Integration Of Geospatial Data Research Articles

A new horizon for rural planning: Modeling rural settlements through the integration of geospatial data

A new horizon for rural planning: Modeling rural settlements through the integration of geospatial data

Integration of Geospatial Data and Information in Margoluwih Village, Yogyakarta and Its Utilization for Multiple Stakeholders

As technology develops, geospatial information has been used for policy-making and decision-making. One of the geospatial technologies used is the Geographic Information System, which has been widely built and utilized by related agencies up to the village or district level. This article explains the strategy and parties involved in developing geospatial data and information integration in Margoluwih District and the benefits for these parties. The research method uses descriptive-qualitative data collection through interviews, observation, and documentation. The results of this paper show that geospatial data and information created by various parties can be integrated into an information system. The parties involved in providing data and receiving benefits from the development information system are the National Land Academy, Margoluwih District Government, Land Office of Sleman Regency, Land and Spatial Planning Office of Sleman Regency, Regional Financial and Asset Agency of Sleman Regency, UPTD Agriculture, Food, and Fisheries Extension Center Region III, Margoluwih Village Community Institution, and the Margoluwih Village Community. This indicates that the integration carried out provides benefits for multiple parties. The Margoluwih District Government, as data regent or information system manager, is expected to always maintain and update information independently and sustainably.

A Critical Evaluation of Government Role in Spatial Data Infrastructures for Healthcare Decision-Making

This study critically examines the government's pivotal role in promoting and regulating Spatial Data Infrastructures (SDIs), with a special focus on the healthcare sector. Through an in-depth analysis of global practices, our research highlights the indispensable value of SDIs in enhancing healthcare decision-making, emergency management, and public health monitoring by facilitating the integration and analysis of geospatial and healthcare data. Despite the evident benefits, the integration of diverse geospatial data sources poses significant challenges, including issues of data standardization, privacy, and interoperability across various stakeholders. Our findings underscore the balance governments must achieve in promoting SDI growth while ensuring data security, privacy, and open access. The study draws on a comprehensive review of literature and case studies to explore the effectiveness of current government strategies in advancing SDI capabilities, while also addressing the ethical considerations and potential barriers to innovation that stringent regulation might impose. By advocating for a collaborative ecosystem that supports data standardization alongside flexibility for innovation, this paper aims to provide insights into how governments can optimize the utility of spatially enabled data in healthcare and beyond. Our conclusion calls for strategic government action to promote a balanced approach to SDI governance, highlighting the importance of public-private partnerships, robust data security frameworks, and the fostering of an environment conducive to data sharing and innovation. This research contributes to the discourse on the critical intersection of technology, healthcare decision-making, and governance, offering valuable recommendations for policymakers, healthcare professionals, and stakeholders in the digital and geospatial domains.

The Multi-temporal and Multi-dimensional Global Urban Centre Database to Delineate and Analyse World Cities

Monitoring sustainable urban development requires comparable geospatial information on cities across several thematic domains. Here we present the first global database combining such information with city extents. The Global Human Settlement Urban Centre Database (GHS-UCDB) is produced by geospatial data integration to characterise more than 10,000 urban centres worldwide. The database is multi-dimensional and multi-temporal, containing 28 variables across five domains and having multitemporal attributes for one or more epochs when the UC are delineated (1975-1990-2000-2015). Delineation of urban centres for the year 2015 is performed via a logic of grid cell population density, population size, and grid cell contiguity defined by the Degree of Urbanisation method. Each of the urban centres has 160 attributes, including a validation assessment. The novel aspects of this database concern the thematic richness and temporal depth of the variables (across geography, socio-economic, environmental, disaster risk reduction, and sustainable development domains) and the type of geo-information provided (location and extent), featuring an overall consistency that allows comparative analyses across locations and time.

Concept for long-term geo-monitoring of the post-mining environment using the example of the Prosper-Haniel mine

The operation of a mining facility provides a wealth of data, starting from mining licenses, documentation of extracted deposits, tunnel reinforcement methods, to documentation regarding the termination of mining operations, which impacts the natural environment at the local, regional, and supra-regional levels. The results of projects conducted by the Research Center of Post-Mining at the Technical University of Georg Agricola in Bochum present the possibilities of integrating environmental geo-monitoring methods to understand the processes occurring both during and after mining operations. Among the research methods used, spatiotemporal multispectral analyses of satellite imagery and images from drone flights stand out, and these will be presented in this paper. Additionally, in-situ measurements using soil sensors, weather stations, the application of mobile GIS, and three-dimensional modelling of geological structures should be noted. A key aspect of mining process research is the implementation and integration of all available geospatial data, allowing the consideration of post-mining processes as a cycle of interconnected, independent values that, through data analysis and validation, enable a comprehensive understanding.

A topology-based approach to identifying urban centers in America using multi-source geospatial big data

Urban structure can be better comprehended through analyzing its cores. Geospatial big data facilitate the identification of urban centers in terms of high accuracy and accessibility. However, previous studies seldom leverage multi-source geospatial big data to identify urban centers from a topological perspective. This study attempts to identify urban centers through the spatial integration of multi-source geospatial big data, including nighttime light imagery (NTL), building footprints (BFP) and street nodes of OpenStreetMap (OSM). We use a novel topological approach to construct complex networks from intra-urban hotspots based on the theory of centers by Christopher Alexander. We compute the degree of wholeness value for each hotspot as the centric index. The overlapped hotspots with the highest centric indices are regarded as urban centers. The identified urban centers in New York, Los Angeles, and Houston are consistent with their downtown areas, with overall accuracy of 90.23%. In Chicago, a new urban center is identified considering a larger spatial extent. The proposed approach can effectively and objectively prevent counting those hotspots with high intensity values but few neighbors into the result. This study proposes a topological approach for urban center identification and a bottom-up perspective for sustainable urban design.

Semantic enrichment of building functions through geospatial data integration and ontological inference

The comprehensive definition of buildings in urban spatial databases (SDBs) and geographic information systems (GISs) is crucial for city management, considering their essential role in cities. Modern urban geospatial applications such as smart cities need a rich-information infrastructure, relying upon urban SDBs and GISs, powered by multiple data sources. In this respect, many geospatial techniques are available for acquiring geometric data of buildings, but their semantic definitions require extra effort. Today, volunteered geographic information (VGI) platforms are assumed to be alternative geospatial data sources and their integration with official datasets provides a new opportunity for the enrichment of urban geospatial datasets. In this context, geospatial semantic web technologies can contribute to the semantic enrichment process. This study presents a methodology for enriching various building datasets using GIS and geospatial semantic web technologies, enabling an enhanced definition of building functions. The proposed method makes use of an OpenStreetMap (OSM) dataset to obtain missing semantic information about building features. The enrichment process was implemented by transferring OSM point of interest (POI) values to associated OSM building features through the application of semantic web rule language (SWRL) rules. Furthermore, the same process was also applied to two additional official datasets at different levels of detail (TOPO1K and TOPO25K), which were subsequently incorporated into the geospatial ontology. The resulting geospatial ontology offers new opportunities for functional definition and logical rule-making for buildings, with the potential to uncover new classes and insights. In addition, it allows for the definition of terms and explanations in multiple languages, covering most of the OSM values used worldwide for selected tags to define building functions.

Land and Seabed Surface Modelling in the Coastal Zone Using UAV/USV-Based Data Integration.

The coastal zone is an area that includes the sea coast and adjacent parts of the land and sea, where the mutual interaction of these environments is clearly marked. Hence, the modelling of the land and seabed parts of the coastal zone is crucial and necessary in order to determine the dynamic changes taking place in this area. The accurate determination of the terrain in the coastal zone is now possible thanks to the use of Unmanned Aerial Vehicles (UAVs) and Unmanned Surface Vehicles (USVs). The aim of this article is to present land and seabed surface modelling in the coastal zone using UAV/USV-based data integration. Bathymetric and photogrammetric measurements were carried out on the waterbody adjacent to a public beach in Gdynia (Poland) in 2022 using the DJI Phantom 4 Real Time Kinematic (RTK) UAV and the AutoDron USV. As a result of geospatial data integration, topo-bathymetric models in the coastal zone were developed using the following terrain-modelling methods: Inverse Distance to a Power (IDP), kriging, Modified Shepard's Method (MSM) and Natural Neighbour Interpolation (NNI). Then, the accuracies of the selected models obtained using the different interpolation methods, taking into account the division into land and seabed parts, were analysed. Research has shown that the most accurate method for modelling both the land and seabed surfaces of the coastal zone is the kriging (linear model) method. The differences between the interpolated and measurement values of the R95 measurement are 0.032 m for the land part and 0.034 m for the seabed part. It should also be noted that the data interpolated by the kriging (linear model) method showed a very good fit to the measurement data recorded by the UAVs and USVs.

Understanding Road Traffic Evolution in an Urbanizing Context through Statistical and Geospatial Analysis

Urbanization, an accelerated process, is inherently coupled with complex issues, including the evolution of road traffic systems. This diversity in urbanization and transport infrastructure largely hinges on economic status and geographic positioning across cities. Leveraging the capabilities of remote sensing and Geographic Information Systems (GIS) in processing geospatial big data, this paper evaluates the urbanization level (UL) and road traffic level (RTL) in 212 prefecture-level cities using statistical and geospatial grid data. We aim to dissect the impact of UL on RTL, thus highlighting the specific challenges and opportunities across regions and pinpointing optimal urban development models. Our findings demonstrate (1) rapid development in UL across all cities, juxtaposed with a surge and then stagnation in RTL; (2) a positive correlation between UL and RTL that grows over time but weakens in later stages; (3) differentiated development models in different city tiers and regions; and (4) region-specific development models and optimization policies aimed at enhancing the symbiosis of urbanization and road traffic. This study underscores the pivotal role of the integration of statistical and geospatial data in understanding the dynamic intersection of urbanization and road traffic systems.

Integration of High-Accuracy Geospatial Data and Machine Learning Approaches for Soil Erosion Susceptibility Mapping in the Mediterranean Region: A Case Study of the Macta Basin, Algeria

Erosion can have a negative impact on the agricultural sustainability and grazing lands in the Mediterranean area, especially in northern Algeria. It is useful to map the spatial occurrence of erosion and identify susceptible erodible areas on large scale. The main objective of this research was to compare the performance of four machine learning techniques: Categorical boosting, Adaptive boosting, Convolutional Neural Network, and stacking ensemble models to predict the occurrence of erosion in the Macta basin, northwestern Algeria. Several climatologic, morphologic, hydrological, and geological factors based on multi-sources data were elaborated in GIS environment to determine the erosion factors in the studied area. The conditioning factors encompassing rainfall erosivity, slope, aspect, elevation, LULC, topographic wetness index, distance from river, distance from roads, clay mineral ratio, lithology, and geology were derived via the integration of topographic attributes and remote sensing data including Landsat 8 and Sentinel 2 within a GIS framework. The inventory map of soil erosion was created by integrating data from the global positioning system to locate erosion sites, conducting extensive field surveys, and analyzing satellite images obtained from Google Earth through visual interpretation. The dataset was divided randomly into two sets with 60% for training and calibrating and 40% for testing the models. Statistical metrics including sensitivity, specificity, accuracy, and the area under the receiver operating characteristic curve (ROC) were used to assess the validity of the proposed models. The results revealed that machine learning and deep learning, as well stacking ensemble techniques, showed outstanding performance with accuracy over 98% with sensitivity 0.98 and specificity 0.98. Policy makers and local authorities can utilize the predicted erosion susceptibility maps to promote sustainable use of water and soil conservation and safeguard agricultural activities against potential damage.

Volunteered Geographic Information (VGI) in Spatial Data Infrastructure (SDI) Continuum

Spatial data infrastructure (SDI) is a system that supports the management and use of geospatial data and related resources. It involves the creation and maintenance of a network of organizations, people, and technology that enables the sharing of geospatial data across sectors and stakeholders. In recent years, the growth of geospatial data and the increasing reliance on it by various sectors has led to the emergence of new trends in SDI, such as the use of cloud computing and big data analytics, the integration of geospatial data with other types of data, and the emphasis on open data and data interoperability. Volunteered geographic information (VGI) refers to geospatial data that is collected and contributed by individuals or groups, rather than traditional sources using the application of web 2.0 and location based applications, social media, mobile devices or say citizens as the censors. Crowdsourcing in geospatial data generation concept of VGI has changed the traditional concept of SDI having one way relationship as producers and users to the user driven SDI, where user create diverse, high quality data (spatial, temporal, attribute) and also use the data interoperable, transparently, world widely and free of cost. Various authors have discussed about the application of VGI in the world of the digital data and also point outs the possibility of integration of VGI in SDI as the starting of the new generation of SDI in the form of Global GIS platform, Data Spaces, System of Systems (SoS), Geoverse, Digital Earth, Digital Twin, Virtual Geographic Environment (VGEs). However, there exists multiple VGI challenges such as data quality, data structure, data differentiation, data copyright, and data confidentiality and privacy, but with the proper cooperation and partnerships, policy and legal arrangements, standard developments, financial arrangements, inter/intra communication and added advantages of web 3.0, concept of Global Digital Ecosystem containing Geoverse, SDI and SoS is possible. Hence, VGI is the present and also the future in this SDI continuum.

Mining Geospatial Relationships from Text

A geospatial Knowledge Graph (KG) is a heterogeneous information network, capable of representing relationships between spatial entities in a machine-interpretable format, and has tremendous applications in logistics and social networks. Existing efforts to build a geospatial KG, have mainly used sparse spatial relationships, e.g., a district located inside a city, which provide only marginal benefits compared to a traditional database. In spite of the substantial advances in the tasks of link prediction and knowledge graph completion, identifying geospatial relationships remains challenging, particularly due to the fact that spatial entities are represented with single-point geometries, and textual attributes are frequently missing. In this study, we present GTMiner, a novel framework capable of jointly modeling Geospatial and Textual information to construct a knowledge graph, by mining three useful spatial relationships from a geospatial database, in an end-to-end fashion. The system is divided into three components: (1) a Candidate Selection module, to efficiently select a small number of candidate pairs; (2) a Relation Prediction component to predict spatial relationships between the entities; (3) a KG Refinement procedure, to improve both coverage and correctness of a geospatial knowledge graph. We carry out experiments on four cities' geospatial databases, from publicly-available sources and compare with existing algorithms for link prediction and geospatial data integration. Finally, we conduct an ablation study to motivate our design choices and an efficiency analysis to show that the time required by GTMiner for training and inference is comparable, or even shorter, than existing solutions.

Sustainable Palm Tree Farming: Leveraging IoT and Multi-Modal Data for Early Detection and Mapping of Red Palm Weevil

The Red Palm Weevil (RPW) is a highly destructive insect causing economic losses and impacting palm tree farming worldwide. This paper proposes an innovative approach for sustainable palm tree farming by utilizing advanced technologies for early detection and management of RPW. Our approach combines computer vision, deep learning (DL), the Internet of Things (IoT), and geospatial data to effectively detect and classify RPW-infested palm trees. The main phases include; (1) DL Classification using sound data from IoT devices, (2) palm tree detection using YOLOv8 on UAV images, and (3) RPW mapping using geospatial data. Our custom DL model achieves 100% precision and recall in detecting and localizing infested palm trees. The integration of geospatial data enables the creation of a comprehensive RPW distribution map for Efficient monitoring and targeted management strategies. This technology-driven approach benefits agricultural authorities, farmers, and researchers in managing RPW infestations, safeguarding palm tree plantations’ productivity.

Multi-Scale Flood Mapping under Climate Change Scenarios in Hexagonal Discrete Global Grids

Among the most prevalent natural hazards, flooding has been threatening human lives and properties. Robust flood simulation is required for effective response and prevention. Machine learning is widely used in flood modeling due to its high performance and scalability. Nonetheless, data pre-processing of heterogeneous sources can be cumbersome, and traditional data processing and modeling have been limited to a single resolution. This study employed an Icosahedral Snyder Equal Area Aperture 3 Hexagonal Discrete Global Grid System (ISEA3H DGGS) as a scalable, standard spatial framework for computation, integration, and analysis of multi-source geospatial data. We managed to incorporate external machine learning algorithms with a DGGS-based data framework, and project future flood risks under multiple climate change scenarios for southern New Brunswick, Canada. A total of 32 explanatory factors including topographical, hydrological, geomorphic, meteorological, and anthropogenic were investigated. Results showed that low elevation and proximity to permanent waterbodies were primary factors of flooding events, and rising spring temperatures can increase flood risk. Flooding extent was predicted to occupy 135–203% of the 2019 flood area, one of the most recent major flooding events, by the year 2100. Our results assisted in understanding the potential impact of climate change on flood risk, and indicated the feasibility of DGGS as the standard data fabric for heterogeneous data integration and incorporated in multi-scale data mining.

Street‐level traffic flow and context sensing analysis through semantic integration of multisource geospatial data

AbstractSensing urban spaces from multisource geospatial data is vital to understanding the transportation system in the urban context. However, the complexity of urban context and its indirect interaction with traffic flow deepen the difficulty of exploring their relationship. This study proposes a geo‐semantic framework first to generate semantic representations of multi‐hierarchical urban context and street‐level traffic flow, and then investigate their mutual correlation and predictability using a novel semantic matching method. The results demonstrate that each street is associated with its multi‐hierarchical spatial signatures of urban context and street‐level temporal signatures of traffic flow. The correlation between urban context and traffic flow displays higher values after semantic matching than those in multi‐hierarchies. Moreover, we found that utilizing traffic flow to predict urban context results in better accuracy than the reversed prediction. The results of signature analysis and relationship exploration can contribute to a deeper understanding of context‐aware transportation research.

Deep attention based optimized Bi-LSTM for improving geospatial data ontology

Recently, the geospatial semantic information of remote sensing (RS) has attracted attention due to its various applications. This paper introduces a model for ontology based geospatial data integration using novel deep learning techniques. Here, we use a semantic web technology to establish the spatial ontology of risk knowledge with deep learning (DL), namely deep attention based bidirectional search and rescue LSTM for analysis. This approach takes into consideration of the study which presents the technique driven by the spatial ontology which minimizes the cost of modelling. The classification results from DL model enhances the performance of the ontology module. In this paper, ontological reasoning and DL model are jointly used for increase the module efficiency. The implementation of the proposed scheme is implemented on MATLAB 2020a. The performance of the implemented scheme is compared against the existing models like U-Net, Semantic referee and collaboratively boosting framework (CBF). The Overall accuracy (OA) of the system is found to be 0.923 on UCM dataset. Thus, the developed spatial ontologies provide the semantic foundation to achieve a semantic knowledge of geospatial data understandings.

The global human settlement layer sets a new standard for global urban data reporting with the urban centre database

Monitoring of sustainable development goals (SDGs) requires a wealth of updated, reliable, and comparable data on planet Earth with respect to societal activities and society–environment interactions. Despite the massive big-data archives available today, salient data are missing for key thematic domains and geographical areas. Even for cities, which are the most prominent manifestations of human agglomeration, the data are scarce, sectoral, and scattered. Earth observation may help reconcile the disparities between data-rich and data-poor territories. The Global Human Settlement Layer (GHSL) project of the European Commission has generated an open-source global dataset on cities—the GHSL Urban Centre Database (GHS-UCDB). This database describes more than 10,000 Urban Centres in 2015, with their locations, extent, and sets of geographical, socio-economic, and environmental attributes, as well as multi-temporal data records (covering a time span of up to 40 years) (Figure 6). The database combines information extracted from satellite imagery with physical and socio-economic information from several voluminous and heterogeneous sources provided by researchers and institutions as open geospatial data. The paradigm introduced with the GHS-UCDB relies on massive geospatial data integration and harmonisation conducted in the GIS environment (mainly via spatial joins and zonal statistics). The range and depth of geospatial and statistical variables in this dataset represent a new standard foundation for information on cities—more than doubling the number of cities usually reported by international organisations and offering the capacity to understand dynamics, e.g., on population, greenness, economic productivity, night-time light, and pollutant emissions. Moreover, the database offers the basis to estimate SDG indicators and other essential variables for the Post 2015 Development Agenda.

A geospatial source selector for federated GeoSPARQL querying.

Background: Geospatial linked data brings into the scope of the Semantic Web and its technologies, a wealth of datasets that combine semantically-rich descriptions of resources with their geo-location. There are, however, various Semantic Web technologies where technical work is needed in order to achieve the full integration of geospatial data, and federated query processing is one of these technologies. Methods: In this paper, we explore the idea of annotating data sources with a bounding polygon that summarizes the spatial extent of the resources in each data source, and of using such a summary as an (additional) source selection criterion in order to reduce the set of sources that will be tested as potentially holding relevant data. We present our source selection method, and we discuss its correctness and implementation. Results: We evaluate the proposed source selection using three different types of summaries with different degrees of accuracy, against not using geospatial summaries. We use datasets and queries from a practical use case that combines crop-type data with water availability data for food security. The experimental results suggest that more complex summaries lead to slower source selection times, but also to more precise exclusion of unneeded sources. Moreover, we observe the source selection runtime is (partially or fully) recovered by shorter planning and execution runtimes. As a result, the federated sources are not burdened by pointless querying from the federation engine. Conclusions: The evaluation draws on data and queries from the agroenvironmental domain and shows that our source selection method substantially improves the effectiveness of federated GeoSPARQL query processing.

OGC API STATE OF PLAY – A PRACTICAL TESTBED FOR THE NATIONAL SPATIAL DATA INFRASTRUCTURE IN SWITZERLAND

Abstract. Interoperability standards from the Open Geospatial Consortium (OGC) shape a backbone within the OSGeo community by defining a pathway to software implementation toward the standardization of geospatial information and related services ensuring interoperability between FOSS4G software. In 2016, the OGC has initiated the creation of a new generation of standards based on the OpenAPI specifications to facilitate the integration of geospatial data in modern web applications and systems. From a practical perspective this paper intends to address the question how organizations and institutions anticipate to leverage this new generation of standards in order to deploy a geospatial data infrastructure. This issue is at the core of this article and at the center of a project that seeks to address the issue by running a Testbed Platform with a special focus to the Swiss context. To challenge these standards, the Testbed platform is configured to run a showcase with experimental cases about climate change. It is expected to bring good visibility toward all actors concerned by the Swiss standardization process and to raise interest from government stakeholders, technical or not, companies and universities as a community in synergy when considering the advances at the OGC.

ГЕОДЕЗІЯ, КАРТОГРАФІЯ І АЕРОФОТОЗНІМАННЯ

The purpose of this work is to study the integration of sets of core reference and thematic geospatial data based on the JOIN operation of relational algebra and its interaction with geocoding of geospatial features, which is implemented in modern geographic information systems (GIS) and database management systems (hereinafter – DBMS) for the development of the national spatial data infrastructure (hereinafter – NSDI). Method. The research is based on the analysis of the possibilities of applying the theory of geospatial databases and knowledge bases, international and national harmonized standards in the field of Geographic Information/ Geomatics to solve the problem of integration of geospatial data using the operation JOIN relational algebra in object-relational database management systems (OR DBMS). Results. The paper examines the models of the Join operation of relational algebra, which underlie the geocoding of features and the creation of electronic gazetteers, and proves its effectiveness: the Join operation integrates of core reference and thematic geospatial datasets. There is a need to define the required geographic identifiers, which must be present among the attributes of the core reference and thematic geospatial datasets to perform the join. The variety of uses of the Join operation covers all possible cases that arise in their practical application. Thus, the use of the Join operation involves identifying these required geographic identifiers at the geospatial database design stage. In particular, it is expedient to determine mandatory geographical identifiers (codes) of features according to the official national systems of features classification (codification) in the relevant sectoral thematic registers, which are responsible for certain holders of thematic data in accordance with Annex 2 of the Decree of Cabinet of Ministers “The order for the functioning of the national spatial data infrastructure” of May 26, 2021, № 532. Scientific novelty and practical significance. The integration of core reference data and thematic geospatial datasets based on JOIN operation models of relational algebra and their interaction with geocoding of geospatial features is researched, which is implemented in modern GIS and DBMS for the development of national spatial data infrastructure. The research was performed on a set of core reference spatial data, namely: information on administrative-territorial units of the Cherkasy region, including their borders; the data from the statistical bulletin of the socio-economic situation of the Cherkasy region for January 2021 of the Main Department of Statistics in Cherkasy region of the State Statistics Service of Ukraine were selected as thematic data. It has been shown that relational algebra join (JOIN) operations can be used to integrate other thematic geospatial data with core reference data using geographic identifiers that contain these datasets.