles spatial data. To some statisticians this may be boring. Do you want, or need, to know the difference between S3 and S4 classes, how spatial objects in R are organized, and how various methods work on the spatial objects? A few years ago I would have said “no,” especially to the “want” part. Just let me slap my EXCEL spreadsheet into R and run some spatial functions on it. Unfortunately, the world is not so simple, and ultimately we want to minimize effort to get all of our spatial analyses accomplished. The first half of this book certainly convinced me that some extra effort in organizing my data into certain spatial class structures makes the analysis easier and less subject to mistakes. I also admit that I found it very interesting and I learned a lot.

The SB-index and the HSB-index: efficient indices for spatial data warehouses

Spatial data warehouses (SDWs) allow for spatial analysis together with analytical multidimensional queries over huge volumes of data. The challenge is to retrieve data related to ad hoc spatial query windows according to spatial predicates, avoiding the high cost of joining large tables. Therefore, mechanisms to provide efficient query processing over SDWs are essential. In this paper, we propose two efficient indices for SDW: the SB-index and the HSB-index. The proposed indices share the following characteristics. They enable multidimensional queries with spatial predicate for SDW and also support predefined spatial hierarchies. Furthermore, they compute the spatial predicate and transform it into a conventional one, which can be evaluated together with other conventional predicates by accessing a star-join Bitmap index. While the SB-index has a sequential data structure, the HSB-index uses a hierarchical data structure to enable spatial objects clustering and a specialized buffer-pool to decrease the number of disk accesses. The advantages of the SB-index and the HSB-index over the DBMS resources for SDW indexing (i.e. star-join computation and materialized views) were investigated through performance tests, which issued roll-up operations extended with containment and intersection range queries. The performance results showed that improvements ranged from 68% up to 99% over both the star-join computation and the materialized view. Furthermore, the proposed indices proved to be very compact, adding only less than 1% to the storage requirements. Therefore, both the SB-index and the HSB-index are excellent choices for SDW indexing. Choosing between the SB-index and the HSB-index mainly depends on the query selectivity of spatial predicates. While low query selectivity benefits the HSB-index, the SB-index provides better performance for higher query selectivity.

Public Commons for Geospatial Data: A Conceptual Model

A wide variety of spatial data collection efforts are ongoing throughout local, state and federal agencies, private firms and non-profit organizations. Each effort is established for a different purpose but organizations and individuals often collect and maintain the same or similar information. The United States federal government has undertaken many initiatives such as the National Spatial Data Infrastructure, the National Map and Geospatial One-Stop to reduce duplicative spatial data collection and promote the coordinated use, sharing, and dissemination of spatial data nationwide. A key premise in most of these initiatives is that no national government will be able to gather and maintain more than a small percentage of the geographic data that users want and desire. Thus, national initiatives depend typically on the cooperation of those already gathering spatial data and those using GIs to meet specific needs to help construct and maintain these spatial data infrastructures and geo-libraries for their nations (Onsrud 2001). Some of the impediments to widespread spatial data sharing are well known from directly asking GIs data producers why they are not currently involved in creating datasets that are of common or compatible formats, documenting their datasets in a standardized metadata format or making their datasets more readily available to others through Data Clearinghouses or geo-libraries. The research described in this thesis addresses the impediments to wide-scale spatial data sharing faced by GIs data producers and explores a new conceptual data-sharing approach, the Public Commons for Geospatial Data, that supports user-friendly metadata creation, open access licenses, archival services and documentation of parent lineage of the contributors and value- adders of digital spatial data sets.

Showing the Past: Integrating Cartographic Collections through Virtual Map Rooms

Spatial Data Infrastructures have become a methodological and technological benchmark enabling distributed access to historical-cartographic archives. However, it is essential to offer enhanced virtual tools that imitate the current processes and methodologies that are carried out by librarians, historians and academics in the existing map libraries around the world. These virtual processes must be supported by a generic framework for managing, querying, and accessing distributed georeferenced resources and other content types such as scientific data or information. The authors have designed and developed support tools to provide enriched browsing, measurement and geometrical analysis capabilities, and dynamical querying methods, based on SDI foundations. The DIGMAP engine and the IBERCARTO collection enable access to georeferenced historical-cartographical archives. Based on lessons learned from the CartoVIRTUAL and DynCoopNet projects, a generic service architecture scheme is proposed. This way, it is possible to achieve the integration of virtual map rooms and SDI technologies bringing support to researchers within the historical and social domains.

A multinational SDI-based system to facilitate disaster risk management in the Andean Community

A useful strategy for improving disaster risk management is sharing spatial data across different technical organizations using shared information systems. However, the implementation of this type of system requires a large effort, so it is difficult to find fully implemented and sustainable information systems that facilitate sharing multinational spatial data about disasters, especially in developing countries. In this paper, we describe a pioneer system for sharing spatial information that we developed for the Andean Community. This system, called SIAPAD (Andean Information System for Disaster Prevention and Relief), integrates spatial information from 37 technical organizations in the Andean countries (Bolivia, Colombia, Ecuador, and Peru). SIAPAD was based on the concept of a thematic Spatial Data Infrastructure (SDI) and includes a web application, called GEORiesgo, which helps users to find relevant information with a knowledge-based system. In the paper, we describe the design and implementation of SIAPAD together with general conclusions and future directions which we learned as a result of this work.

The design and implementation of a repository for the management of spatial data integrity constraints

This paper reviews the key features of an environment to support domain users in spatial information system (SIS) development. It presents a full design and prototype implementation of a repository system for the storage and management of metadata, focusing on a subset of spatial data integrity constraint classes. The system is designed to support spatial system development and customization by users within the domain that the system will operate.

On aggregation issues in spatial data management

Large amounts of information can be overwhelming and costly to process, especially when transmitting data over a network. A typical modern Geographical Information System (GIS) brings all types of data together based on the geographic component of the data and provides simple point-and-click query capabilities as well as complex analysis tools. Querying a Geographical Information System, however, can be prohibitively expensive due to the large amounts of data which may need to be processed. Since the use of GIS technology has grown dramatically in the past few years, there is now a need more than ever, to provide users with the fastest and least expensive query capabilities, especially since an approximated 80 % of data stored in corporate databases has a geographical component. However, not every application requires the same, high quality data for its processing. In this paper we address the issues of reducing the cost and response time of GIS queries by preaggregating data by compromising the data accuracy and precision. We present computational issues in generation of multi-level resolutions of spatial data and show that the problem of finding the best approximation for the given region and a real value function on this region, under a predictable error, in general is "NP-complete.