Management methods of spatial data based on PostGIS

Microsoft SQL Server 2008 introduces new geography and geometry spatial datatypes that enable the storage of structured data describing the shape and position of objects in space. This is an interesting and exciting new feature, with many potentially useful applications. Beginning Spatial with SQL Server 2008 covers everything you need to know to begin using these new spatial datatypes, and explains how to apply them in practical situations involving the spatial relationships of people, places, and things on the earth. All of the spatial concepts introduced are explained from the ground up, so you need not have any previous knowledge of working with spatial data. Every section is illustrated with code examples that you can use directly in SQL Server. All of the topics covered in this book apply to all versions of SQL Server 2008, including the freely available SQL Server 2008 Express. What youll learn Understand the fundamental concepts involved in working with spatial data, including spatial references and coordinate systems. Apply these concepts in the collection and storage of spatial data in SQL Server 2008, using the new geometry and geography field types. Create different types of spatial data objectspoints, lines, and polygonsand use these to describe realworld objects. Learn how to analyze spatial data using a range of supported methods, and be aware of a number of different practical applications for these methods. Be shown how to integrate SQL Server with other tools, such as Microsoft Virtual Earth, to display a visual representation of spatial data. Know how to ensure the performance of spatially enabled databases by creating appropriate spatial indexes. Who is this book for? SQL Server developers who wish to use spatial data in Microsoft SQL Server 2008. About the Apress Beginning Series The Beginning series from Apress is the right choice to get the information you need to land that crucial entrylevel job. These books will teach you a standard and important technology from the ground up because they are explicitly designed to take you from novice to professional. Youll start your journey by seeing what you need to knowbut without needless theory and filler. Youll build your skill set by learning how to put together realworld projects step by step. So whether your goal is your next career challenge or a new learning opportunity, the Beginning series from Apress will take you thereit is your trusted guide through unfamiliar territory!

3D spatial data acquired from aerial and remote sensing images by photogrammetric techniques is one of the most accurate and economic data sources for GIS, map production, and spatial data updating. However, there are still many problems concerning storage, structuring and appropriate management of spatial data obtained using these techniques. According to the capabilities of spatial database management systems (SDBMSs); direct integration of photogrammetric and spatial database management systems can save time and cost of producing and updating digital maps. This integration is accomplished by replacing digital maps with a single spatial database. Applying spatial databases overcomes the problem of managing spatial and attributes data in a coupled approach. This management approach is one of the main problems in GISs for using map products of photogrammetric workstations. Also by the means of these integrated systems, providing structured spatial data, based on OGC (Open GIS Consortium) standards and topological relations between different feature classes, is possible at the time of feature digitizing process. In this paper, the integration of photogrammetric systems and SDBMSs is evaluated. Then, different levels of integration are described. Finally design, implementation and test of a software package called Integrated Photogrammetric and Oracle Spatial Systems (IPOSS) is presented.

Spatial big data architecture: From Data Warehouses and Data Lakes to the LakeHouse

Abstract. The storage of spatial data that consists of spatial and non-spatial properties requires a database management system that possesses spatial functions that can cater to the spatial characteristics of data. These characteristics include the geometrical shape, topological and positional information. Parallel to how geometries describe the shape of an object, topological information is also an important spatial property which describes how the geometries in a space are related to each other. This information describes the connectivity, containment and adjacencies of spatial objects which are the foundation for more complex analysis such as navigation, data reconstruction, spatial queries and others. However, the topological support provided by spatial databases varies. This paper provided an overview on the current implementations of topological support in spatial databases such as ArcGIS, QGIS, PostgreSQL and others. The native topology in most spatial databases was found to be 2D topology maintained by 2D topology rules with limited representation of 3D topological relationships. Consequently, 3D objects represented by 2D topology had to be decomposed into objects of lower dimensions. Approaches to implement additional topological support for spatial databases included the use of topological data models, data structures, operators, and rules. 3D applications such as 3D cadastre required more detailed representations of topological information which required a more comprehensive 3D topological data model. Nonetheless, comprehensive preservation of topological information also mandates voluminous storage and higher computational efficiency. Thus, the appropriate 3D topological support should be provided in spatial databases to accurately represent 3D objects and meet 3D analysis requirements.

This paper describes how the semantic binary database model [Rishe-92-DDS] and its implementation [Rishe-91-FS] are extended to represent spatial data: functions over multi-dimensional space, e.g. the ocean temperature as a function of the four-dimensional space-time. The implementation, utilizing extended linear quad-trees, is compact in storage and allows efficient resolution of queries.

Spatial data is data or information that contains a geographical component. Its representation is considered to be increasingly important in decision-making and it is therefore necessary to guarantee access to spatial data in an easy, intuitive and fast way. The main problems of Geographic Information Systems (GIS) are the large amount of information and performance of relational databases (RDBMS) in storage and in unstructured spatial data queries. This problem is significant, since the efficiency of accessing large amounts of spatial data over the Web today is important for conducting on-demand and real-time queries. This work presents a solution to improve efficiency in accessing large amounts of spatial data through the Web, by standardizing spatial data formats. This proposal, called Web GIS, uses the NoSQL MongoDB database engine and provides the spatial data in GeoJSON via a REST API. The results show that MongoDB has substantial advantages over relational databases in storing and querying large amounts of unstructured spatial data, since it supports the storage of data with different structures and also by the efficiency of its spatial operators.

Our study focuses on the task of land use evolution in urban environment which is fundamental in revealing the territorial planning. It refers crucially to the use of spatial data mining tools due to their high potential in handling with spatial data characteristics. The results of our knowledge discovery process are spatial and spatiotemporal association rules referring to the land use and its evolution. Three proposals based on different knowledge extraction techniques are detailed. The first approach aims to extract spatiotemporal association rules by introducing time into the attributes. The second approach forecasts the extracted rules at different dates. The third approach is devoted to the mining of spatiotemporal association rules. This proposal looks for rules that relate properties of reference objects with properties of other spatial relevant objects. The extracted patterns are relationships involving the spatial objects during time periods. To prove the applicability of each approach, experimentations are conducted on real world data. The obtained results are promising.

The impetus for the development of GIS technologies is derived primarily from the fact that traditional manual techniques for the derivation, organization, storage and use of spatial data are no longer sufficient to meet the demands and expectations placed on such activities. The GIS technology is a service technology used to assist spatial data management, evaluations and analyses. There are three basic approaches currently developed that organize spatial data in a format such that the computer can perform previously time-consuming analytical and display processes. These are: (1)the rectangular grid approach; (2)the fine or coarse polygon approach; and (3)the triangulated irregular network approach. There is a wide range of applications that can be supported by GIS. These can be reasonably divided into two groups: those that depend on qualitative analysis and those that depend on quantitative analysis. The GIS design requirements are much more demanding to support quantitative analyses.

In this paper we present a Spatial Data Warehouse system that we use for aggregation and analysis of huge amounts of spatial data. The data is generated by utilities meters communicating via radio. In order to provide sufficient efficiency for our system we propose data and workload distribution as well as advanced indexing techniques. The system is based on a cascaded star model, which is a spatial development of a standard star schema and contains interconnected and often nested star schemas. The cascaded star allows efficient storage and analysis of spatial data, whose range extends from meter measurement values to weather information. The indexing tree structure and operation is tightly integrated with the spatial character of the data. Thanks to an available memory evaluating mechanism the system is very flexible in the field of aggregates accuracy. We also implemented indexing structure updating mechanism. The system is written in Java; for the data base we use Oracle 9i. Basing on the wide variety of tests results, we prove that a distributed system significantly surpasses the centralized version in terms of efficiency. We also show that a selective materialization of indexing structure fragments strongly increases system efficiency.

This paper presents a cadastral spatial data storage structure based on relational database, the method and the procedure to realize it. The paper consists of three parts. In the first part, some existing problems in some developed cadastral management systems are discussed. These problems are the following four. 1) The security of cadastral spatial data is difficult to be assured. 2) It is difficult to varify cadastral data and the integrality of cadastral data is difficult to be kept. 3) To transmit and share cadastral data is difficult. 4) The efficiency of data access is low. In the second part, the feasibility of using relational database to store spatial data is analyzed and a new cadastral spatial data storage structure is presented. At the same time, the related table structures and field descriptions are given, and then the merits and demerits of this storage structure are analyzed in detail. In the last part, through a real example, the detailed methods to make the new storage structure a reality are given. Moreover, some involving key techniques of the new storage structure are discussed. These techniques are: 1) the application of database transaction, 2) the application of database trigger, 3) and the application of secure recovery of database.

Extraction of interesting knowledge from large spatial databases is an important task in the development of spatial database systems. Spatial data mining is the branch of data mining that deals with spatial (location) data. Analyzing the huge amount (usually tera-bytes) of spatial data obtained from large databases such as credit card payments, telephone calls, environmental records, census demographics etc. is, however, a very difficult task. Visual data mining applies human visual perception to the exploration of large data sets. Presenting data in an interactive, graphical form often fosters new insights, encouraging the formation and validation of new hypotheses to the end of better problem-solving and gaining deeper domain knowledge. In this paper we give a short overview of visual data mining techniques, especially the area of analyzing spatial data. We provide some examples for effective visualizations of spatial data in important application areas such as consumer analysis, e-mail traffic analysis, and census demographics.

As archaeologists continue to adopt geographic information systems and computer-aided design software packages to record and store spatial data, excavations have begun to forgo hand-drawn plans in favor of digital recording. The purpose of this article is to present two case studies that have successfully utilized digital drafting techniques to create architectural plans at multiple scales. The first presents on-site 2D and 3D documentation at the site of Tel Akko, Israel, at the square, field, and tell scales. The second study combines orthophotos and legacy data to create an accurate site plan of Qasrin on the Golan Heights. Both harness image-based modeling to produce 2D and 3D spatial data in order to produce top plans with unprecedented spatial accuracy.

Geographic information system (GIS) applications for the siting and monitoring of gas pipeline rights-of-way (ROWS) were developed for areas near Rio Vista, California. The data layers developed for this project represent geographic features, such as landcover, elevation, aspect, slope, soils, hydrography, transportation, endangered species, wetlands, and public line surveys. A GIS was used to develop and store spatial data from several sources; to manipulate spatial data to evaluate environmental and engineering issues associated with the siting, permitting, construction, maintenance, and monitoring of gas pipeline ROWS; and to graphically display analysis results. Examples of these applications include (1) determination of environmentally sensitive areas, such as endangered species habitat, wetlands, and areas of highly erosive soils; (2) evaluation of engineering constraints, including shallow depth to bedrock, major hydrographic features, and shallow water table; (3) classification of satellite imagery for land use/landcover that will affect ROWS; and (4) identification of alternative ROW corridors that avoid environmentally sensitive areas or areas with severe engineering constraints.

Oracle Spatial and Graph is a geographic information system (GIS) which provides users the ability to store spatial data alongside conventional data in Oracle. As a result of the coexistence of spatial and other data, we observe a trend towards users performing increasingly complex queries which involve spatial as well as non-spatial predicates. Accurate selectivity values, especially for queries with multiple predicates requiring joins among numerous tables, are essential for the database optimizer to determine a good execution plan. For queries involving spatial predicates, this requires that reasonably accurate statistics collection has been performed on the spatial data. For extensible data cartridges such as Oracle Spatial and Graph, the optimizer expects to receive accurate predicate selectivity and cost values from functions implemented within the data cartridge. Although statistics collection for spatial data has been researched in academia for a few years; to the best of our knowledge, this is the first work to present spatial statistics collection implementation details for a commercial GIS database. In this paper, we describe our experiences with implementation of statistics collection methods for complex geometry objects within Oracle Spatial and Graph. Firstly, we exemplify issues with previous partitioning-based algorithms in presence of complex geometry objects and suggest enhancements which resolve the issues. Secondly, we propose a main memory implementation which not only speeds up the disk-based partitioning algorithms but also utilizes existing R-tree indexes to provide surprisingly accurate selectivity estimates. Last but not the least, we provide extensive experimental results and an example study which displays the efficacy of our approach on Oracle query performance.

HARMO-DATA is an ongoing project, funded by EU in the framework of the INTERREG V-A Italy-Slovenia 2014-2020 Programme. It involves different stakeholders, target groups and end-users in three regions: Friuli-Venezia-Giulia (Italy), Veneto (Italy) and Slovenia. The main purpose of this project is to develop common solutions for more efficient cross-border spatial data management – by harmonizing the existing spatial data, implementing a cross-border spatial data platform, and developing a common protocol for the harmonization of territorial data. It will provide an instrument to define the specific obligations and rights of the involved parts – in terms of data harmonization, exchange, use and maintenance. Five pilot case studies were identified by the project partners – in cooperation with public and private end-users, and additional stakeholders. The core use cases of the project relate to spatial data search, view and download, and the harmonization model for spatial datasets applies the INSPIRE data specifications. A joined common spatial data platform was established as an extension of the existing search-view-download platforms (metadata systems), upgraded and improved to better enable open data access by users from both Italy and Slovenia. The common spatial HARMO-DATA data platform, as well as, a joint protocol for cross-border spatial data harmonization, have been formalized in an official bilateral agreement.