Spatial Data Management Research Articles

Advanced spatial query processing in IoT through mobile agent integration

In IoT environments, various applications and protocols rely on spatial query processing to obtain, analyze, and interpret information from sensor nodes based on their location within a specific area. It enables the efficient monitoring and management of spatial data, which is crucial for applications such as environmental monitoring, smart cities, and disaster response. By querying the location and status of sensor nodes, spatial query processing facilitates real-time decision-making, optimizes resource allocation, and enhances the overall performance of the IoT system.This paper focuses on Window queries, a key spatial query type that retrieves data from sensors within a defined two-dimensional zone. The research aims to improve spatial query processing in IoT environments by proposing a Window query processing method that balances various performance factors such as energy efficiency, latency, accuracy, and query success rate. We introduce a novel approach using mobile agents to enhance spatial query processing by addressing the constraints of sensor networks. Simulation results from the NS-2 simulator demonstrate the effectiveness of our method across different parameters and environments.

ML Approaches for the Study of Significant Heritage Contexts: An Application on Coastal Landscapes in Sardinia

Remote Sensing (RS) and Geographic Information Science (GIS) techniques are powerful tools for spatial data collection, analysis, management, and digitization within cultural heritage frameworks. Despite their capabilities, challenges remain in automating data semantic classification for conservation purposes. To address this, leveraging airborne Light Detection And Ranging (LiDAR) point clouds, complex spatial analyses, and automated data structuring is crucial for supporting heritage preservation and knowledge processes. In this context, the present contribution investigates the latest Artificial Intelligence (AI) technologies for automating existing LiDAR data structuring, focusing on the case study of Sardinia coastlines. Moreover, the study preliminary addresses automation challenges in the perspective of historical defensive landscapes mapping. Since historical defensive architectures and landscapes are characterized by several challenging complexities—including their association with dark periods in recent history and chronological stratification—their digitization and preservation are highly multidisciplinary issues. This research aims to improve data structuring automation in these large heritage contexts with a multiscale approach by applying Machine Learning (ML) techniques to low-scale 3D Airborne Laser Scanning (ALS) point clouds. The study thus develops a predictive Deep Learning Model (DLM) for the semantic segmentation of sparse point clouds (<10 pts/m2), adaptable to large landscape heritage contexts and heterogeneous data scales. Additionally, a preliminary investigation into object-detection methods has been conducted to map specific fortification artifacts efficiently.

SGIR-Tree: Integrating R-Tree Spatial Indexing as Subgraphs in Graph Database Management Systems

Efficient spatial query processing in Graph Database Management Systems (GDBMSs) has become increasingly important owing to the prevalence of spatial graph data. However, current GDBMSs lack effective spatial indexing, causing performance issues with complex spatial graph queries. This study proposes a spatial index called Subgraph Integrated R-Tree (SGIR-Tree) for efficient spatial query processing in GDBMSs. The SGIR-Tree integrates the hierarchical R-Tree structure with the graph structure of GDBMSs by converting R-Tree elements into graph components like nodes and edges. The Minimum Bounding Rectangle (MBR) information of spatial objects and R-Tree nodes is stored as properties of these graph elements, and the leaf nodes are directly connected to the spatial nodes. This approach combines the efficiency of spatial indexing with the flexibility of graph databases, thereby allowing spatial query results to be directly utilized in graph traversal. Experiments using OpenStreetMap datasets demonstrate that the SGIR-Tree outperforms the previous approaches in terms of query overhead and index overhead. The results are expected to improve spatial graph data processing in various fields, including location-based service and urban planning, significantly advancing spatial data management in GDBMSs.

Elevating Tanzania's Tourism: Integrating GIS, AR and AI for Immersive Exploration and Promotion

This study presents a comprehensive examination of the integration of Geographic Information Systems (GIS), Augmented Reality (AR) and Artificial Intelligence (AI) in tourism promotion in Tanzania. The literature review underscores the significance of these technologies in enhancing visitor experiences, destination management, and marketing strategies. The proposed integrated system design combines GIS's spatial mapping capabilities, AI's personalised recommendations, and AR's immersive content delivery to optimise tourist satisfaction and engagement. Key components include the GIS module for spatial data management, the AI recommendation engine for personalised suggestions, and the AR interface for immersive content overlay. Discussions highlight how the proposed system, by addressing critical challenges in the tourism sector, aligns with existing research findings and reassures its effectiveness. Ultimately, the study emphasises the potential of GIS, AR and AI technologies to revolutionise tourism promotion in Tanzania, fostering sustainable growth and cultural appreciation while enhancing visitor experiences.

Evaluating the impact of knowledge management and database management on decision-making process: A case study of subsea project services

The oil and gas industry is known for its rapid technological advancements and the complexity of its operations, increasingly relying on data-intensive management. As subsea projects—from exploration to decommissioning—become more complex and data-driven, integrating knowledge management (KM) and database management systems (DBMS) has become essential. The research specifically explores how KM and DBMS contribute to decision-making, utilizing a quantitative methodology through a questionnaire survey. Findings reveal that KM processes significantly improve the effectiveness of non-spatial data management, highlighting KM's crucial role in facilitating technology adoption and operational efficiency. However, the influence of KM on spatial data management and overall decision-making is found to be limited, indicating the necessity for more adaptive and integrated KM strategies that serve the unique requirements of database systems. This study underscores the critical but nuanced role of KM and DBMS in the oil and gas industry, advocating for tailored KM strategies to optimize database management. It suggests future research into the interplay of KM with emerging technologies like AI and machine learning to enhance database management. This investigation stresses the importance of holistic KM practices in effectively managing the intricate data landscapes of subsea project services, thereby contributing to the broader discourse on KM, DBMS, and decision-making in complex settings.

Spatial Data Handling System in The Nasai Village Area, South Papua

Merauke Regency is one of the districts in the South Papua Province, geographically located between 1370 - 1410 East Longitude and 50 - 90 South Latitude. With an area of ​​up to 45,013.35 km2 or 38.2 percent of the total area of ​​South Papua Province, making Merauke Regency the largest district not only in South Papua Province but also among other districts in Indonesia. Administratively, Merauke Regency has 22 districts. With the expansion of new provincial territories, Merauke Regency became part of South Papua Province along with Boven Digoel, Asmat and Mappi Regencies. This expansion is based on Law No. 14 of 2022. Merauke has 22 districts/sub-districts and 190 villages. The existence of 190 villages means that spatial data collection is still considered a normal thing, even though spatial data collection in an area is very important, especially with current technological advances. Accurate and integrated management of spatial data is important in planning and decision making in a region, especially in the Nasai Village area, South Papua. This research aims to examine a comprehensive spatial data handling system in this area, using Geographic Information System (GIS) technology as a tool. The methods used in this research include identifying needs and problems related to spatial data, collecting spatial and non-spatial data, as well as designing an integrated geographic information system. It is hoped that the results of this research will provide an overview of the current condition of spatial data management in Nasai Village and provide system recommendations that can increase the efficiency and effectiveness of geographic information management in the area.

Analysis for an open-source library in database management systems

AbstractSpatial data management is crucial for applications like urban planning and environmental monitoring. While traditional relational databases are commonly used, they struggle with large and complex spatial data. NoSQL databases provide support for unstructured data and scalability. This article compares the performance and disk space usage of SQL Server (a relational database) and MongoDB (NoSQL database) using an open-source library. Experiments conducted with the OpenStreetMap dataset from Central America show that the MongoDB database outperformed the relational SQL Server database in most cases, offering practical advantages for spatial data management in Geographic Information System applications.

Oil Palm Bunch Ripeness Classification and Plantation Verification Platform: Leveraging Deep Learning and Geospatial Analysis and Visualization

Oil palm cultivation thrives as a prominent agricultural endeavor within the southern region of Thailand, where the country ranks third globally in production, following Malaysia and Indonesia. The assessment of oil palm bunch ripeness serves various purposes, notably in determining purchasing prices, pre-harvest evaluations, and evaluating the impacts of disasters or low market prices. Presently, two predominant methods are employed for this assessment, namely human evaluation, and machine learning for ripeness classification. Human assessment, while boasting high accuracy, necessitates the involvement of farmers or experts, resulting in prolonged processing times, especially when dealing with extensive datasets or dispersed fields. Conversely, machine learning, although capable of accurately classifying harvested oil palm bunches, faces limitations concerning its inability to process images of oil palm bunches on trees and the absence of a platform for on-tree ripeness classification. Considering these challenges, this study introduces the development of a classification platform leveraging machine learning (deep learning) in conjunction with geospatial analysis and visualization to ascertain the ripeness of oil palm bunches while they are still on the tree. The research outcomes demonstrate that oil palm bunch ripeness can be accurately and efficiently classified using a mobile device, achieving an impressive accuracy rate of 99.89% with a training dataset comprising 8779 images and a validation accuracy of 96.12% with 1160 images. Furthermore, the proposed platform facilitates the management and processing of spatial data by comparing coordinates derived from images with oil palm plantation data obtained through crowdsourcing and the analysis of cloud or satellite images of oil palm plantations. This comprehensive platform not only provides a robust model for ripeness assessment but also offers potential applications in government management contexts, particularly in scenarios necessitating real-time information on harvesting status and oil palm plantation conditions.

GWmodelS: a standalone software to train geographically weighted models

ABSTRACT With the recent increase in studies on spatial heterogeneity, geographically weighted (GW) models have become an essential set of local techniques, attracting a wide range of users from different domains. In this study, we demonstrate a newly developed standalone GW software, GWmodelS using a community-level house price data set for Wuhan, China. In detail, a number of fundamental GW models are illustrated, including GW descriptive statistics, basic and multiscale GW regression, and GW principle component analysis. Additionally, functionality in spatial data management and batch mapping are presented as essential supplementary activities for GW modeling. The software provides significant advantages in terms of a user-friendly graphical user interface, operational efficiency, and accessibility, which facilitate its usage for users from a wide range of domains.

Analysis of the Current Status of Sensors and HBIM Integration: A Review Based on Bibliometric Analysis

This article presents a bibliometric analysis conducted on the implementation of sensor-extracted data in the context of Historical Building Information Modeling (HBIM) with a focus on the conservation of heritage buildings. The study utilized recognized databases, namely, Web of Science (WoS) and Scopus. A bibliometric analysis methodology was applied, employing the VOSviewer 1.6.19 software. The results obtained provide a robust overview of the current state and trends in HBIM and sensor research and enabled the identification of emerging trends in the field of preventive conservation. Three promising trends were identified, establishing connections between this field and 4D spatial data management, energy performance, and artificial intelligence. Additionally, relations with climate change were also detected. These trends underscore a significant convergence of technologies and methodologies in the realm of heritage building conservation. The conclusions serve as a valuable contribution to guiding future research in the field of built heritage conservation.

Результаты сравнения длин линий на базисе пространственном эталонном СГУГиТ

The results of line lengths comparison on the spatial reference basis (SRB) of SGUGiT, carried out according to the program in all combinations measurements in the forward direction from point BI01 to point BI10 in spring and summer of 2021 and 2023 using total station Leica TM30, as well as, in the same seasons of 2023 done by laser tracker Leica AT403 are given. Based on the analysis of multiple measurements of each section of the basis (at least 30 ones), the standard deviation and uncertainty were calculated. Meanwhile, at the extreme points of the base section, meteorological parameters (temperature, pressure and relative humidity) were recorded by two independent instruments, which were then entered immediately when metering with a Leica TM 30 electronic total station. In the case of the Leica AT403 laser tracker, they were automatically taken into account in specialized software management and processing of Spatial Analyzer measurement data. Due to the research results, it was found out that the expanded uncertainty in measuring the lengths of the basic lines in the range from 12 to 192 m on the WPT using a Leica AT403 laser tracker and a Leica TM30 electronic total station, taking into account the accepted errors that affect the final result, is no more than 1,4 mm, and the maximum contribution to the mistake in determining line lengths is made by the standard uncertainty estimated by type B. In addition, seasonal fluctuations in the position of individual basis points are observed

DEVELOPING 4D MALAYSIAN MARINE CADASTRE DATA MODEL BASED ON LADM - PRELIMINARY WORKS

Abstract. The complex interactions between terrestrial and aquatic dimensions make effective land management in coastal and marine settings particularly difficult. For thorough spatial data administration, it has been acknowledged that the traditional Land Administration Domain Model (LADM) must incorporate temporal and three-dimensional (3D) elements. The main goal of this work is to develop a 4D Malaysian marine cadastre data model based on LADM that integrates the temporal dimensions (3D + T= 4D) to the current 3D Malaysian marine cadastre data model and to assess how well the extended model handles issues with the representation of 3D marine environment. The depiction and management of temporal data related to shifting coastlines, fluctuating tides, and seasonal fluctuations in the maritime environment are some of the key questions investigated. The findings of this study are expected to provide valuable insights for augmenting land administration methodologies in dynamic marine settings. The outcomes of this research hold significance for sustainable marine development and effective spatial data management in coastal regions.

Systems Strategic Planning with the Ward and Peppard Framework (Case Study of Research Data and Innovation in Magelang City)

Law Number 23 of 2014 encourages innovation in the administration of the Regional Government to improve performance and public services towards community welfare. The development of Regional Innovation in Magelang City involves the Government, the Community, Local Government Organizations (OPD), and Higher Education. The Regional Development Planning Agency of Magelang City (BAPPEDA) strives to facilitate innovation development from both the community and OPD. However, several issues have been encountered, such as (1) Less (2) Conventional optimal management of spatial data, (3) Difficulty in accessing research data due to a challenging database, (4) scattered government innovation data within each OPD, and (5) the Department of Investment and Integrated One-Stop Services (DPMPTSP), as the research permit agency, has not followed up on the research implementation process. To address these issues, a study focused on information strategic planning IS/IT using the Ward and Peppard Framework as a planning strategy for managing research and innovation data in Magelang City. The research revealed that using Value Chain Analysis identified primary and support activities. Additionally, Fishbone Analysis highlighted factors underlying the importance of information strategic planning IS/IT in the Machine, Measurement, and Method categories. The study’s outcome includes a recommendation to utilize Pieces Analysis.

Designing PostGIS Database System with Fuzzy Theory to Support Accessibility Tools for Urban Pedestrians

The combination of fuzzy logic and crowdsourcing can be a powerful tool for generating geospatial data for pedestrians with mobility challenges in urban areas. Although potentially useful, information about the accessibility of paths that is generated through crowdsourcing is susceptible to a high degree of imprecision. Spatial data management is required for such systems, which supports the management of uncertain data. Fuzzy theory allows us to model ambiguous information. To fill this gap, an improved method based on a fuzzy relational PostGIS database (FPostGIS) is proposed. The method includes extensions to represent imprecise data within an entity-relationship (ER) data model specifically tailored for path accessibility, and a set of steps for the derivation of FPostGIS from this extended ER model. According to the case study, this methodology has been applied in the design and development of decision support application within the Maps for Easy Paths (MEP) project. This application stores and retrieves accessibility information about a particular path and allows performing spatial operations and analysis inside the database.

Implementation of digital gis models in desktop applications developed on the basis of the «QT» framework

This article explores the implementation of digital geoinformation systems (GIS) models in desktop applications using the Qt framework. This highlights the importance of GIS technologies in various fields and the need for powerful desktop applications capable of efficiently processing spatial data. Integration of GIS models into Qt-based applications provides a number of advantages, such as efficient data management, spatial analysis and user-friendly interfaces. The article highlights the cross-platform compatibility of the Qt framework, which allows you to deploy GIS-enabled applications on multiple operating systems. In conclusion, it should be noted that the introduction of digital GIS models into desktop applications developed using the Qt framework provides significant benefits to both developers and users. This allows users to access, analyze and visualize geospatial data, thereby improving decision-making processes. The combination of GIS and Qt capabilities allows developers to create reliable and user-friendly applications to meet the growing demand for spatial data management and analysis tools. The list of sources used includes links to the official Qt and Esri websites, as well as relevant publications related to integration, GIS and noise mapping using ArcGIS.

Modeling and Evaluation of the Susceptibility to Landslide Events Using Machine Learning Algorithms in the Province of Chañaral, Atacama Region, Chile

Landslides represent one of the main geological hazards, especially in Chile. The main purpose of this study is to evaluate the application of machine learning algorithms (SVM, RF, XGBoost and logistic regression) and compare the results for the modeling of landslide susceptibility in the province of Chañaral, III region, Chile. A total of 86 sites are identified using various sources, in addition to 86 non-landslide sites. This spatial data management and analysis are conducted using QGIS software. The sites are randomly divided, and then a cross-validation process is applied to calculate the accuracy of the models. After that, from 22 conditioning factors, 12 are chosen based on the information gain ratio (IGR). Subsequently, five factors are excluded by the correlation criterion. After this analysis, two indices not previously utilized in the literature, the NDGI (normalized difference glacier index) and EVI (enhanced vegetation index), are employed for the final model. The performance of the models is evaluated through the area under the ROC (receiver operating characteristic) curve (AUC). To study the statistical behavior of the model, the Friedman nonparametric test is performed to compare the performance with the other algorithms and the Nemenyi test for pairwise comparison. Of the algorithms used, RF (AUC = 0.957) and XGBoost (AUC = 0.955) have the highest accuracy values measured in AUC compared to the other models and can be used for the same purpose in other geographic areas with similar characteristics. The findings of this investigation have the potential to assist in land use planning, landslide risk reduction, and informed decision making in the surrounding zones.

Researching and applying open-source WebGIS technology to build national-standard vector geographic database management software

Geographical database management is a crucial component of spatial data infrastructures (SDIs) and is used by government organizations in provinces, regions, and countries. It plays an important role, especially during the 4.0 technology revolution and digital transformation to build e-government and digital government. A complete and scientific management of geographical databases is the most crucial core foundation of a system. The article applies open-source WebGIS technologies such as Geoserver map server, PostGIS data management software, Python programming language, Vue, and Openlayers map library to build spatial data management software. Spatial data is mainly in vector form, built according to national geographic database standards (QCVN 42: 2020/BTNMT). The system has main functional groups such as user account authorization and user groups; and geographic database management, including seven groups: borders, geodesy, population, topography, traffic, surface cover, and hydrology. In addition, the administration of some thematic classes such as drafting plans, managing equipment and units, etc., are performed. The software system is built in a service-oriented manner, serving as the core foundation to facilitate the development of many geo-portal applications.

How to manage massive spatiotemporal dataset from stationary and non-stationary sensors in commercial DBMS?

AbstractThe growing diffusion of the latest information and communication technologies in different contexts allowed the constitution of enormous sensing networks that form the underlying texture of smart environments. The amount and the speed at which these environments produce and consume data are starting to challenge current spatial data management technologies. In this work, we report on our experience handling real-world spatiotemporal datasets: a stationary dataset referring to the parking monitoring system and a non-stationary dataset referring to a train-mounted railway monitoring system. In particular, we present the results of an empirical comparison of the retrieval performances achieved by three different off-the-shelf settings to manage spatiotemporal data, namely the well-established combination of PostgreSQL + PostGIS with standard indexing, a clustered version of the same setup, and then a combination of the basic setup with Timescale, a storage extension specialized in handling temporal data. Since the non-stationary dataset has put much pressure on the configurations above, we furtherly investigated the advantages achievable by combining the TSMS setup with state-of-the-art indexing techniques. Results showed that the standard indexing is by far outperformed by the other solutions, which have different trade-offs. This experience may help researchers and practitioners facing similar problems managing these types of data.

Efficient Integration of Heterogeneous Mobility-Pollution Big Data for Joint Analytics at Scale with QoS Guarantees

Numerous real-life smart city application scenarios require joint analytics on unified views of georeferenced mobility data with environment contextual data including pollution and meteorological data. particularly, future urban planning requires restricting vehicle access to specific areas of a city to reduce the adverse effect of their engine combustion emissions on the health of dwellers and cyclers. Current editions of big spatial data management systems do not come with over-the-counter support for similar scenarios. To close this gap, in this paper, we show the design and prototyping of a novel system we term as EMDI for the enrichment of human and vehicle mobility data with pollution information, thus enabling integrated analytics on a unified view. Our system supports a variety of queries including single geo-statistics, such as ‘mean’, and Top-N queries, in addition to geo-visualization on the combined view. We have tested our system with real big georeferenced mobility and environmental data coming from the city of Bologna in Italy. Our testing results show that our system can be efficiently utilized for advanced combined pollution-mobility analytics at a scale with QoS guarantees. Specifically, a reduction in latency that equals roughly 65%, on average, is obtained by using EMDI as opposed to the plain baseline, we also obtain statistically significant accuracy results for Top-N queries ranging roughly from 0.84 to 1 for both Spearman and Pearson correlation coefficients depending on the geo-encoding configurations, in addition to significant single geo-statistics accuracy values expressed using Mean Absolute Percentage Error on the range from 0.00392 to 0.000195.

A Systematic Review of Disaster Management Systems: Approaches, Challenges, and Future Directions

Disaster management is a critical area that requires efficient methods and techniques to address various challenges. This comprehensive assessment offers an in-depth overview of disaster management systems, methods, obstacles, and potential future paths. Specifically, it focuses on flood control, a significant and recurrent category of natural disasters. The analysis begins by exploring various types of natural catastrophes, including earthquakes, wildfires, and floods. It then delves into the different domains that collectively contribute to effective flood management. These domains encompass cutting-edge technologies such as big data analysis and cloud computing, providing scalable and reliable infrastructure for data storage, processing, and analysis. The study investigates the potential of the Internet of Things and sensor networks to gather real-time data from flood-prone areas, enhancing situational awareness and enabling prompt actions. Model-driven engineering is examined for its utility in developing and modeling flood scenarios, aiding in preparation and response planning. This study includes the Google Earth engine (GEE) and examines previous studies involving GEE. Moreover, we discuss remote sensing; remote sensing is undoubtedly a valuable tool for disaster management, and offers geographical data in various situations. We explore the application of Geographical Information System (GIS) and Spatial Data Management for visualizing and analyzing spatial data and facilitating informed decision-making and resource allocation during floods. In the final section, the focus shifts to the utilization of machine learning and data analytics in flood management. These methodologies offer predictive models and data-driven insights, enhancing early warning systems, risk assessment, and mitigation strategies. Through this in-depth analysis, the significance of incorporating these spheres into flood control procedures is highlighted, with the aim of improving disaster management techniques and enhancing resilience in flood-prone regions. The paper addresses existing challenges and provides future research directions, ultimately striving for a clearer and more coherent representation of disaster management techniques.