Spatial Database Management System Research Articles

Design and Management of a Spatial Database for Monitoring Building Comfort and Safety

As the impacts of climate change on urban environments and buildings become more and more prominent, building comfort and structural safety monitoring becomes crucial. However, efficiently storing and managing the multi-source monitoring data generated during the long-term monitoring process has been an urgent challenge. In order to solve the above problems, this paper designs and develops a spatial database management system for building comfort and structural safety monitoring based on standard database development tools. A conceptual model of a spatial database for building comfort and structural safety monitoring is proposed, and the entities, attributes, and connections in the model are discussed to transform the E-R conceptual model into a logical model supported by an object-relational spatial database management system. Based on this conceptual and logical model, a mainstream backend framework was adopted and combined with common database and programming language tools and BIM (Building Information Modeling) technology for development to establish a spatial database management system with data storage, management, analysis, and visualization functions. We designed building monitoring experiments and proved through the experiments that the database management system can stably store, analyze, and manage the monitoring data and visualize the display, with the advantages of a fast response speed and low error rate. The spatial database system improves the storage and management efficiency of building comfort and structural safety monitoring data, eliminates redundant data, and realizes comprehensive analysis and management of building comfort and structural safety monitoring data. It provides data support for building comfort and structural safety assessment, helps users analyze the formation mechanism and evolution law of the urban heat island effect, assesses the interrelationship between climate change and urban building morphology, and constructs an urban thermal environment that is more adaptable to climate change.

A Marine Spatial Data Infrastructure to manage multidisciplinary, inhomogeneous and fragmented geodata in a FAIR perspective ... the Adriatic Sea experience

The Institute of Marine Sciences (ISMAR) and the Institute of Polar Sciences (ISP) of the Italian National Research Council (CNR) have gathered a substantial amount of heterogeneous geodata through the years in the Adriatic Sea, with different methodologies and for multiple scopes regarding geological, oceanographic, biological, anthropogenic aspects, and their interactions.To overcome challenges in datasets heterogeneity and fragmentation, a Marine Spatial Data Infrastructure (MSDI) has been set up, with the aim to integrate and preserve geodata, foster their reuse (e.g. the generation of scenarios for geological past and future developments by the application of numerical models), and ensure a good degree of FAIRness (FAIR: Findable, Accessible, Interoperable, and Reusable). The MSDI consists of a Spatial Relational Database Management System (RDBMS) based on specific data models designed following in part the INSPIRE Directive data specifications, a WebGIS, a metadata catalogue, and a cloud system. This paper shows the potentialities of this MSDI and discusses the main implementation steps, the elements that make up the infrastructure, the level of FAIRness reached, the main elements promoting FAIRness, and the gaps to be covered.Compliance with the FAIR principles represents a fundamental step to developing interoperability with European and international marine data management infrastructures for handling and exchanging multidisciplinary data.

A WEBGIS FRAMEWORK FOR SEMI-AUTOMATED GEODATABASE UPDATING ASSISTED BY DEEP LEARNING

Abstract. The automation of geoinformation (GI) collection and interpretation has been a fundamental goal for many researchers. The developments in various sensors, platforms, and algorithms have been contributing to the achievement of this goal. In addition, the contributions of citizen science (CitSci) and volunteered geographical information (VGI) concepts have become evident and extensive for the geodata collection and interpretation in the era where information has the utmost importance to solve societal and environmental problems. The web- and mobile-based Geographical Information Systems (GIS) have facilitated the broad and frequent use of GI by people from any background, thanks to the accessibility and the simplicity of the platforms. On the other hand, the increased use of GI also yielded a great increment in the demand for GI in different application areas. Thus, new algorithms and platforms allowing human intervention are immensely required for semi-automatic GI extraction to increase the accuracy. By integrating the novel artificial intelligence (AI) methods including deep learning (DL) algorithms on WebGIS interfaces, this task can be achieved. Thus, volunteers with limited knowledge on GIS software can be supported to perform accurate processing and to make guided decisions. In this study, a web-based geospatial AI (GeoAI) platform was developed for map updating by using the image processing results obtained from a DL algorithm to assist volunteers. The platform includes vector drawing and editing capabilities and employs a spatial database management system to store the final maps. The system is flexible and can utilise various DL methods in the image segmentation.

DEVELOPMENT OF A GEO-ANALYTICS PLATFORM FOR POST-DISASTER GROUND ASSESSMENT

Abstract. Earthquake is a frequently encountered natural hazard and can cause losses of lives and assets. In order to understand the nature of seismic hazards and the risks sourced from them, careful investigations are needed by using diverse geoinformation types. Thanks to the availability of multi-platform and high-resolution geospatial datasets, vast amount of geodata can be collected and analyzed for obtaining timely information on land’s surface before and after an earthquake event; and to assist the disaster management authorities and first responders for supporting the mitigation efforts. On January 24, 2020, an earthquake with Mw 6.8 occurred in Elazig Province, Turkey; caused 41 deaths and damaged the buildings and infrastructure. Although various geodatasets could be collected and processed by the geomatics experts, their utilization by other stakeholders, such as geoscientists, local authorities, and citizens remained limited due to the accessibility issues, and high complexity in their visualization, processing and interpretation. In this study, a web-based platform called EQ4D was designed and implemented to present the multi-sensor and multi-platform 4D data to the stakeholders, and to allow for basic geo-analytical processes such as change detection, 3D surface measurements, etc. Cesium JS and Potree libraries were employed for 3D visualization. PostGIS spatial database management system was used for storing and managing the data, and for performing spatial queries. Currently EQ4D is suitable for the use of geoscientists, and can be further customized for generic use at similar geohazards and developed for the use of inexperienced users, such as citizen scientists.

Accurate Algorithms for Spatial Operations on the Spheroid in a Spatial Database Management System

Some of the most powerful spatial analysis software solutions (Oracle, Google Earth Engine, PostgreSQL + PostGIS, etc.) are currently performing geometric calculations directly on the ellipsoid (a quadratic surface that models the earth shape), with a double purpose: to attain a high degree of accuracy and to allow the full management of large areas of territory (countries or even continents). It is well known that both objectives are impossible to achieve by means of the traditional approach using local mathematical projections and Cartesian coordinates. This paper demonstrates in a quantitative methodological way that most of the spatial analysis software products make important deviations in calculations regarding to geodesics, being the users unaware of the magnitude of these inaccuracies, which can easily reach meters depending on the distance. This is due to the use of ellipsoid calculations in an approximate way (e.g., using a sphere instead of an ellipsoid). This paper presents the implementation of two algorithms that solve with high accuracy (less than 100 nm) and efficiently (few iterations) two basic geometric calculations on the ellipsoid that are essential to build more complex spatial operators: the intersection of two geodesics and the minimum distance from a point to a geodesic.

Sea level rise inundation risk assessment in residential cadastral parcels along the Mediterranean Andalusian coast

The general objective of this article is to present the methodological approach for the assessment of the inundation risks associated with the rise in the mean sea level for different models and scenarios of climatic change (IPCC, 2013 and Jevrejeva et al., 2012). The approach focuses on the assessment of the hazard, exposure and physical vulnerability of the built areas, especially the residential areas along the Mediterranean coast of Andalusia (Spain). For hazards calculation a set of tide gauges’ data and climatic scenarios were analyzed to obtain future sea levels for all the spectrum of probabilities. Further, a method is proposed for mapping the probability associated with each scenario using a 5 m cell size DTM. For exposure and vulnerability assessment of built up areas, especially for residential ones, the National Cadastre, which is the most detailed set of data, have been used. Modeling the original cadastral data in a spatial database management system and their analysis through SQL sentences have made possible to identify the cadastral parcels with residential use and associated variables (area, number of residential real estate units, constructed area below ground, etc..), being therefore the cadastral parcel the spatial reference unit for mapping. The overlay of cadastral parcels and the inundation hazard maps has allowed identifying the residential cadastral parcels exposed to each climatic scenario. A method is proposed for better identification of the number of real estate units in each cadastral parcel, assuming this number as the base for residential parcel vulnerability. Finally, the risk assessment is calculated as the product of the previous variables. For the dissemination of the inundation risk assessment results, a web application (geoviewer) was developed. Results show that for the more pessimistic scenario more than 24,000 exposed cadastral parcels would be affected. Out of that, 13,000 exposed residential cadastral parcels were identified and their hazards, exposure and vulnerability calculated in order to map the final inundation risk associated with future sea levels.

ДОСЛІДЖЕННЯ ПРОСТОРОВИХ СИСТЕМ УПРАВЛІННЯ БАЗОЮ ДАНИХ ДЛЯ РОЗВИТКУ 3D-КАДАСТРУ

Due to the development of world technologies, users of land resources and facilities that exist on them are aware of the need to radically change the ways of information content of cadastral systems, the organization of these works and the need to introduce a 3-dimensional cadastre. The 2D inventory is no longer able to reflect the true state of objects due to increasing requirements for accuracy and interconnection of information, namely on the ground, underground and above ground. The suggested 3D-cadastre is considered as an information element of the state land cadastre with underground parts of buildings and communication networks that go beyond the aboveground part of the land. For the 3D-cadastre to function, it is necessary to provide an integrated spatial database management system, which will be the basis of a 3-dimensional cadastre. It is determined that one of the main factors of 3D-cadastre, which is not taken into account in the modern existence of land cadastral information is the volume of the object and taking into account the complexity of the terrain, which in turn causes incorrect functioning of 3D-cadastre. In addition, there is an equally popular factor, namely, taking into account time, that is the information about the object over time. In the operation of 3D-cadastre, it should be noted that all cadastral information must be integrated, in other words, the relationship of all data, which, when superimposed, creates a single land cadastral information through the formation of a database management system in three-dimensional space. Existing land cadastral information is a basic component of the 3D-cadastre, which should be filled with information and create a database management system that will display the 3D-cadastre. The use of ordinary cadastral information to ensure the completeness of 2D-cadastre data is sufficient. Most of the parameters of land cadastral information in 2D format are quite suitable for 3D-cadastre. The introduction of 3D-cadastre will be used in land management, administrative and urban planning works. A separate and important place in this system is given to 3D-registration.

Simulating Large-Scale 3D Cadastral Dataset Using Procedural Modelling

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.

High-quality topological structures selection for smart city land spatial understanding and governance

Due to the acceleration of urbanization process in modern society, there are many metropolises throughout the world, such as New York, Tokyo, and Shanghai. Such large cities typically have a carefully designed space planning. For example, the residential area should be distant from the industrial area due to the potential environmental pollution issue. In order to cope with public health emergencies, the infrastructure needed for modern urban governance needs to be improved. Notably, establishing a quality visual model to exploit such sophisticated spatial configurations is an important but challenging task. Such a model can facilitate many applications such as urban planning, environmental evaluation, smart transportation, and urban governance. However, the flexible spatial interactions among multiple regions make it difficult to apply a traditional visual model to encode them. In this work, a quality-guided feature selection framework is proposed to obtain a set of high-quality topologies to model the discriminative structures from different land spatial city regions. Given a city region from a metropolitan area, the well-known super pixel algorithm SLIC is used to decompose each land spatial city region image into multiple atomic regions. Based on this, a binary graph is used to model the spatial interactions among these regions. Each binary graph is then decomposed into multiple subgraphs and a topology selection algorithm is proposed to discover subgraphs with highly discriminative topologies. By leveraging these high-quality subgraphs, the image kernel machine is used to convert the high quality subgraphs from each city region’s image into a feature vector. Afterward, a multi-category support vector machine (SVM) is learned to classify each city region’s image into one particular category. Comprehensive experimental results by comparing with many state-of-the-art have shown the competitiveness of this method. Furthermore, the selected high-quality topologies have demonstrated that highly representative spatial interactions are nicely encoded. The results are of great significance to the establishment of the land spatial database management system.

A Survey on Big Data for Trajectory Analytics

Trajectory data allow the study of the behavior of moving objects, from humans to animals. Wireless communication, mobile devices, and technologies such as Global Positioning System (GPS) have contributed to the growth of the trajectory research field. With the considerable growth in the volume of trajectory data, storing such data into Spatial Database Management Systems (SDBMS) has become challenging. Hence, Spatial Big Data emerges as a data management technology for indexing, storing, and retrieving large volumes of spatio-temporal data. A Data Warehouse (DW) is one of the premier Big Data analysis and complex query processing infrastructures. Trajectory Data Warehouses (TDW) emerge as a DW dedicated to trajectory data analysis. A list and discussions on problems that use TDW and forward directions for the works in this field are the primary goals of this survey. This article collected state-of-the-art on Big Data trajectory analytics. Understanding how the research in trajectory data are being conducted, what main techniques have been used, and how they can be embedded in an Online Analytical Processing (OLAP) architecture can enhance the efficiency and development of decision-making systems that deal with trajectory data.

Towards Integrating Heterogeneous Data: A Spatial DBMS Solution from a CRC-LCL Project in Australia

Over recent decades, more and more cities worldwide have created semantic 3D city models of their built environments based on standards across multiple domains. 3D city models, which are often employed for a large range of tasks, go far beyond pure visualization. Due to different spatial scale requirements for planning and managing various built environments, integration of Geographic Information Systems (GIS) and Building Information Modeling (BIM) has emerged in recent years. Focus is now shifting to Precinct Information Modeling (PIM) which is in a more general sense to built-environment modeling. As scales change so do options to perform information modeling for different applications. How to implement data interoperability across these digital representations, therefore, becomes an emerging challenge. Moreover, with the growth of multi-source heterogeneous data consisting of semantic and varying 2D/3D spatial representations, data management becomes feasible for facilitating the development and deployment of PIM applications. How to use heterogeneous data in an integrating manner to further express PIM is an open and comprehensive topic. In this paper, we develop a semantic PIM based on multi-source heterogeneous data. Then, we tackle spatial data management problems in a Spatial Database Management System (SDBMS) solution for our defined unified model. Case studies on the University of New South Wales (UNSW) campus demonstrate the efficiency of our solution.

The design of road conditions mapping system by utilizing openstreetmap spatial data

Land transportation still becomes a vital role in human and material mobility to date. In developing countries, like Indonesia, the development of land transportation infrastructure is growing very fast, but the level of road damage is also still quite high. Therefore, road users need information on road conditions that will be passed. This research proposes the use of openstreetmap data to develop spatial data of road conditions dynamically and inexpensively, because it is open access and free editable. The focus is to design spatial data of road conditions and to present a visualization of road condition maps based on Openstreetmap. The raw data will be exported to the Spatial Database Management Systems (SDBMS) and then redesigned the data so that the system can add the status of road condition on each road segments. The result is a system design that can store spatial data and present a visualization of the road condition map. With this system, anyone can update the condition data of a road and can access the road condition map through the web and mobile applications.

Query rewriting for semantic query optimization in spatial databases

Query processing is an important challenge for spatial databases due to the use of complex data types that represent spatial attributes. In particular, due to the cost of spatial joins, several optimization algorithms based on indexing structures exist. The work in this paper proposes a strategy for semantic query optimization of spatial join queries. The strategy detects queries with empty results and rewrites queries to eliminate unnecessary spatial joins or to replace spatial by thematic joins. This is done automatically by analyzing the semantics imposed by the database schema through topological dependencies and topological referential integrity constraints. In this way, the strategy comes to complement current state-of-art algorithms for processing spatial join queries. The experimental evaluation with real data sets shows that the optimization strategy can achieve a decrease in the time cost of a join query using indexing structures in a spatial database management system (SDBMS).

Relational spatial database and multi-criteria decision methods for selecting optimum locations for photovoltaic power plants in the province of Seville (southern Spain)

This work aims to develop a methodology for selecting optimum locations for the construction of photovoltaic power plants. In order to achieve this, a data model is defined and a multi-criteria decision methodology, based on an analytical hierarchy process, is applied. In contrast to the previous studies, in which the spatial analysis was undertaken by a GIS managing different layers of information (grids, shapefiles, etc.), in this work the spatial analysis is carried out by means of an open-code spatial database management system: PostgreSQL-PostGIS. This system uses Structure Query Language to manage different tables in the context of relational spatial databases. The case study is the province of Seville (southern Spain), where this sort of facility already exists. The empirical analysis concludes that a large percentage of the province of Seville has an excellent potential for the installation of photovoltaic plants. The methodology allows the dynamic updating of criteria and parameters, as well as the reproducibility, scalability and automating of analyses carried out in other fields.

Research on the spatial database management system of water resources in a river basin

Research on the spatial database management system of water resources in a river basin

SPATIAL DMBS ARCHITECTURE FOR A FREE AND OPEN SOURCE BIM

Abstract. Recent research on the field of Building Information Modelling (BIM) technology, revealed that except of a few, accessible and free BIM viewers there is a lack of Free & Open Source Software (FOSS) BIM software for the complete BIM process. With this in mind and considering BIM as the technological advancement of Computer-Aided Design (CAD) systems, the current work proposes the use of a FOSS CAD software in order to extend its capabilities and transform it gradually into a FOSS BIM platform. Towards this undertaking, a first approach on developing a spatial Database Management System (DBMS) able to store, organize and manage the overall amount of information within a single application, is presented.

Open Polar Server (OPS)—An Open Source Infrastructure for the Cryosphere Community

The Center for Remote Sensing of Ice Sheets (CReSIS) at the University of Kansas has collected approximately 1000 terabytes (TB) of radar depth sounding data over the Arctic and Antarctic ice sheets since 1993 in an effort to map the thickness of the ice sheets and ultimately understand the impacts of climate change and sea level rise. In addition to data collection, the storage, management, and public distribution of the dataset are also primary roles of the CReSIS. The Open Polar Server (OPS) project developed a free and open source infrastructure to store, manage, analyze, and distribute the data collected by CReSIS in an effort to replace its current data storage and distribution approach. The OPS infrastructure includes a spatial database management system (DBMS), map and web server, JavaScript geoportal, and MATLAB application programming interface (API) for the inclusion of data created by the cryosphere community. Open source software including GeoServer, PostgreSQL, PostGIS, OpenLayers, ExtJS, GeoEXT and others are used to build a system that modernizes the CReSIS data distribution for the entire cryosphere community and creates a flexible platform for future development. Usability analysis demonstrates the OPS infrastructure provides an improved end user experience. In addition, interpolating glacier topography is provided as an application example of the system.

Micro Hydro Potential Modelling: Integrating GIS into Energy Alternatives for Climate Change Mitigation

This research focused on integrating GIS into energy alternatives for climate change mitigation by creating a GIS-based hydrologic model that can be used to identify sites that have significant potential for micro hydropower development within the River Perkerra catchment area. Hydropower is a clean and renewable energy source that remains largely untapped in the country and its development can be used to mitigate anthropogenic climate change by reducing reliance on fossil or biomass derived fuels. This research established the extent of this resource and whether the available sites with significant micro hydropower potential within the study area were amply copious to warrant further development. Currently, such identification is done physically using means that are menial, costly and significantly time consuming. A 90-metre resolution Digital Terrain Model (DTM) data obtained from the Shuttle Radar Topography Mission and various GIS tools were used to create a hydrologic framework which was used to identify potential sites along River Perkerra that suited any desired head requirement for the purposes of locating micro hydropower plants. The derived model demonstrated that it was possible to identify sites at discrete geographic locations along any stream drainage network using GIS. In addition, the model also provides a decision support system that integrates a powerful graphical user interface, spatial database management system and a generalized river basin network flow model for the purposes of exploiting and developing micro hydropower. With sufficient data on catchment discharge and use of higher resolution DTM, the model can be further enhanced to accurately obtain the total microhydro potential of River Perkerra by aggregating the respective potentials of every steam segment.

Integration of Spatial and Descriptive Information to Solve the Urban Waste Accumulation Problem: A Pilot Study

The paper reports about a pilot study that gives a numerical solution to the solid waste accumulation problem (SWAP). The purpose is to show both a simple and effective way to implement the theory using the technology of the Spatial DataBase Management Systems (SDBMSs), and the versatility of the proposed solution from the point of view of those responsible for the MSW management who, in fact, are offered a dual-mode display of the results: one tabular (typical of relational databases) and the other based on geographical maps, the latter particularly useful to highlight the spatial component of the data of the SWAP.

3-D Geovisualization of satellite images on smart devices by the integration of spatial DBMS, RESTful API and WebGL

Recent technological advancements in web-based geographic information systems have enabled access to satellite images on smart devices. The Representational State Transfer (REST) architecture overcomes difficulties that are associated with conventional data communications on the web, and the Web Graphics Library (WebGL) can be used as an alternative to web-based three-dimensional geographic visualization (3-D geovisualization) due to its efficient image processing capabilities. This paper describes a 3-D geovisualization system that was developed for satellite images on smart devices by integrating a spatial database management system (DBMS), a RESTful application programming interface (API), and WebGL. Spatiotemporal objects were constructed for time-series satellite images within a DBMS and a RESTful API was built for spatiotemporal queries to the time-series database so that the requested satellite data could be represented in 3-D on smart devices using WebGL. Satellite images that are represented in WebGL give a more realistic 3-D experience when they are combined with terrain data and provide for intuitive observations of the relationships between pixel values and associated geospatial conditions. This paper shows that a creative combination of existing technologies can be used to enhance and display satellite images on smart devices for 3-D geovisualization.