scholarly journals 3D TOPOLOGICAL SUPPORT IN SPATIAL DATABASES: AN OVERVIEW

2021 ◽  
Vol XLVI-4/W5-2021 ◽  
pp. 473-478
Author(s):  
S. Salleh ◽  
U. Ujang ◽  
S. Azri
Keyword(s):  
Spatial Data ◽  
Complex Analysis ◽  
Spatial Databases ◽  
Database Management System ◽  
Geometrical Shape ◽  
3D Analysis ◽  
Topological Information ◽  
3D Objects ◽  
3D Cadastre ◽  
Topological Data

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.

Integrity Constraints in Spatial Databases

2002 ◽  
pp. 144-171 ◽  
Author(s):  
Karla A.V. Borges ◽  
Clodoveu A. Davis Jr. ◽  
Alberto H.F. Laender
Keyword(s):  
Spatial Data ◽  
Data Model ◽  
Spatial Databases ◽  
Spatial Information ◽  
Spatial Relationship ◽  
Object Oriented ◽  
Database Management System ◽  
Geometric Constraints ◽  
Integrity Constraints ◽  
Spatial Relationships

This chapter addresses the relationship that exists between the nature of spatial information, spatial relationships, and spatial integrity constraints, and proposes the use of OMT-G (Borges et al., 1999; Borges et al., 2001), an object-oriented data model for geographic applications, at an early stage in the specification of integrity constraints in spatial databases. OMT-G provides appropriate primitives for representing spatial data, supports spatial relationships and allows the specification of spatial integrity rules (topological, semantic and user integrity rules) through its spatial primitives and spatial relationship constructs. Being an object-oriented data model, it also allows some spatial constraints to be encapsulated as methods associated to specific georeferenced classes. Once constraints are explicitly documented in the conceptual modeling phase, and methods to enforce the spatial integrity constraints are defined, the spatial database management system and the application must implement such constraints. This chapter does not cover integrity constraints associated to the representation of simple objects, such as constraints implicit to the geometric description of a polygon. Geometric constraints are related to the implementation, and are covered here in a higher level view, considering only the shape of geographic objects. Consistency rules associated with the representation of spatial objects are discussed in Laurini and Thompson (1992).

scholarly journals Simulating Large-Scale 3D Cadastral Dataset Using Procedural Modelling

2020 ◽  
Vol 9 (10) ◽  
pp. 598 ◽  
Author(s):  
Jernej Tekavec ◽  
Anka Lisec ◽  
Eugénio Rodrigues
Keyword(s):  
Data Management ◽  
Spatial Data ◽  
Large Scale ◽  
Synthetic Data ◽  
Database Management System ◽  
Spatial Queries ◽  
Administration System ◽  
Land Administration ◽  
3D Cadastre ◽  
Land Administration Systems

Geospatial data and information within contemporary land administration systems are fundamental to manage the territory adequately. 3D land administration systems, often addressed as 3D cadastre, promise several benefits, particularly in managing today’s complex built environment, but these are currently still non-existent in their full capacity. The development of any complex information and administration system, such as a land administration system, is time-consuming and costly, particularly during the phase of evaluation and testing. In this regard, the process of implementing such systems may benefit from using synthetic data. In this study, the method for simulating the 3D cadastral dataset is presented and discussed. The dataset is generated using a procedural modelling method, referenced to real cadastral data for the Slovenian territory and stored in a spatial database management system (DBMS) that supports storage of 3D spatial data. Spatial queries, related to 3D cadastral data management, are used to evaluate the database performance and storage characteristics, and 3D visualisation options. The results of the study show that the method is feasible for the simulation of large-scale 3D cadastral datasets. Using the developed spatial queries and their performance analysis, we demonstrate the importance of the simulated dataset for developing efficient 3D cadastral data management processes.

Infrastructural approach to processing of spatial data in the problems of management of territorial development

2018 ◽  
Author(s):  
И.В. Бычков ◽  
Г.М. Ружников ◽  
В.В. Парамонов ◽  
А.С. Шумилов ◽  
Р.К. Фёдоров
Keyword(s):  
Data Processing ◽  
Spatial Data ◽  
Data Exchange ◽  
Complex Analysis ◽  
Data Entry ◽  
Content Management ◽  
Volume Data ◽  
Territorial Development ◽  
Distributed Services ◽  
Spatial Data Processing

Рассмотрен инфраструктурный подход обработки пространственных данных для решения задач управления территориальным развитием, который основан на сервис-ориентированной парадигме, стандартах OGC, web-технологиях, WPS-сервисах и геопортале. The development of territories is a multi-dimensional and multi-aspect process, which can be characterized by large volumes of financial, natural resources, social, ecological and economic data. The data is highly localized and non-coordinated, which limits its complex analysis and usage. One of the methods of large volume data processing is information-analytical environments. The architecture and implementation of the information-analytical environment of the territorial development in the form of Geoportal is presented. Geoportal provides software instruments for spatial and thematic data exchange for its users, as well as OGC-based distributed services that deal with the data processing. Implementation of the processing and storing of the data in the form of services located on distributed servers allows simplifying their updating and maintenance. In addition, it allows publishing and makes processing to be more open and controlled process. Geoportal consists of following modules: content management system Calipso (presentation of user interface, user management, data visualization), RDBMS PostgreSQL with spatial data processing extension, services of relational data entry and editing, subsystem of launching and execution of WPS-services, as well as services of spatial data processing, deployed at the local cloud environment. The presented article states the necessity of using the infrastructural approach when creating the information-analytical environment for the territory management, which is characterized by large volumes of spatial and thematical data that needs to be processed. The data is stored in various formats and applications of service-oriented paradigm, OGC standards, web-technologies, Geoportal and distributed WPS-services. The developed software system was tested on a number of tasks that arise during the territory development.

scholarly journals A Survey on Spatio-temporal Data Analytics Systems

2022 ◽  
Author(s):  
Md Mahbub Alam ◽  
Luis Torgo ◽  
Albert Bifet
Keyword(s):  
Programming Languages ◽  
Spatial Data ◽  
Data Analytics ◽  
Spatial Databases ◽  
Location Based Services ◽  
Temporal Data ◽  
Wide Range ◽  
Data Volume ◽  
Spatio Temporal ◽  
Gis Software

Due to the surge of spatio-temporal data volume, the popularity of location-based services and applications, and the importance of extracted knowledge from spatio-temporal data to solve a wide range of real-world problems, a plethora of research and development work has been done in the area of spatial and spatio-temporal data analytics in the past decade. The main goal of existing works was to develop algorithms and technologies to capture, store, manage, analyze, and visualize spatial or spatio-temporal data. The researchers have contributed either by adding spatio-temporal support with existing systems, by developing a new system from scratch, or by implementing algorithms for processing spatio-temporal data. The existing ecosystem of spatial and spatio-temporal data analytics systems can be categorized into three groups, (1) spatial databases (SQL and NoSQL), (2) big spatial data processing infrastructures, and (3) programming languages and GIS software. Since existing surveys mostly investigated infrastructures for processing big spatial data, this survey has explored the whole ecosystem of spatial and spatio-temporal analytics. This survey also portrays the importance and future of spatial and spatio-temporal data analytics.

scholarly journals The Unification of the Code Lists Provided within the Data Model Originating from the Inspire Technical Guidelines and the Ones Provided for Gesut Databases in the Context of Potential Exploitation in the Mining Industry

2016 ◽  
Vol 23 (3) ◽  
pp. 178-182
Author(s):  
Andrzej Zygmuniak ◽  
Violetta Sokoła-Szewioła
Keyword(s):  
Spatial Data ◽  
Data Model ◽  
Spatial Databases ◽  
Mining Industry ◽  
Data Models ◽  
Research Projects ◽  
Mining Areas ◽  
European Parties ◽  
Potential Exploitation ◽  
The Way

Abstract This study is aimed at exposing differences between two data models in case of code lists values provided there. The first of them is an obligatory one for managing Geodesic Register of Utility Networks databases in Poland [9] and the second is the model originating from the Technical Guidelines issued to the INSPIRE Directive. Since the second one mentioned is the basis for managing spatial databases among European parties, correlating these two data models has an effect in easing the way of harmonizing and, in consequence, exchanging spatial data. Therefore, the study presents the possibilities of increasing compatibility between the values of the code lists concerning attributes for objects provided in both models. In practice, it could lead to an increase of the competitiveness of entities managing or processing such databases and to greater involvement in scientific or research projects when it comes to the mining industry. Moreover, since utility networks located on mining areas are under particular protection, the ability of making them more fitted to their own needs will make it possible for mining plants to exchange spatial data in a more efficient way.

scholarly journals From Surveying to Geomatics

2016 ◽  
Vol 10 (3-4) ◽  
pp. 153-160 ◽  
Author(s):  
Besim Ajvazi ◽  
Fisnik Loshi ◽  
Béla Márkus
Keyword(s):  
Information And Communication Technologies ◽  
Spatial Data ◽  
Professional Education ◽  
Spatial Databases ◽  
Unmanned Aerial Systems ◽  
Geospatial Information ◽  
Automatic Data ◽  
Data Infrastructure ◽  
Building Information ◽  
Concept Building

In the land surveying profession fast changes have been taking place in the last fifty years. Technological changes are generated by the Information and Communication Technologies; the analogue – digital trends; the automatic data acquisition methods replace manual ones; instead of two-dimensional base maps we use dynamic spatial databases more and more integrated into a global data infrastructure. However, these changes cause impacts also on scientific level. The traditional top-down approach substituted by bottom-up methodologies; in many cases the point-by-point measurement is changed by 3D laserscanning or Unmanned Aerial Systems, which produces huge amount of data, but it needs new algorithms for information extraction; instead of a simple data provision land surveyors support complex spatial decisions. The paper is dealing with some aspects of these changes. In the first chapter the authors would like to highlight the “data-information-knowledge” relations and the importance of changes in professional education. The second chapter gives an example of the benefits of a Global Spatial Data Infrastructure in spatial decision support. Finally we introduce a new concept (Building Information Modelling) in modelling the real world. However, until now BIM is used in building construction industry, it can can be a paradigm shift in geospatial information management in general.

scholarly journals A REVIEW OF 3D GIS FOR USE IN CREATING VIRTUAL HISTORIC DUBLIN

2019 ◽  
Vol XLII-2/W9 ◽  
pp. 249-254
Author(s):  
A. Chenaux ◽  
M. Murphy ◽  
S. Pavia ◽  
S. Fai ◽  
T. Molnar ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Spatial Data ◽  
Complex Analysis ◽  
Remote Sensing Data ◽  
Public Works ◽  
3D Gis ◽  
Building Information ◽  
Urban Rural ◽  
Interactive 3D ◽  
Under Construction

<p><strong>Abstract.</strong> This paper illustrates how BIM integration with GIS is approached as part of the workflow in creating Virtual Historic Dublin. A design for a WEB based interactive 3D model of historic buildings and centres in Dublin City (Virtual Historic Dublin City) paralleling smart city initiates is now under construction and led by the National Monuments at the Office of Public Works in Ireland. The aim is to facilitate the conservation and maintenance of historic infrastructure and fabric and the dissemination of knowledge for education and cultural tourism using an extensive Historic Building Information Model. Remote sensing data is now processed with greater ease to create 3D intelligent models in Historic BIM. While the use of remote sensing, HBIM and game engine platforms are the main applications used at present, 3D GIS has potential to form part of the workflow for developing the Virtual Historic City. 2D GIS is now being replaced by 3D spatial data allowing more complex analysis to be carried out, 3D GIS can define and depict buildings, urban rural centres in relation to their geometry topological, semantic and visualisation properties. The addition of semantic attributes allows complex analysis and 3D spatial queries for modelling city and urban elements. This analysis includes fabric and structural elements of buildings, relief, vegetation, transportation, water bodies, city furniture and land use.</p>

An Object-Oriented Approach to Managing Fuzziness in Spatially Explicit Models Coupled to a Geographic Database

2011 ◽  
pp. 269-300 ◽  
Author(s):  
Vincent B. Robinson ◽  
Phil A. Graniero
Keyword(s):  
Spatial Data ◽  
Spatial Databases ◽  
Ecological Modeling ◽  
Simulation Models ◽  
Spatially Explicit ◽  
Loosely Coupled ◽  
Spatially Explicit Models ◽  
Modeling Problem ◽  
Object Oriented Approach ◽  
Oriented Approach

This chapter uses a spatially explicit, individual-based ecological modeling problem to illustrate an approach to managing fuzziness in spatial databases that accommodates the use of nonfuzzy as well as fuzzy representations of geographic databases. The approach taken here uses the Extensible Component Objects for Constructing Observable Simulation Models (ECO-COSM) system loosely coupled with geographic information systems. ECO-COSM Probe objects flexibly express the contents of a spatial database within the context of an individualized fuzzy schema. It affords the ability to transform traditional nonfuzzy spatial data into fuzzy sets that capture the uncertainty inherent in the data and model’s semantic structure. The ecological modeling problem was used to illustrate how combining Probes and ProbeWrappers with Agent objects affords a flexible means of handling semantic variation and is an effective approach to utilizing heterogeneous sources of spatial data.

Data Discovery Approaches for Vague Spatial Data

Data Mining ◽  
2013 ◽  
pp. 50-65
Author(s):  
Frederick E. Petry
Keyword(s):  
Data Mining ◽  
Fuzzy Sets ◽  
Association Rules ◽  
Rough Set ◽  
Fuzzy Set ◽  
Spatial Data ◽  
Spatial Databases ◽  
Uncertain Data ◽  
Rule Extraction ◽  
Data Discovery

This chapter focuses on the application of the discovery of association rules in approaches vague spatial databases. The background of data mining and uncertainty representations using rough set and fuzzy set techniques is provided. The extensions of association rule extraction for uncertain data as represented by rough and fuzzy sets is described. Finally, an example of rule extraction for both types of uncertainty representations is given.

Parallel Queries of Cluster-Based k Nearest Neighbor in MapReduce

2016 ◽  
pp. 163-182
Author(s):  
Wei Yan
Keyword(s):  
Spatial Data ◽  
Spatial Databases ◽  
Nearest Neighbor ◽  
Programming Model ◽  
K Nearest Neighbor ◽  
K Nearest Neighbors ◽  
Data Intensive ◽  
Parallel Queries ◽  
Massive Spatial Data ◽  
Nearest Neighbor Queries

Parallel queries of k Nearest Neighbor for massive spatial data are an important issue. The k nearest neighbor queries (kNN queries), designed to find k nearest neighbors from a dataset S for every point in another dataset R, is a useful tool widely adopted by many applications including knowledge discovery, data mining, and spatial databases. In cloud computing environments, MapReduce programming model is a well-accepted framework for data-intensive application over clusters of computers. This chapter proposes a parallel method of kNN queries based on clusters in MapReduce programming model. Firstly, this chapter proposes a partitioning method of spatial data using Voronoi diagram. Then, this chapter clusters the data point after partition using k-means method. Furthermore, this chapter proposes an efficient algorithm for processing kNN queries based on k-means clusters using MapReduce programming model. Finally, extensive experiments evaluate the efficiency of the proposed approach.

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