Materialized aR-Tree in Distributed Spatial Data Warehouse

Spatial Data Warehouses (SDW) and Spatial OLAP (SOLAP) systems are well-known Business Intelligence technologies that aim to support multidimensional and online analysis of huge volumes of datasets with spatial reference. Spatial vagueness is one of the most neglected imperfections of spatial data. Although several works propose new ad-hoc models for handling spatial vagueness in information systems, the implementation of those models in Spatial DBMS and SDW is still in an embryonic state. Thus, in this paper, we present a new design method for SOLAP datacubes that allows handling vague spatial data analysis issues. This method relies on a risk management method applied to the potential risks of data misinterpretation and decision-makers’ tolerance levels to those risks. We also present a system implementing our method.

A spatial data warehouse (SDW) is constructed to support the spatial data analysis for decision support purposes. Selectively materializing spatial views to rewrite input queries and thus reduce query response time is a challenging issue for spatial query evaluations. In this paper, we first investigate the issue of using spatial metadata to construct a spatial view dependency framework, which implies an order to materialize views. We then propose a cost model to evaluate the cost for processing spatial queries, which measures the online computation vs. space cost for spatial queries. A greedy algorithm is introduced to materialize a set of views based on the view dependence framework with associated cost value at each dependence level, which shows the local cost optimality of designing an SDW.

GIS is a geo-referenced data based system for manipulating digital spatial and thematic data. Because of its unique properties of spatial data (spatial dependence and spatial heterogeneity), there is an urgent need to investigate the mechanisms of spatial processes through exploring spatial data. The techniques of spatial analysis can be categorized into six groups: (1) generation of spatial data and its preliminary treatment; (2) spatialization of attribute data and scale transformation of spatial data; (3) visualization and exploration of spatial data; (4) geo-statistics; (5) analysis of lattice data; and (6) reversing information from complicated spatial data. To meet the real world solutions, a procedural diagram of spatial data analysis is suggested as below: (1) preliminary data treatment; (2) modeling; (3) solution; and (4) interpretation. Various techniques are being employed to investigate spatial processes, the identical study objective and some common characteristics of the study object may lead to the integration and convergence of the various approaches, and further the introduction of a general science: spatial mathematics. A same important issue is to develop robust spatial analysis computer packages for public uses.

With a huge amount of data stored in spatial databases and the introduction of spatial components to many relational or object-relational databases, it is important to study the methods for spatial data warehousing and OLAP of spatial data. In this paper, we study methods for spatial OLAP, by integrating nonspatial OLAP methods with spatial database implementation techniques. A spatial data warehouse model, which consists of both spatial and nonspatial dimensions and measures, is proposed. Methods for the computation of spatial data cubes and analytical processing on such spatial data cubes are studied, with several strategies being proposed, including approximation and selective materialization of the spatial objects resulting from spatial OLAP operations. The focus of our study is on a method for spatial cube construction, called object-based selective materialization, which is different from cuboid-based selective materialization (proposed in previous studies of nonspatial data cube construction). Rather than using a cuboid as an atomic structure during the selective materialization, we explore granularity on a much finer level: that of a single cell of a cuboid. Several algorithms are proposed for object-based selective materialization of spatial data cubes, and a performance study has demonstrated the effectiveness of these techniques.

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

Computational science has significantly advanced theory and experiment over many decades. Specifically, computational geography emerged in the 1980s in response to the reductionist limitations of early GIS software, which hindered deeper analyses of complex geographic data. The advent of relational databases further facilitated the use of computational methods in spatial data analysis. This study discusses the application of computational techniques and geocomputation in the spatial analysis of geographical phenomena. A literature search and data synthesis were conducted, followed by an exploration of computational methods, geocomputation, spatial data representation, storage and organization, spatial analytics, and GeoAI. Geocomputation is defined as the application of computer- intensive approaches, particularly those employing non-conventional data clustering and analysis methods, to discern knowledge. At present, these computational techniques enable the integration of diverse fields, supporting spatial analyses that require resources or ontological frameworks beyond the capabilities of traditional GIS software.

With the increasing development of the application of location services, massive check-in data is produced by social media applications on the mobile appliances, which includes characteristics of spatio-temporal information, user-emotion information, and etc. Traditional analysis techniques cannot handle check-in data well because of the complexity of spatio-temporal information. Spatial data warehouse provided a good architecture for spatial data’s storage and analysis. In this research, we designed a spatial data warehouse to store and manage the check-in data, used OLAP analysis technology to analyze it, and found many interesting results. It showed spatial data warehouse and OLAP provided a good frame to analyze check-in data.

Spatial data generated by an Internet of Things (IoT) network is important to assist the spatial analytics process in issues related to smart cities. In these cities, IoT devices generate spatial data constantly. Thus, data can get increasingly voluminous very fast. In this paper, we investigate the challenge of managing these data through the use of a spatial data warehouse designed over a parallel and distributed data processing framework extended with a spatial analytics system. We propose an architecture aimed to assist a smart cities manager in the decision-making process. This architecture integrates a cloud layer where these technologies are located with a fog computing layer for extracting, transforming and loading the data into the spatial data warehouse. Furthermore, we introduce a set of guidelines to aid smart cities managers to implement the proposed architecture. These guidelines describe and discuss important issues that should be faced by the managers. We validate our architecture with a case study that uses real data collected by IoT devices in a smart city. This case study encompasses the execution of three different categories of spatial queries, demonstrating the architecture's efficacy and effectiveness to support spatial analytics in the context of smart cities.

In order to study nowcasting operations using spatial data mining, a spatial data warehouse must be built via ETL process to store radar spatial data reflects strong convection weather. Also, the quality control of radar spatial data in the ETL process, which directed at weather station observed data and radar spatial data. based on dynamic incremental rule engine(DIRE) and ISTDW method. The experimental result demonstrates that DIRE can effectively uncover the abnormal value in radar data and that ISTDW can estimate the absent values in radar data via interpolation with the tolerance of errors.

Although many real world phenomena are vague and characterized by having uncertain location or vague shape, existing spatial data warehouse models do not support spatial vagueness and then cannot properly represent these phenomena. In this paper, we propose the VSCube conceptual model to represent and manipulate shape vagueness in spatial data warehouses, allowing the analysis of business scores related to vague spatial data, and therefore improving the decision-making process. Our VSCube conceptual model is based on the cube metaphor and supports geometric shapes and the corresponding membership values, thus providing more expressiveness to represent vague spatial data. We also define vague spatial aggregation functions (e.g. vague spatial union) and vague spatial predicates to enable vague SOLAP queries (e.g. intersection range queries). Finally, we introduce the concept of vague SOLAP and its operations (e.g. drill-down and roll-up). We demonstrate the applicability of our model by describing an application concerning pest control in agriculture and by discussing the reuse of existing models in the VSCube conceptual model.

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!

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.

This article describes how Spatial On-Line Analytical Processing, such a decisional technology, offers the possibilities of spatial and multidimensional analysis of data stored in multidimensional structure namely spatial data warehouse. However, this technology is limited in the quality aspect of the decision related to the multicriteria consideration. In the current article, the objective is to propose a Spatial Decision Support System namely “Silvicultura” for facilitating decision making in complex situations. This approach is based on integrating multicriteria analysis with SOLAP in order to enrich the spatial and multidimensional analysis with the contribution of MCA tools for mitigating conflict situations. The authors have based their proposal modeling on Unified Modeling language, since it is a well-known standard modeling language and can be easily extended for multidimensional modeling. Finally, in order to validate their proposal, the authors present a case study to show how to use it in the agroforestry management.

Geographic Artificial Intelligence (GeoAI) and Natural Language Processing (NLP) are two rapidly advancing technologies in spatial data analysis. GeoAI integrates artificial intelligence with geographic data to identify patterns, make predictions, and support decision-making. Meanwhile, NLP enables the processing and analysis of textual data related to spatial information, such as documents, reports, or geographic descriptions. The objective of this research is to obtain a representation of spatial data patterns through a series of processes, including problem identification, needs analysis, data collection and processing, document representation, and the application of Geographic Artificial Intelligence (GeoAI), Natural Language Processing (NLP), and Fuzzy Similarity methods to spatial or textual tax data and textual land data to identify data similarities. The research explores the integration of GeoAI and NLP in spatial data analysis to enhance the efficiency and accuracy of geographic data interpretation. The methods used in this study are based on artificial intelligence, which extracts spatial information from text and performs machine learning-based spatial analysis. The results demonstrate that the combination of GeoAI and NLP can improve the understanding of spatial patterns in unstructured data and support location-based decision-making processes. This research contributes to the development of more accurate spatial data analysis techniques that can be applied in various fields, such as urban planning, disaster management, and environmental analysis.

The origins of modern spatial analysis lie in the development of quantitative geography and regional science in the late 1950s. The use of quantitative procedures and techniques to analyse patterns of points, lines, areas and surfaces depicted on analogue maps or defined by co-ordinates in two-or three-dimensional space characterise the initial stage. Later on, more emphasis was placed on the indigenous features of geographical space, on spatial choices and processes, and their implications for the spatio-temporal evolution of complex spatial systems. Spatial analysis, as it has become over the past four decades, is more than spatial statistics and data analysis, and goes far beyond data sampling, data manipulation, exploratory and confirmatory spatial data analysis, into areas of spatial modelling encompassing a large and diverse set of models in both the environmental and the social sciences. In the environmental sciences, they range from physical dispersion models (e.g. for suspended particulates), chemical reaction models (e.g. photochemical smog), and biological systems models (e.g. for ecosystems in water), through deterministic process and stochastic process models of regional environmental quality management, and integrated models for environment-energy-economic assessment. For many models, especially the physical dispersion models, chemical reaction models, and biological systems models, the geographical location of the site is not considered to be of overriding importance. The variation in model results over space can be achieved by obtaining different inputs to the area in question. Process models include deterministic versions which attempt to describe a particular sequence in terms of known physical laws, and stochastic model versions which aim to describe a particular process such as erosion, groundwater movement and absorption of pollutants in terms of probability theory. For such processes, there is an interaction between the spatial process and a substrate. This substrate provides a one-, two-or three-dimensional framework within which the process model can operate. The space in which the process is modelled is disaggregated into a set of finite elements which are usually assumed to be internally homogeneous. Two-dimensional models generally use as their data small pixels (see Fischer et al., 1996).