Integration of spatial database systems and sampling-based path planning for optimizing maritime navigation

Spatial database systems and Geographic Information Systems (GIS) are mainly able to support geographical applications that deal with crisp spatial objects , that is, objects whose extent, shape, and boundary are precisely determined. But geoscientists have pointed out for a long time that there is also a need to represent fuzzy spatial objects that reveal an intrinsically vague or blurred nature and structure and feature indeterminate boundaries and/or interiors. A spatial object is fuzzy if locations exist that cannot be assigned completely to the object or to its complement. In this article, we propose an abstract, formal, and conceptual type system called Fuzzy Spatial Algebra ( FUSA ) that provides a collection of fuzzy spatial data types for fuzzy points , fuzzy lines , and fuzzy regions in the two-dimensional Euclidean space. We introduce a set of expressive spatial operations such as fuzzy union , fuzzy intersection , and fuzzy difference to perform geometric computations on fuzzy spatial objects. As a specialty, users may exert influence on how spatial fuzziness is interpreted and handled in these operations. Our formal framework is based on fuzzy set theory and fuzzy topology. FUSA is designed to serve as a specification of its implementation in a spatial database and GIS context. We show the applicability of FUSA and its possible embedding into the query languages of extensible database systems by employing a running example.

Carbon capture and storage (CCS) is widely recognized as a significant technology in mitigating carbon dioxide (CO2) emissions from major industrial facilities, such as power plants and refineries. CCS involves the capture of concentrated CO2 streams from point sources, followed by subsequent safe and secure storage in appropriate geological reservoirs. We developed spatial database system using Geographic Information System (GIS) tools to facilitate source-sink matching between CO2 emitter and CO2 storage to foster the implementation of CCS/CCUS technologies in Indonesia. In this study, we proposed workflow approach to determine the location of CO2 sinks/storage candidates given limited data available. Additionally, this method spatially characterizes and represents probable clusters where opportunities for CCS/CCUS implementation are present. We consider the existing pipeline route and Right of Ways (ROW) to minimize the potential cost related to transportation of CO2 using pipeline. The priority of available storage is classified based on the storage capacity, distance, and other technical criteria to determine the optimal location of potential CO2 injection. We applied the workflow to Coal Fired Power Plant in West Java as the CO2 source, and we obtained 6 depleted fields that are connected to the existing ROW with CO2 storage capacity of 42.03 MMT.

Development of Spatial Database System Based on Cloud Computing Remote Sensing for Monitoring of Oil Palm Plantation in Indonesia

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.

Abstract Spatial libraries are core components in many geographic information systems, spatial database systems, and spatial data science projects. These libraries provide the implementation of spatial type systems that include spatial data types and a large diversity of geometric operations. Their focus relies on handling crisp spatial objects, which are characterized by an exact location and a precisely defined extent, shape, and boundary in space. However, there is an increasing interest in analyzing spatial phenomena characterized by fuzzy spatial objects, which have inexact locations, vague boundaries, and/or blurred interiors. Unfortunately, available spatial libraries do not provide support for fuzzy spatial objects. In this article, we describe the R package named fsr, which is based on the Spatial Plateau Algebra and is publicly available at https://cran.r‐project.org/package=fsr. Our tool provides methods for building fuzzy spatial objects as spatial plateau objects and conducting exploratory spatial data analysis by using fuzzy spatial operations.

Many applications rely on spatial information retrieval, which involves costly computational geometric algorithms to process spatial queries. Spatial approximations simplify the geometric shape of complex spatial objects, allowing faster spatial queries at the expense of result accuracy. In this sense, spatial approximations have been employed to efficiently reduce the number of objects under consideration, followed by a refinement step to restore accuracy. For instance, spatial index structures employ spatial approximations to organize spatial objects in hierarchical structures (e.g., the R-tree). It leads to the interest in studying how spatial approximations can be efficiently employed to improve spatial query processing. This article presents a systematic review on this topic. We gather relevant studies by performing a search string on several digital libraries. We further expand the studies under consideration by employing a single iteration of the snowballing approach, where we track the reference list of selected papers. As a result, we provide an overview and comparison of existing approaches that propose, evaluate, or make use of spatial approximations to optimize the performance of spatial queries. The spatial approximations mentioned by the approaches are also summarized. Further, we characterize the approaches and discuss some future trends.

Long-term vehicular motion prediction is a crucial function for both autonomous driving and advanced driver-assistant systems. However, due to the uncertainties of vehicle dynamics and complexities of surroundings, long-term motion prediction is never trivial work. As they combine effects of humans, vehicles and environments, kinematic trajectory data reflect several aspects of vehicles’ spatial behaviors. In this paper, we propose a novel method that leverages spatial database and kinematic trajectory data to achieve long-term vehicular motion prediction in a lightweight way. In our system, a spatial database system is initially embedded in an extended Kalman filter (EKF) framework. The spatial kinematic trajectory data are managed through the database and directly used in motion prediction; namely, weighted means are derived from the spatially retrieved kinematic data and used to update EKF predictions. The proposed method is validated in the real world. The experiments indicate that different weighting methods make a slight accuracy difference. Our method is not data-and-computation-consumed; its performance is acceptable in the limited data conditions and its prediction accuracy is improved as the size of used data sets increases; our method can predict in real time. The efficiency of an unscented Kalman filter (UKF) is compared with that of the EKF. The results show that the UKF can hardly meet real-time requirements.

Although graph theory has already been introduced in spatial reasoning, current spatial database systems do not provide out-of-the-box routing on geometric points that are not matched on the graph. Methods that connect new reference locations to the graph render different routing results. Moreover, current solutions break reasoning down to local analysis. We bridge the gap between routing networks and spatial geometry by a global matching of geometric points to routing networks.

A smart city is a concept of urban development that requires different technologies to integrate all city elements into a sustainable city system. Land administration, including three-dimensional (3D) cadaster and planning, is a pre-condition for having a smart city. Land administration in the smart city will be more attractive when the city has a cultural heritage area that must be preserved for economic, social, and territory benefits. This paper describes the development of a multipurpose land administration system prototype of a city, especially in the cultural heritage area. The first activity of this development is to create a 3D city map for documentation and management of cities, especially for cultural heritage areas, and involve the role of the community in participatory mapping. The participatory mapping method is used to form a more detailed 3D building model using simple techniques for measuring the room distance on a building. Then, the 3D city model is stored in a spatial database and management system to visualize, analyze, and manage the data. This research uses the complex area of Kasepuhan Palace, Cirebon City, West Java, Indonesia, as a case study. That area is a cultural heritage area with complicated objects and unique information to document.

Geometric similarity plays an important role in geographic information retrieval, map matching, and data updating. Many approaches have been developed to calculate the similarity between simple features. However, complex group objects are common in map and spatial database systems. With a micro scene that contains different types of geographic features, calculating similarity is difficult. In addition, few studies have paid attention to the changes in a scene’s geometric similarity in the process of generalization. In this study, we developed a method for measuring the geometric similarity of micro scene generalization based on shape, direction, and position. We calculated shape similarity using the hybrid feature description, and we constructed a direction Voronoi diagram and a position graph to measure the direction similarity and position similarity. The experiments involved similarity calculation and quality evaluation to verify the usability and effectiveness of the proposed method. The experiments showed that this approach can be used to effectively measure the geometric similarity between micro scenes. Moreover, the proposed method accounts for the relationships amongst the geometrical shape, direction, and position of micro scenes during cartographic generalization. The simplification operation leads to obvious changes in position similarity, whereas delete and merge operations lead to changes in direction and position similarity. In the process of generalization, the river + islands scene changed mainly in shape and position, the similarity change in river + lakes occurred due to the direction and location, and the direction similarity of rivers + buildings and roads + buildings changed little.

Land-use change for examining the expansion of built-up and recreation, required effective techniques of spatial assessment, especially in areas with limited space such as Koh Chang island in Thailand which needed to be emphasized. The research objectives were to study land-use patterns in Koh Chang area in Trat province from 2000-2020, and study land-use change, especially the expansion of buildings and recreation area during that period, using geo-informatic technique. The study found that most of Koh Chang is forest land, up to 80% of the island, but the trend is declining. On the other hand, the area that has increased in number is built-up and recreation, which has increased from 7.22 km2 to 18.28 km2 and up to 253.19% in the past 20 years. The efficiency of geo-informatic technology can extract useful information, especially spatial data on land-use change. Therefore, it is known from which areas built-up and recreation areas are transformed in order to bring such information into a spatial database system for supporting decision-making in directing, monitoring and controlling areas for further expansion of tourism business in order not to create an impact on the environment.

Abstract. Products and outputs for water quality monitoring are made available in the IM4ManilaBay MapABLE Web Portal, a web-based GIS platform that serves as a repository of processed maps for Manila Bay and linked systems. The Portal is also linked with the MASDAN mobile application that has been developed to allow the public to report different environmental issues. The web development involved the utilization of geospatial content management system, spatial database system, operating systems, programming, and style language. Services included frontend and backend development. The Web Portal serves as a repository for all outputs derived for water quality monitoring. It contains processed results, technical overview on the developed models and tools, historical data viewing and use of basic geoprocessing tools, and viewing of reported incidents from the MASDAN app. It is expected that the Web Portal will contribute significantly in the efforts of communities and environmental agencies to implement policies for sustainable development and management of Manila Bay and its watershed.

Support for efficient spatial data storage and retrieval has become a vital component in almost all spatial database systems. While GPUs have become a mainstream platform for high-throughput data processing in recent years, exploiting the massively parallel processing power of GPUs is non-trivial. Current approaches that parallelize one query at a time have low work efficiency and cannot make good use of GPU resources. On the other hand, many spatial database systems could receive a large number of queries simultaneously. In this paper, we present a comprehensive framework named G-PICS for parallel processing of concurrent spatial queries on GPUs. G-PICS encapsulates efficient parallel algorithms for constructing a variety of spatial trees with different space partitioning methods. G-PICS also provides highly optimized programs for processing major spatial query types, and such programs can be accessed via an API that could be further extended to implement user-defined algorithms. While support for dynamic data inputs is missing in existing work, G-PICS implements efficient parallel algorithms for bulk updates of data. Furthermore, G-PICS is designed to work in a Multi-GPU environment to support datasets beyond the size of a single GPU’s global memory. Empirical evaluation of G-PICS shows significant performance improvement over the state-of-the-art GPU and parallel CPU-based spatial query processing systems. In particular, G-PICS achieves double-digit speedup over such systems in tree construction (up to 53X) and query processing (up to 80X).

BackgroundThe exponential accumulation of environmental and ecological data together with the adoption of open data initiatives bring opportunities and challenges for integrating and synthesising relevant knowledge that need to be addressed, given the ongoing environmental crises.FindingsHere we present Biospytial, a modular open source knowledge engine designed to import, organise, analyse and visualise big spatial ecological datasets using the power of graph theory. The engine uses a hybrid graph-relational approach to store and access information. A graph data structure uses linkage relationships to build semantic structures represented as complex data structures stored in a graph database, while tabular and geospatial data are stored in an efficient spatial relational database system. We provide an application using information on species occurrences, their taxonomic classification and climatic datasets. We built a knowledge graph of the Tree of Life embedded in an environmental and geographical grid to perform an analysis on threatened species co-occurring with jaguars (Panthera onca).ConclusionsThe Biospytial approach reduces the complexity of joining datasets using multiple tabular relations, while its scalable design eases the problem of merging datasets from different sources. Its modular design makes it possible to distribute several instances simultaneously, allowing fast and efficient handling of big ecological datasets. The provided example demonstrates the engine’s capabilities in performing basic graph manipulation, analysis and visualizations of taxonomic groups co-occurring in space. The example shows potential avenues for performing novel ecological analyses, biodiversity syntheses and species distribution models aided by a network of taxonomic and spatial relationships.

Groundwater is the least known resource in terms of volume, location, quality, and spatial distribution. This problem is further aggravated in Ethiopia due to a lack of a groundwater spatial database system. The study was conducted in Angolelana Tera Woreda which covers a total area of 782.48 km2. The main objective of this study was to identify and delineate groundwater potential areas by using Geographic Information Systems (GIS) and Remote Sensing (RS) techniques. Multi-criteria decision analysis method in GIS environment was employed to identify groundwater potential zone. To do this, the geological, geomorphologic, slope, soil, lineament, land use/land cover and drainage density data were used. By integrating these data in GIS environment, therefore, groundwater potential zones were identified, and the output was classified as very high, high, moderate, low and very low potential zones. Based on the result, 109.2 km2 (12.9%), 129.53 km2 (16.55%), 435.52 km2 (55.66%), 63.28 km2 (8.09%) and 53.23 km2 (6.8%) of the study area has very high, high, moderate, low and very low groundwater potential, respectively. It can be concluded that relatively the study area has high groundwater potential. So in order to utilize this important resource for different uses, high resolution groundwater potential map and spatial based groundwater analysis techniques should be required. Therefore, the woreda administrator bodies and other concerned stakeholders who might utilize and manage the available groundwater resource of the study area should use this research output as an input to develop an overall master plan for groundwater exploitation and management.

The use of a spatial index is a common strategy to improve the performance of spatial queries in spatial database systems and Geographic Information Systems. Choosing the right spatial index to be employed in a given context requires a quantitative method to analyze the performance of spatial indices. This is done through extensive experimental evaluations. However, conducting these evaluations is an expensive, error-prone, and challenging task because (i) spatial objects are complex data to manage, (ii) spatial indices can apply different parameter values and thus assume distinct configurations, and (iii) there are indices specifically developed for different storage systems, such as disks and flash memories. In this article, we propose FESTIval, a versatile framework for conducting experimental evaluations of spatial indices. FESTIval has the following main advantages:•the support for different types of disk-based and flash-aware spatial indices;•the specification and execution of user-defined workloads;•the use of a data schema that stores index configurations and statistical data of executed workloads.Because of its characteristics, FESTIval allows users to reproduce executed experiments. Further, FESTIval provides an extensible environment, where any spatial dataset can be handled by spatial indices. FESTIval has been used to validate new proposals of flash-aware spatial indices, such as eFIND-based indices.

The spatial join is a popular operation in spatial database systems and its evaluation is a well-studied problem. This paper reviews research and recent trends on spatial join evaluation. The complexity of different data types, the consideration of different join predicates, the use of modern commodity hardware, and support for parallel processing open the road to a number of interesting directions for future research, some of which we outline in the paper.

هدفت الدراسة إلى استخلاص شبكة المجاري المائية باستخدامGIS في محافظة بيت لحم، وتقييم مخاطر السيول وأكثر المناطق عرضة للفيضان في المحافظة وذلك من أجل التنبؤ والحد من مخاطر الكوارث التي تتعلق بالسيول، مع بيان دور GIS في عمل التحليل الهيدرولوجي من أجل الاستفادة من مياه السيول عن طريق تحديد المناطق الأمثل لتجميع مياه السيول. وأظهرت استخلاص خارطة شبكة الأودية وتحديد مجاري الرتب المائية والأحواض النهرية باستخدام GIS، وبينت قدرة نظم المعلومات الجغرافية على تقييم المخاطر الخاصة بالسيول وكذلك إنتاج خرائط دقيقة وتوضيحية تبين مواقع الخطر. واستعرضت دور GIS في صناعة الفرصة والحد من مخاطر الكارثة من خلال الاستفادة من السيول وتحديد أفضل المناطق المقترحة لتجمع مياه الأمطار ومن ثم العمل على تخزينها. وتوصي بإنشاء بنك معلومات وطني تختص كل جهة مسئولة عن عمل جزء من المعلومات ليتكون نظام قواعد بيانات مكاني يحتوي على خرائط شاملة في جميع المجالات ضمن معايير مشتركة، تساعد في إدارة الكوارث وتدعم اتخاذ القرارات وقت الأزمات والكوارث باستخدامGIS

Boundary representation models are data models that represent the topology of a building or city model. This leads to an issue in combination with geometry, as the geometric model necessarily has an underlying topology. In order to allow topological queries to rely on the incidence graph only, a new notion of topological consistency is introduced that captures possible topological differences between the incidence graph and the topology coming from geometry. Intersection matrices then describe possible types of topological consistency and inconsistency. As an application, it is examined which matrices can occur as intersection matrices, and how matrices from topologically consistent data look. The analysis of CityGML data sets stored in a spatial database system then shows that many real-world data sets contain many topologically inconsistent pairs of polygons. It was observed that even if data satisfy the val3dity test, they can still be topologically inconsistent. On the other hand, it is shown that the ISO 19107 standard is equivalent to our notion of topological consistency. In the case when the intersection is a point, topological inconsistency occurs because a vertex lies on a line segment. However, the most frequent topological inconsistencies seem to arise when the intersection of two polygons is a line segment. Consequently, topological queries in present CityGML data cannot rely on the incidence graph only, but must always make costly geometric computations if correct results are to be expected.