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More often than not, spatial objects are associated with some context, in the form of text, descriptive tags (e.g., points of interest, flickr photos), or linked entities in semantic graphs (e.g., Yago2, DBpedia). Hence, location-based retrieval should be extended to consider not only the locations but also the context of the objects, especially when the retrieved objects are too many and the query result is overwhelming. In this article, we study the problem of selecting a subset of the query result, which is the most representative. We argue that objects with similar context and nearby locations should proportionally be represented in the selection. Proportionality dictates the pairwise comparison of all retrieved objects and hence bears a high cost. We propose novel algorithms which greatly reduce the cost of proportional object selection in practice. In addition, we propose pre-processing, pruning, and approximate computation techniques that their combination reduces the computational cost of the algorithms even further. We theoretically analyze the approximation quality of our approaches. Extensive empirical studies on real datasets show that our algorithms are effective and efficient. A user evaluation verifies that proportional selection is more preferable than random selection and selection based on object diversification. [ABSTRACT FROM AUTHOR]

The construction of systems supporting spatial data has experienced great enthusiasm in the past, due to the richness of this type of data and their semantics, which can be used in the decision-making process in various fields. Thus, the problem of integrating spatial data into existing databases and information systems has been addressed by creating spatial extensions to relational tables or by creating spatial data warehouses, while arranging data structures and query languages by making them more spatially-aware. With the advent of Big Data, these conventional storage and spatial representation structures are becoming increasingly outdated, and required a new organization of spatial data. Approaches based on distributed storage and data lakes have been proposed, to integrate the complexity of spatial data, with operational and analytical systems which unfortunately quickly showed their limits. Recently the concept of lakehouse was introduced in order to integrate, among other things, the notion of reliability and ACID properties to the volume of data to be managed. This new data architecture is a combination of governed and reliable Data Warehouses and flexible, scalable and cost-effective Data Lakes. In this paper, we present how traditional approaches of spatial data management in the context of spatial big data have quickly shown their limits. We present a literature overview of these approaches, and how they led to the Data LakeHouse. We detail how the Lakehouse paradigm can be used and extended for managing spatial big data, by giving the different components and best practices for building a spatial data LakeHouse architecture optimized for the storage and computing over spatial big data. • Limitations of Data Warehouses and Data Lakes for Spatial Big Data. • Characteristics and Architecture of the Data LakeHouse. • Overview of Three Major Open Source LakeHouse Systems. • Challenges in Using Data LakeHouse for Spatial Big Data. • Optimized Data LakeHouse Architecture for Spatial Big Data. [ABSTRACT FROM AUTHOR]

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Many scientific fields show great interest in the extraction and processing of spatiotemporal data, such as medicine with an emphasis on epidemiology and neurology, geology, social sciences, meteorology, and a great interest is also observed in the study of transport. Spatiotemporal data differ significantly from spatial data, since spatiotemporal data refer to measurements, which take into account both the place and the time in which they are received, with their respective characteristics, while spatial data refer to and describe information related only to place. The innovation brought about by spatiotemporal data mining has caused a revolution in many scientific fields, and this is because through it we can now provide solutions and answers to complex problems, as well as provide useful and valuable predictions, through predictive learning. However, combining time and place in data mining presents significant challenges and difficulties that must be overcome. Spatiotemporal data mining and analysis is a relatively new approach to data mining which has been studied more systematically in the last decade. The purpose of this article is to provide a good introduction to spatiotemporal data, and through this detailed description, we attempt to introduce descriptive logic and gain a complete knowledge of these data. We aim to introduce a new way of describing them, aiming for future studies, by combining the expressions that arise by type of data, using descriptive logic, with new expressions, that can be derived, to describe future states of objects and environments with great precision, providing accurate predictions. In order to highlight the value of spatiotemporal data, we proceed to give a brief description of ST data in the introduction. We describe the relevant work carried out to date, the types of spatiotemporal (ST) data, their properties and the transformations that can be made between them, attempting, to a small extent, to introduce constraints and rules using descriptive logic, introducing descriptive logic into spatiotemporal data by type, when initially presenting the ST data. The data snapshots by species and similarities between the cases are then described. We describe methods, introducing clustering, dynamic ST clusters, predictive learning, pattern mining frequency, and pattern emergence, and problems such as anomaly detection, identifying time points of changes in the behavior of the observed object, and development of relationships between them. We describe the application of ST data in various fields today, as well as the future work. We finally conclude with our conclusions, with the representation and study of spatiotemporal data can, in combination with other properties which accompany all natural phenomena, through their appropriate processing, lead to safe conclusions regarding the study of problems, and also with great precision in the extraction of predictions by accurately determining future states of an environment or an object. Thus, the importance of ST data makes them particularly valuable today in various scientific fields, and their extraction is a particularly demanding challenge for the future. [ABSTRACT FROM AUTHOR]

The extent of player formation usage and the characteristics of player arrangements are not well understood in Australian football, unlike other team-based invasion sports. Using player location data from all centre bounces in the 2021 Australian Football League season; this study described the spatial characteristics and roles of players in the forward line. Summary metrics indicated that teams differed in how spread out their forward players were (deviation away from the goal-to-goal axis and convex hull area) but were similar with regard to the centroid of player locations. Cluster analysis, along with visual inspection of player densities, clearly showed the presence of different repeated structures or formations used by teams. Teams also differed in their choice of player role combinations in forward lines at centre bounces. New terminology was proposed to describe the characteristics of forward line formations used in professional Australian Football. [ABSTRACT FROM AUTHOR]

With the increasing rise of distributed system technologies, one of the most pressing problems facing the digital world is ensuring the security of sensitive and confidential data during transport and storage, which is also regarded as one of the most critical difficulties facing cloud computing. Numerous techniques exist for enhancing data security in the cloud computing storage environment. Encryption is the most important method of data protection. Consequently, several accessible encryption strategies are utilized to provide security, integrity, and authorized access by employing modern cryptographic algorithms. Cloud computing is an innovative paradigm widely accepted as a platform for storing and analysing user data. The cloud is accessible via the internet, exposing the data to external and internal threats. Cloud Service Providers (CSPs) must now implement a secure architecture to detect cloud intrusions and safeguard client data from hackers and attackers. This paper combines Stochastic Gradient Descent long short-term memory (SGD-LSTM) and Blow Fish encryption to detect and prevent unauthorized cloud access. User registration, intrusion detection, and intrusion prevention are the three phases of the planned system. The SGD-LSTM classifier predicts cloud data access and prevents unauthorized cloud access. In the data access phase, cloud data access is managed by authenticating the authorized user with the Blowfish encryption algorithm. Comparing the proposed classifier to existing classifiers demonstrates that it detects abnormal access accurately. The experimental outcomes enhanced data security, which can be utilized to protect cloud computing applications. The experimental results of the suggested SGD-LSTM algorithm indicated a high level of protection, as well as a considerable improvement in security and execution speed when compared to algorithms that are often used in cloud computing. [ABSTRACT FROM AUTHOR]

Predicting house prices is a challenging task that many researchers have attempted to address. As accurate house prices allow better informing parties in the real estate market, improving housing policies and real estate appraisal, a comprehensive overview of house price prediction strategies is valuable for both research and society. In this work, we present a systematic literature review in order to provide insights with regard to the data types and modeling approaches that have been utilized in the current body of research. As such, we identified 93 articles published between 1992 and 2021 presenting a particular technique for house price prediction. Subsequently, we scrutinized these works and scored them according to model and data novelty. A cluster analysis allowed mapping of the property valuation domain and identification of trends. Although conventional methods and traditional input data remain predominant, house price prediction research is slowly adopting more advanced techniques and innovative data sources. In addition, we identify opportunities to include more advanced input data types such as unstructured data and complex spatial data and to introduce deep learning and tailored methods, which could guide further research. [ABSTRACT FROM AUTHOR]

We introduce the ReverseSpatial Top-kKeyword (RSK) query, which is defined as: given a query term q, an integer k and a neighborhood size find all the neighborhoods of that size where q is in the top-k most frequent terms among the social posts in those neighborhoods. An obvious approach would be to partition the dataset with a uniform grid structure of a given cell size and identify the cells where this term is in the top-k most frequent keywords. However, this answer would be incomplete since it only checks for neighborhoods that are perfectly aligned with the grid. Furthermore, for every neighborhood (square) that is an answer, we can define infinitely more result neighborhoods by minimally shifting the square without including more posts in it. To address that, we need to identify contiguous regions where any point in the region can be the center of a neighborhood that satisfies the query. We propose an algorithm to efficiently answer an RSK query using an index structure consisting of a uniform grid augmented by materialized lists of term frequencies. We apply various optimizations that drastically improve query latency against baseline approaches. We also provide a theoretical model to choose the optimal cell size for the index to minimize query latency. We further examine a restricted version of the problem (RSKR) that limits the scope of the answer and propose efficient approximate algorithms. Finally, we examine how parallelism can improve performance by balancing the workload using a smart load slicing technique. Extensive experimental performance evaluation of the proposed methods using real Twitter datasets and crime report datasets, shows the efficiency of our optimizations and the accuracy of the proposed theoretical model. [ABSTRACT FROM AUTHOR]

The concept of ecosystem services (ES) has aroused interest among researchers around the world with the growing anthropogenic pressure on natural ecosystems. The meaning of the concept is taking into consideration the ecosystem functions during making decisions and ensuring sustainable development. Mapping of ES is crucial for territorial representation of ecosystem services, spatial analysis, dynamic changes detection, relationships between ecosystem elements, etc. The goal of the study is to review global scientific literature of mapping ecosystem services at the local level. The paper describes the aims, input datasets, types of mapping, and methods of 19 scientific publications. A summary contains a brief description of the initial data and the methods used. Cartographic material has been prepared to show the research geography on a world map. The most popular groups of ecosystem services for scholars have been identified. More than 39% of the mapped ES belong to the regulating services. Global land cover and land use maps are often used as input data for modeling and mapping. Regression models and software-based models (InVEST, KINEROS, etc) are more frequent among the methods for the ES assessment and mapping. Specially protected natural areas, as well as vulnerable mountain, river, and coastal ecosystems are primary objects of research. The study of ES in Russia is also confined to protected areas. Mapping of ecosystem functions and services of local objects is mostly developed in European countries.

Land is the vital base of a nation. Land is not only a natural economic resource, but also a special cultural and social basis for establishing our identity. Land is also the creative space of a nation. Among the many challenges facing to the Georgian state today is resolving the land issue. The issue of land is most fundamentally linked to the fate of the country, as territorial identity is the ground of national consciousness, and its maintenance is impossible without regulating land ownership and land use. The primary attribute of the creation of a state is the possession of a certain territory, and the determining factor in the strength of the state is a detailed study of the wealth of this territory, its successful management and the development of a strategy for the development of the country. It all depends a lot on who lives in the area and how it is managed. Therefore, the issue of land management and accounting is vital not only for our country, but for all countries of the world and for humanity in general. The historical aspiration of the Georgian state to become a full member of the European family, which includes the integration and compatibility of various fields or spheres with European standards. There is also the area of land administration. The ongoing democratic transformations in Georgia and the need to fully manage land reform, as one of its organic and important components, have raised the issue of improving the land market formation-development mechanism. World experience shows that the formation of the land market should be preceded by the creation of a unified state information system, a comprehensive land code, a land registration system, a land cadastre, which legally secures the land market. The development of the land (real estate) market should be preceded by land registration and cadastre, which are interrelated systems and complement each other. The Cadastre and all other registers containing officially registered data on land and land ownership constitute the land administration infrastructure. Cadastre is a French word meaning a description of something, a set of data about something. Cadastre This is a set of data compiled by an official body or agency (eg land, water, forestry, city, etc. cadastre). The land cadastre is used in a dual sense - narrow and territorial in the narrow sense, cadastral data are accurate graphical and textual information about the configuration and location of the border of the land, buildings on it, including a linear construction, as well as easements or other legal restrictions. And in a broad sense, a land cadastre is a set of data that includes information about the natural, economic, legal, environmental, economic state of the land and answers the questions "Who? Where? How much? How?" The land registry is to some extent maintained by the LEPP - the National Public Registry Agency. The land registry prepared by the agency answers the questions: Where? whose? How much? This information is also incomplete at the moment, because, despite the reforms carried out, the number of unregistered land plots is still high. It also doesn't answer the question, what kind? In Georgia, there is no complete production of the land cadastre, a single database has not been created, which should contain information on the natural, economic, legal, ecological and economic state of lands and fully answer the questions “Who?” Where? How much? as?" The land cadastre in Georgia was last conducted in 1985 at a time when there was only state ownership of land in the country. Georgia does not currently have a complete land cadastre production, therefore, a land cadastre production methodology should be developed, which, in the presence of various forms of land ownership, will allow us to obtain comprehensive information on land plots. The land cadastre system is the basis for the functioning of the economy of any country. Having a sound land registration system is one of the most important factors in promoting an effective land administration policy for all countries and ensuring the protection of private and public interests related to land ownership and investment. Effective systems of land administration and comprehensive land registration are the basis for the productive functioning of the market economy, the development of the agricultural sector and the sustainable and efficient management of land resources, which will significantly contribute to the sustainable growth and development of the economy.

Abstract With the increasing utilization of space related data, the demand for spatial big data sharing and trading is growing rapidly, which promotes the emergence of spatial data market. However, in conventional data markets, both data buyers and data sellers have to use a centralized trading platform which might be dishonest. Blockchain is a decentralized distributed data storage technology, which uses the traceability and unforgeability to confirm and record each transaction, and can solve partial disadvantages of the centralized data market; unfortunately, it also introduces the problems of security and privacy. To address this issue, in this paper, we propose a blockchain-based spatial data trading framework with trusted execution environment to provide a trusted decentralized platform, including data storage, data query, data pricing, data reputation and security computing. Based on this framework, we use an auction pricing mechanism to ensure data trading authenticity and efficiency. At last, a spatial data trading framework was implemented and its effectiveness and security were verified.

Detection of small objects in natural scene images is a complicated problem due to the blur and depth found in the images. Detecting house numbers from the natural scene images in real-time is a computer vision problem. On the other hand, convolutional neural network (CNN) based deep learning methods have been widely used in object detection in recent years. In this study, firstly, a classical CNN-based approach is used to detect house numbers with locations from natural images in real-time. Faster R-CNN, MobileNet, YOLOv4, YOLOv5 and YOLOv7, among the commonly used CNN models, models were applied. However, satisfactory results could not be obtained due to the small size and variable depth of the door plate objects. A new approach using the fine-tuning technique is proposed to improve the performance of CNN-based deep learning models. Experimental evaluations were made on real data from Kayseri province. Classic Faster R-CNN, MobileNet, YOLOv4, YOLOv5 and YOLOv7 methods yield f1 scores of 0.763, 0.677, 0.880, 0.943 and 0.842, respectively. The proposed fine-tuned Faster R-CNN, MobileNet, YOLOv4, YOLOv5, and YOLOv7 approaches achieved f1 scores of 0.845, 0.775, 0.932, 0.972 and 0.889, respectively. Thanks to the proposed fine-tuned approach, the f1 score of all models has increased. Regarding the run time of the methods, classic Faster R-CNN detects 0.603 seconds, while fine-tuned Faster R-CNN detects 0.633 seconds. Classic MobileNet detects 0.046 seconds, while fine-tuned MobileNet detects 0.048 seconds. Classic YOLOv4 and fine-tuned YOLOv4 detect 0.235 and 0.240 seconds, respectively. Classic YOLOv5 and fine-tuned YOLOv5 detect 0.015 seconds, and classic YOLOv7 and fine-tuned YOLOv7 detect objects in 0.009 seconds. While the YOLOv7 model was the fastest running model with an average running time of 0.009 seconds, the proposed fine-tuned YOLOv5 approach achieved the highest performance with an f1 score of 0.972.

This paper aims to present an approach for the planning of carbon low heat supply in a future district heating system based on open data for German cities with existing district heating networks. One focus is on the integration of industrial waste heat and the uncertainty of future waste heat sources as well as restrictions on the use of biomass. For that purpose, knowledge about the energy demand is necessary. In a first step it is shown how the demand around a heating network is estimated with spatial data and a load profile is generated. Local available heat sources are examined according to their suitability and their kind of integration in the heating network. As heat production from different units are optimised, the development of a simulation model will be presented. The simulation is based on the optimisation of the operational costs of the used technologies for heating supply. Different scenarios covering various technologies and economic assumptions are applied. The results show the levelized costs of heating as well as the ecological performance. A sensitivity analysis shows the importance of uncertainties for the economic assumptions. The results showing levelized costs of heating as well as the ecological performance underlining the advantage of excess heat integration.

Maritime Spatial Planning (MSP) is widely recognised around the world as a lever for sustainable development at sea. In practice, it is a new form of negotiation for the use of maritime space and its resources. To support shared decision-making, MSP requires information on the spatiotemporal dynamics of existing maritime activities. In this context, maritime surveillance, operated in real-time by the State (defence, fisheries monitoring) is a strategic source of data (AIS, VMS and radar) and information for planning purposes. While these data are growingly used by the scientific community, their contributions to MSP remains marginal. This article identifies the main reasons for this, drawing on 38 semi-directive interviews with French MSP stakeholders. It highlights two obstacles to the use of data for MSP: a) a limited accessibility of data for many actors, especially public ones and; b) a lack of acceptability on the part of historical users of maritime space when faced with their mobilization. These results illustrate the fundamentally political dimension of geographic information in the context of MSP, and argues for a gradual “desensitization” of the use of maritime surveillance data, which have gained crucial importance in a context of increasing spatial competition between maritime actors.

Habitat encroachment can have devastating effects upon biodiversity, especially amphibians. Phyllobates vittatus is an endemic frog from Costa Rica, where land cover has seen significant changes over recent decades. Here we use remote sensing to create a land cover map of the region and carry out ecological niche modelling to identify the main abiotic factors associated to the distribution of this species. We have informed our models based on our own field observations, those from other researchers, and citizen science participants to obtain a comprehensive database of P. vittatus occurrences. Elevation, forest percentage, distance to lakes and rivers, annual temperature range and precipitation variables were found to shape the ecological niche of P. vittatus , which is mostly located within protected areas. Prior knowledge of the habitat of the species was key to interpret the model output. We identify populations that might be isolated, and areas where presence has not yet been verified or that have not been occupied by the species, thus, identifying potential areas for reintroductions. We also calculated the area of occupancy and recommend that P. vittatus' status be adjusted to "Endangered". Future surveys and evaluation of population health and connectivity would help to better ensure the protection of the species in the long-term. [ABSTRACT FROM AUTHOR]

Due to the complexity of planning processes, as well as the desire to satisfy stakeholders and entities involved in spatial development and planning procedures, there is a clear need to create a platform for managing spatial development planning information. The aim of this paper is to present a project and pilot version of an urban platform (spatial planning geoportal), which is intended to be a solution used for the creation, analysis, and presentation of spatial data related to spatial planning. To implement the concept of the geoportal, one of the models used in systems and software engineering known as incremental execution was used, together with the language for documenting phases of an IT project - the Unified Modeling Language (UML). The concept includes a formal description of the geoportal functionalities using UML as well as the perspective of solution users and stakeholders. This paper also shows the functionality of the pilot version of the geoportal, which refers to spatial planning at the local level and local spatial development. The spatial planning geoportal presented in this paper adheres to the reform of spatial planning and is intended to help in conducting spatial policy, both at the local and central level. Establishment of the spatial planning geoportal may contribute to increasing the efficiency and quality of the spatial planning system in Poland, as well as other areas such as management of local government units, local and regional transport, and regional planning. [ABSTRACT FROM AUTHOR]

This paper aims to present an approach for the planning of carbon low heat supply in a future district heating system based on open data for German cities with existing district heating networks. One focus is on the integration of industrial waste heat and the uncertainty of future waste heat sources as well as restrictions on the use of biomass. For that purpose, knowledge about the energy demand is necessary. In a first step it is shown how the demand around a heating network is estimated with spatial data and a load profile is generated. Local available heat sources are examined according to their suitability and their kind of integration in the heating network. As heat production from different units are optimised, the development of a simulation model will be presented. The simulation is based on the optimisation of the operational costs of the used technologies for heating supply. Different scenarios covering various technologies and economic assumptions are applied. The results show the levelized costs of heating as well as the ecological performance. A sensitivity analysis shows the importance of uncertainties for the economic assumptions. The results showing levelized costs of heating as well as the ecological performance underlining the advantage of excess heat integration. [ABSTRACT FROM AUTHOR]

Monitoring progress towards the achievement of the Sustainable Development Goals (SDGs) mainly relies on national voluntary review mechanisms, which often depend on regional spatial data and statistics. While it is critical that governments take ownership of SDG implementation and reporting, many communities are proactively driving changes towards sustainability through local action. This paper explores the potential implications of bridging national and local implementation and reporting through the lens of SDG indicators data. It presents a community-driven case study for sustainability monitoring in the Republic of Ireland, exploring how local priorities and associated data scalability and insights provide opportunities and challenges towards a comprehensive and accurate understanding of SDG progress in implementation and achievement. Systemic data availability and scale limitations weaken the evidence-base needed for informed community-driven sustainable development initiatives. Similarly, local efforts to track changes on relevant indicators are uncommon but necessary for filling in data gaps and contributing to a more accurate national reporting. The achievement of the SDGs requires invested commitment across national, regional, local, and community levels. The implementation of sustainability interventions and tracking any changes these may enact on relevant indicators is equally a joint effort, which calls for strategic data and knowledge exchange partnerships. [ABSTRACT FROM AUTHOR]

We present acreg, a new command that implements the arbitrary clustering correction of standard errors proposed in Colella et al. (2019, IZA discussion paper 12584). Arbitrary here refers to the way observational units are correlated with each other: we impose no restrictions so that our approach can be used with a wide range of data. The command accommodates both cross-sectional and panel databases and allows the estimation of ordinary least-squares and two-stage least-squares coefficients, correcting standard errors in three environments: in a spatial setting using units' coordinates or distance between units, in a network setting starting from the adjacency matrix, and in a multiway clustering framework taking multiple clustering variables as input. Distance and time cutoffs can be specified by the user, and linear decays in time and space are also optional. [ABSTRACT FROM AUTHOR]

Background: Spatial data is spatial data is data that is connected to a location or a place on earth. In terms of managing and analyzing spatial data that will be used for decision making, a system is needed that can facilitate the work automatically but still based on scientific results. Purpose: The purpose of this study is to provide an overview of the implementation of the use of decision system support in the field of geography so that it can help professionals in this field in decision making. Method: In this study, the literature review method was used with four stages of research, namely, 1. Formulate problems; 2. Literature search; 3. Evaluation of literature search result data; 4. Analyze and interpret search result data. Results: Based on the search results, the first journal article discussing the use of (Decision Support System) DSS in the field of geography is entitled "An ontology-based web decision support system to find entertainment points of interest in an urban area. In the article it is explained that all this time the institution responsible for the restoration of the river embankment was oriented at the level of urgency. So that the restoration work of the embankment is usually only carried out after the occurrence of floods, without paying attention to the complete priority scale. Increasing the effect of using DSS in agriculture is carried out by combining big data technology to perform agricultural data mining more precisely and analyzed with decision tree algorithms to process data classification. Conclusion: From the results of the analysis of the 10 latest journals, it is known that DSS in the field of geography is used for things such as determining tourist destinations, flood risk management, improving the agricultural industry, use in geographic information systems, security of drone operationalization, determining land selection and buying and selling real estate, monitoring the quality and risk of water pollution, and determining brownfield redevelopment. This shows that the use of DSS in the field of geography is believed to provide various significant benefits for policy makers in making decisions. The benefits of using DSS are also expected to be felt in all aspects of life to assist humans in managing available resources more effectively and efficiently based on valid data. [ABSTRACT FROM AUTHOR]

Wildlife on and in the surroundings of an aerodrome pose a potential hazard for flight safety. Canada geese (Branta canadensis) represent a substantial risk for aircraft, due to their size and flocking behaviour. A group of 51 Canada geese were caught and neck-collared at two moult sites in the vicinity of Paris-Le Bourget airport. Six individuals were also GPS tagged. Bird movements were monitored between July 2019 and June 2021. The flight patterns and phenology, as well as the dispersion pattern of these birds, were investigated. Flights represented only about 1 per cent of geese daily activity. Geese spent most of their time on the ground in a 10km radius area around the capture sites. More than half of the flights outside the capture sites took place at the end of the day, between 6pm and 8pm. These flights were performed on average at less than 50m above the ground, and only less than 1 per cent of them crossed Paris-Le Bourget airport airspace. The finding of this study can be of interest to airport operators and regulators for the development of a management plan to help to reduce birdstrike risk to aircraft. [ABSTRACT FROM AUTHOR]

Large-scale geospatial data pave the way for geospatial machine learning algorithms, and a good representation is related to whether the machine learning model is effective. Hence, it is a critical task to learn effective feature representation for geospatial data. In this paper, we construct a spatial graph from the locations and propose a geospatial graph contrastive learning method to learn the location representations. Firstly, we propose a skeleton graph in order to preserve the primary structure of the geospatial graph to solve the positioning bias problem of remote sensing. Then, we define a novel mixed node centrality measure and propose four data augmentation methods based on the measure. Finally, we propose a heterogeneous graph attention network to aggregate information from both the structural neighborhood and semantic neighborhood separately. Extensive experiments on both geospatial datasets and non-geospatial datasets are conducted to illustrate that the proposed method outperforms state-of-the-art baselines. [ABSTRACT FROM AUTHOR]

GIS technology has advanced rapidly in recent decades to become a standard tool for managing and analyzing spatial data to solve spatial problems. Spatial modelling is one of the GIS products that can be used as a reference for future policy development for effective spatial management. This research aims to create land use models for the Tanah Datar area of West Sumatra in 2025 and 2035. The model is created using the Cellular Automata method and remote sensing data. The land use data from 1989 and 2002 became the model's base year, and 2019 served as the basis for testing the model's accuracy. According to the modelling results, there are several trends in land use change. Dryland agriculture expanded significantly, indicating an improvement in the pattern of agricultural land use. This condition suggests that regional development should focus more on potential and good spatial management. [ABSTRACT FROM AUTHOR]

Abstract Parallel processing of large spatial datasets over distributed systems has become a core part of modern data analytic systems like Apache Hadoop and Apache Spark. The general-purpose design of these systems does not natively account for the data’s spatial attributes and results in poor scalability, accuracy, or prolonged runtimes. Spatial extensions remedy the problem and introduce spatial data recognition and operations. At the core of a spatial extension, a locality-preserving spatial partitioner determines how to spatially group the dataset’s objects into smaller chunks using the distributed system’s available resources. Existing spatial extensions rely on data sampling and often mismanage non-spatial data by either overlooking their memory requirements or excluding them entirely. This work discusses the various challenges that face spatial data partitioning and proposes a novel spatial partitioner for effectively processing spatial queries over large spatial datasets. For evaluation, the proposed partitioner is integrated with the well-known k-Nearest Neighbor ( $$k$$ k NN) spatial join query. Several experiments evaluate the proposal using real-world datasets. Our approach differs from existing proposals by (1) accounting for the dataset’s unique spatial traits without sampling, (2) considering the computational overhead required to handle non-spatial data, (3) minimizing partition shuffles, (4) computing the optimal utilization of the available resources, and (5) achieving accurate results. This contributes to the problem of spatial data partitioning through (1) providing a comprehensive discussion of the problems facing spatial data partitioning and processing, (2) the development of a novel spatial partitioning technique for in-memory distributed processing, (3) an effective, built-in, load-balancing methodology that reduces spatial query skews, and (4) a Spark-based implementation of the proposed work with an accurate $$k$$ k NN spatial join query. Experimental tests show up to $$1.48$$ 1.48 times improvement in runtime as well as the accuracy of results.

Abstract In this commentary, we describe the current state of the art of points of interest (POIs) as digital, spatial datasets, both in terms of their quality and affordings, and how they are used across research domains. We argue that good spatial coverage and high-quality POI features — especially POI category and temporality information — are key for creating reliable data. We list challenges in POI geolocation and spatial representation, data fidelity, and POI attributes, and address how these challenges may affect the results of geospatial analyses of the built environment for applications in public health, urban planning, sustainable development, mobility, community studies, and sociology. This commentary is intended to shed more light on the importance of POIs both as standalone spatial datasets and as input to geospatial analyses.

The disciplines of Coastal Management and Marine Spatial Planning (MSP) are predominantly focussed on creating a regulatory and legislative framework within which national and regional planning policies, climate mitigation measures and environmental monitoring requirements can be effectively implemented. These evidence-based processes necessitate the discoverability, accessibility and integration of appropriate 'trusted' coastal and marine data together with contiguous terrestrial datasets. This presents significant challenges at national and local levels. While authoritative open data is often available, the data for any management requirement is often dispersed across a wide range of national online portals, statistical and research hubs, 'scientific' platforms and occasionally, private companies. This is exemplified in Ireland, where MSP is in its infancy, and consequently, the breadth of MSP foundational thematic marine data requirements required to comply with this Directive has yet to be fully realised by end-users/stakeholders and data providers. This study examines the nexus between mandatory data requirements of the European Community's Infrastructure for Spatial Information in the European Community (INSPIRE) Directive 1 1 INSPIRE Directive (2007/2/EC) is a legal EU initiative to establish an INfrastructure for SPatial InfoRmation in Europe and to make geographical information more accessible and interoperable for the management of the environment and other wide range of purposes towards supporting sustainable development (EC, 2021) (2007/2/EC), (EC, 2007) and the evidence-base needs of its Maritime Spatial Planning (MSP) Directive (2014/89/EU). An extensive inventory of coastal and MSP thematic data features is identified and categorised (into sectoral/thematic clusters) and appropriate and authoritative online sources (where available) are scoped out. Through an in-depth interrogation of national and EU sectoral platforms or portals, (much of whose data was originally captured for a diverse range of applications and management processes), and in view of INSPIRE's well-established (legally-binding) data specifications, standards and guidance, a methodology is presented to accelerate coastal and marine data discovery, and to collate or 'group' these authoritative data sources under broad but succinct MSP thematic headings. The resulting coastal and marine data catalogue is the first step in the creation of an easy data reference and retrieval tool for Irish coastal and marine practitioners, and presents a model for other nations. [ABSTRACT FROM AUTHOR]

Using spatial data in mobile applications has grown significantly, thereby empowering users to explore locations, navigate unfamiliar areas, find transportation routes, employ geomarketing strategies, and model environmental factors. Spatial databases are pivotal in efficiently storing, retrieving, and manipulating spatial data to fulfill users’ needs. Two fundamental spatial query types, k-nearest neighbors (kNN) and range search, enable users to access specific points of interest (POIs) based on their location, which are measured by actual road distance. However, retrieving the nearest POIs using actual road distance can be computationally intensive due to the need to find the shortest distance. Using straight-line measurements could expedite the process but might compromise accuracy. Consequently, this study aims to evaluate the accuracy of the Euclidean distance method in POIs retrieval by comparing it with the road network distance method. The primary focus is determining whether the trade-off between computational time and accuracy is justified, thus employing the Open Source Routing Machine (OSRM) for distance extraction. The assessment encompasses diverse scenarios and analyses factors influencing the accuracy of the Euclidean distance method. The methodology employs a quantitative approach, thereby categorizing query points based on density and analyzing them using kNN and range query methods. Accuracy in the Euclidean distance method is evaluated against the road network distance method. The results demonstrate peak accuracy for kNN queries at k=1, thus exceeding 85% across classes but declining as k increases. Range queries show varied accuracy based on POI density, with higher-density classes exhibiting earlier accuracy increases. Notably, datasets with fewer POIs exhibit unexpectedly higher accuracy, thereby providing valuable insights into spatial query processing.

Securing reliable data on forest areas is necessary for the establishment of various policies and decision-making for forest administration. In this study, the definition of forest, minimum partitioning criteria, purpose of production, production method, and period of update were analyzed, which were prescribed for the statistics (Forest Basic Statistics [FBS] and Cadastral Statistical Annual Report [CSAR]) and spatial data (digital forest type map, sub-divided land cover map, continuous cadastral map). Forested area was calculated according to the statistics and spatial data for Wonju, Gangwon-do, and the forest area between statistics and spatial data was quantitatively compared. In terms of the definition of forest and minimum partitioning criteria, the FBS and digital forest type maps were similar, and the land cover map, CSAR, and continuous cadastral maps were different in these aspects. About forest area, there was a difference in each forestry data. The highest was CSAR(61,406 ha) and the lowest was Sub-divided Land Cover Map(57,818 ha). This is thought to be because there were some types of spatial areas that were classified as forest in the digital forest type map but classified as cropland, grassland, settlement, and bare land in the sub-divided land cover map and continuous cadastral map. Moreover, in the case of the continuous cadastral map, it is thought that there was an error in the area calculation due to differences between the land category in the map and the actual land use status, which led to differences in the calculated area between different types of spatial data. For future statistics and spatial data, appropriate measures should be established to address the issue of the differences between the calculated area due to misclassification during visual reading, and the difference between the land category in the map and the actual land use status. The calculated forest area should be used for research on the definition of forest, the purposes of map production, and production methods for each type of information and data

Developments in big data technology, wireless networks, Geographic information system (GIS) technology, and internet growth has increased the volume of data at an exponential rate. Internet users are generating data with every single click. Geospatial metadata is widely used for urban planning, map making, spatial data analysis, and so on. Scientific databases use metadata for computations and query processing. Cleaning of data is required for improving the quality of geospatial metadata for scientific computations and spatial data analysis. In this paper, we have designed a data cleaning tool named as GeoWebCln to remove useless data from geospatial metadata in a user-friendly environment using the Python console of QGIS Software.