Geospatial Queries Research Articles

Dynamic knowledge graph approach for modelling the decarbonisation of power systems

This paper presents a dynamic knowledge graph approach that offers a reusable, interoperable, and extensible framework for modelling power systems. Domain ontologies have been developed to support a linked data representation of infrastructure data, socio-demographic data, areal attributes like demand, and models describing power systems. The knowledge graph links the data with a hierarchical representation of administrative regions, supporting geospatial queries to retrieve information about the population within the vicinity of a power plant, the number of power plants, total generation capacity, and demand within specific areas. Computational agents were developed to operate on the knowledge graph. The agents performed tasks including data uploading, updating, retrieval, processing, model construction and scenario analysis. A derived information framework was used to track the provenance of information calculated by agents involved in each scenario. The knowledge graph was populated with data describing the UK power system. Two alternative models of the transmission grid with different levels of structural resolution were instantiated, providing the foundation for the power system simulation and optimisation tasks performed by the agents. The application of the dynamic knowledge graph was demonstrated via a case study that investigates clean energy transition trajectories based on the deployment of Small Modular Reactors in the UK.

An Ontology-Based Framework for Geospatial Integration and Querying of Raster Data Cube Using Virtual Knowledge Graphs

The integration of the raster data cube alongside another form of geospatial data (e.g., vector data) raises considerable challenges when it comes to managing and representing it using knowledge graphs. Such integration can play an invaluable role in handling the heterogeneity of geospatial data and linking the raster data cube to semantic technology standards. Many recent approaches have been attempted to address this issue, but they often lack robust formal elaboration or solely concentrate on integrating raster data cubes without considering the inclusion of semantic spatial entities along with their spatial relationships. This may constitute a major shortcoming when it comes to performing advanced geospatial queries and semantically enriching geospatial models. In this paper, we propose a framework that can enable such semantic integration and advanced querying of raster data cubes based on the virtual knowledge graph (VKG) paradigm. This framework defines a semantic representation model for raster data cubes that extends the GeoSPARQL ontology. With such a model, we can combine the semantics of raster data cubes with features-based models that involve geometries as well as spatial and topological relationships. This could allow us to formulate spatiotemporal queries using SPARQL in a natural way by using ontological concepts at an appropriate level of abstraction. We propose an implementation of the proposed framework based on a VKG system architecture. In addition, we perform an experimental evaluation to compare our framework with other existing systems in terms of performance and scalability. Finally, we show the potential and the limitations of our implementation and we discuss several possible future works.

Delivering Personalized Content to Open-air Museum Visitors Using Geofencing

This paper presents the architecture and implementation of a service that delivers personalized content to open-air museum visitors. The service uses push notifications to deliver this content. Notifications can be delivered to all visitors or personalized - to a group of visitors or to a specific visitor. The service segments museum visitors according to their location and their profile, which is built dynamically over time. For the geospatial segmentation of visitors, geofencing is used - each visitor is assigned to a segment that corresponds to a specific geographic area - part of an open space or exhibit. The service allows localization of visitors by their GPS coordinates or by estimating their distance from Bluetooth Low Energy (BLE) beacons. The geofences are described as a polygons or circles. Geospatial segmentation is implemented using a NoSQL database MongoDB, which has built-in capabilities for working with geospatial queries. Depending on the profile, each visitor falls into one or several segments: professional researcher, non-professional researcher, inspiration seeker, casual visitor, and visitors with disabilities. For each visitor, personalized content is delivered, depending on the segments to which the visitor is assigned. The necessary experiments have been conducted and analyzed to prove the applicability of the service for real-time delivery of personalized content.

Optimizing within-distance queries by approximating shapes with maximal bounded boxes

Background: In geospatial query processing, spatial containment and intersection queries can be efficiently answered from the index. There is, however, a class of queries (such as within-distance) with a semantics that implies that every shape in the database is a potential match and should, in principle, be compared with the threshold. Naturally, this is impractical and optimizations have been developed that efficiently refine the set of candidate shapes before starting to actually compute distances and apply the threshold. In the case of the within-distance queries, many instances can be discarded in advance as too distant. Since geospatial databases organize data as a hierarchy of bounding boxes, this already provided the first direct optimization as the actual distance cannot be smaller than the distance between the bounding boxes. One can easily understand that there are shape configurations that give bounding boxes that are not very selective for near-by shapes. That is, configurations where there are shapes outside the requested distance but within the request distance from the bounding box. Methods: In this article, we investigate a further optimization in addition to and after comparing the bounding boxes, but before computing precise distances. We describe the distance optimizer operation currently used by PostGIS and show how the existing implementation prevails over approaches that use additional approximations. We implement a recursive algorithm to calculate the minimal possible largest inner rectangles of geometries. Results: We observe that the performance of the distance operation cannot be improved by using the inner approximations instead of the actual shapes. The overheads of the inner rectangles would not be recovered from calculating the distance between simpler geometries. Conclusions: The execution time of the distance operator has a small dependence on polygon complexity. Conclusively, an inner approximation for complex polygons cannot out-perform the standard PostGIS implementation.

Benchmarking geospatial database on Kubernetes cluster

Kubernetes is an open-source container orchestration system for automating container application operations and has been considered to deploy various kinds of container workloads. Traditional geo-databases face frequent scalability issues while dealing with dense and complex spatial data. Despite plenty of research work in the comparison of relational and NoSQL databases in handling geospatial data, there is a shortage of existing knowledge about the performance of geo-database in a clustered environment like Kubernetes. This paper presents benchmarking of PostgreSQL/PostGIS geospatial databases operating on a clustered environment against non-clustered environments. The benchmarking process considers the average execution times of geospatial structured query language (SQL) queries on multiple hardware configurations to compare the environments based on handling computationally expensive queries involving SQL operations and PostGIS functions. The geospatial queries operate on data imported from OpenStreetMap into PostgreSQL/PostGIS. The clustered environment powered by Kubernetes demonstrated promising improvements in the average execution times of computationally expensive geospatial SQL queries on all considered hardware configurations compared to their average execution times in non-clustered environments.

Geospatial Queries on Data Collection Using a Common Provenance Model

Lineage information is the part of the metadata that describes “what”, “when”, “who”, “how”, and “where” geospatial data were generated. If it is well-presented and queryable, lineage becomes very useful information for inferring data quality, tracing error sources and increasing trust in geospatial information. In addition, if the lineage of a collection of datasets can be related and presented together, datasets, process chains, and methodologies can be compared. This paper proposes extending process step lineage descriptions into four explicit levels of abstraction (process run, tool, algorithm and functionality). Including functionalities and algorithm descriptions as a part of lineage provides high-level information that is independent from the details of the software used. Therefore, it is possible to transform lineage metadata that is initially documenting specific processing steps into a reusable workflow that describes a set of operations as a processing chain. This paper presents a system that provides lineage information as a service in a distributed environment. The system is complemented by an integrated provenance web application that is capable of visualizing and querying a provenance graph that is composed by the lineage of a collection of datasets. The International Organization for Standardization (ISO) 19115 standards family with World Wide Web Consortium (W3C) provenance initiative (W3C PROV) were combined in order to integrate provenance of a collection of datasets. To represent lineage elements, the ISO 19115-2 lineage class names were chosen, because they express the names of the geospatial objects that are involved more precisely. The relationship naming conventions of W3C PROV are used to represent relationships among these elements. The elements and relationships are presented in a queryable graph.

Universal location referencing and homomorphic evaluation of geospatial query

Location data reveals users’ trajectories, yet it is often shared to enable many location-based services (LBS). In this paper, we propose a privacy-preserving geospatial query system with geo-hashing and somewhat homomorphic encryption. We geo-hash locations using space-filling curves for locality-preserving dimension reduction, which allows the users to specify granularity preference of their location and is agnostic to specific maps or precoded location models. Our system features three homomorphic algorithms to compute geospatial queries on encrypted location data and encrypted privacy preferences. Comparing with previous work, one of our algorithms reduces the multiplicative depth of a basic homomorphic computation approach by more than half, which significantly speeds it up. We then present an optimized prototype and experimentally demonstrates its utility in spatial cloaking.

Improving geospatial query performance of an interoperable geographic situation‐awareness system for disaster response

AbstractDisaster response operations require fast and coordinated actions based on real‐time disaster situation information. Although crowdsourced geospatial data applications have been demonstrated to be valuable tools for gathering real‐time disaster situation information, they only provide limited utility for disaster response coordination because of the lack of semantic compatibility and interoperability. To help overcome the semantic incompatibility and heterogeneity problems, we use Geospatial Semantic Web (GSW) technologies. We then combine GSW technologies with Web Feature Service requests to access multiple servers. However, a GSW‐based geographic information system often has poor performance due to the complex geometric computations required. The objective of this research is to explore how to use optimization techniques to improve the performance of an interoperable geographic situation‐awareness system (IGSAS) based on GSW technologies for disaster response. We conducted experiments to evaluate various client‐side optimization techniques for improving the performance of an IGSAS prototype for flood disaster response in New Haven, Connecticut. Our experimental results show that the developed prototype can greatly reduce the runtime costs of geospatial semantic queries through on‐the‐fly spatial indexing, tile‐based rendering, efficient algorithms for spatial join, and caching, especially for those spatial‐join geospatial queries that involve a large number of spatial features and heavy geometric computation.

Spatio-Fog: A green and timeliness-oriented fog computing model for geospatial query resolution

Geospatial data analysis is an emerging area of research today. Systems need to respond to user requests in a timely manner. In this paper we have proposed a fog computing framework namely Spatio-Fog, where the fog devices contain the geospatial data of their current region and process geospatial queries using resources in the proximity. The geospatial query resolution is performed by the fog device either itself or using cloud servers or fog device of other region depending on the geographical region related to the geospatial query. We have performed both empirical study and experimental analysis to demonstrate feasibility of our proposed approach. The empirical study illustrates that the proposed architecture Spatio-Fog reduces the power consumption and delay by approximately 43–47% and 47–83% respectively over the use of existing geospatial query resolution system. The experimental analysis demonstrates that the proposed framework reduces the power consumption and delay by 30–60% approximately than the existing geospatial query resolution system.

Framework for Visualization of GeoSpatial Query Processing by Integrating Redis With Spark

Nowadays we are moving towards digitization and making all our devices produce a variety of data, this has paved the way to the emergence of NoSQL databases like Cassandra, MongoDB, and Redis. Big data such as geospatial data allows for geospatial analytics in applications such as tourism, marketing, and rural development. Spark frameworks provide operators storage and processing of distributed data. This article proposes “GeoRediSpark” to integrate Redis with Spark. Redis is a key-value store that uses an in-memory store, hence integrating Redis with Spark can extend the real-time processing of geospatial data. The article investigates storage and retrieval of the Redis built-in geospatial queries and has added two new geospatial operators, GeoWithin and GeoIntersect, to enhance the capabilities of Redis. Hashed indexing is used to improve the processing performance. A comparison on Redis metrics with three benchmark datasets is made. Hashset is used to display geographic data. The output of geospatial queries is visualized to the type of place and the nature of the query using Tableau.

GLOSSary: the GLobal Ocean 16S subunit web accessible resource

BackgroundEnvironmental metagenomics is a challenging approach that is exponentially spreading in the scientific community to investigate taxonomic diversity and possible functions of the biological components. The massive amount of sequence data produced, often endowed with rich environmental metadata, needs suitable computational tools to fully explore the embedded information. Bioinformatics plays a key role in providing methodologies to manage, process and mine molecular data, integrated with environmental metagenomics collections. One such relevant example is represented by the Tara Ocean Project.ResultsWe considered the Tara 16S miTAGs released by the consortium, representing raw sequences from a shotgun metagenomics approach with similarities to 16S rRNA genes. We generated assembled 16S rDNA sequences, which were classified according to their lengths, the possible presence of chimeric reads, the putative taxonomic affiliation. The dataset was included in GLOSSary (the GLobal Ocean 16S Subunit web accessible resource), a bioinformatics platform to organize environmental metagenomics data. The aims of this work were: i) to present alternative computational approaches to manage challenging metagenomics data; ii) to set up user friendly web-based platforms to allow the integration of environmental metagenomics sequences and of the associated metadata; iii) to implement an appropriate bioinformatics platform supporting the analysis of 16S rDNA sequences exploiting reference datasets, such as the SILVA database. We organized the data in a next-generation NoSQL “schema-less” database, allowing flexible organization of large amounts of data and supporting native geospatial queries. A web interface was developed to permit an interactive exploration and a visual geographical localization of the data, either raw miTAG reads or 16S contigs, from our processing pipeline. Information on unassembled sequences is also available. The taxonomic affiliations of contigs and miTAGs, and the spatial distribution of the sampling sites and their associated sequence libraries, as they are contained in the Tara metadata, can be explored by a query interface, which allows both textual and visual investigations. In addition, all the sequence data were made available for a dedicated BLAST-based web application alongside the SILVA collection.ConclusionsGLOSSary provides an expandable bioinformatics environment, able to support the scientific community in current and forthcoming environmental metagenomics analyses.

Dynamic searchable symmetric encryption for storing geospatial data in the cloud

We present a dynamic searchable symmetric encryption scheme allowing users to securely store geospatial data in the cloud. Geospatial data sets often contain sensitive information, for example, about urban infrastructures. Since clouds are usually provided by third parties, these data need to be protected. Our approach allows users to encrypt their data in the cloud and make them searchable at the same time. It does not require an initialization phase, which enables users to dynamically add new data and remove existing records. We design multiple protocols differing in their level of security and performance, respectively. All of them support queries containing boolean expressions, as well as geospatial queries based on bounding boxes, for example. Our findings indicate that although the search in encrypted data requires more runtime than in unencrypted data, our approach is still suitable for real-world applications. We focus on geospatial data storage, but our approach can also be applied to applications from other areas dealing with keyword-based searches in encrypted data. We conclude the paper with a discussion on the benefits and drawbacks of our approach.

BIM AND GIS: WHEN PARAMETRIC MODELING MEETS GEOSPATIAL DATA

Abstract. Geospatial data have a crucial role in several projects related to infrastructures and land management. GIS software are able to perform advanced geospatial analyses, but they lack several instruments and tools for parametric modelling typically available in BIM. At the same time, BIM software designed for buildings have limited tools to handle geospatial data. As things stand at the moment, BIM and GIS could appear as complementary solutions, notwithstanding research work is currently under development to ensure a better level of interoperability, especially at the scale of the building. On the other hand, the transition from the local (building) scale to the infrastructure (where geospatial data cannot be neglected) has already demonstrated that parametric modelling integrated with geoinformation is a powerful tool to simplify and speed up some phases of the design workflow. This paper reviews such mixed approaches with both simulated and real examples, demonstrating that integration is already a reality at specific scales, which are not dominated by “pure” GIS or BIM. The paper will also demonstrate that some traditional operations carried out with GIS software are also available in parametric modelling software for BIM, such as transformation between reference systems, DEM generation, feature extraction, and geospatial queries. A real case study is illustrated and discussed to show the advantage of a combined use of both technologies. BIM and GIS integration can generate greater usage of geospatial data in the AECOO (Architecture, Engineering, Construction, Owner and Operator) industry, as well as new solutions for parametric modelling with additional geoinformation.

Mapping Overweight Vehicle Permits for Pavement Engineering Applications

AbstractOversize-overweight (OSOW) permit programs allow carriers to move large, heavy, and often high-value freight on state and federal highways. This study analyzed six years of Wisconsin single-trip overweight permits and two years of Iowa single-trip overweight permits to visualize routing trends, identify heavily travelled highway segments, and provide support for mechanistic-empirical pavement design guide (MEPDG) analyses and pavement reliability research. The methodology used custom visual basic for applications (VBA) scripts for text parsing and route processing with results linked to a geographic information system (GIS) database. The results allowed for visualization of permit traffic, geospatial queries of permit routes, origin-destination analyses, and identification of heavily-used permit vehicle corridors. These results have numerous applications for highway design, bridge engineering, freight trend analysis, and highway system planning. Additionally, the methodology supports the generatio...

Research on the Spatial Query Technology of Geospatial Database

The application requirement of Geospatial data is increasing and complex as it is getting numerous as a result of furthering study on geosciences. Based on a deeply research on Oracle Spatial storage management mechanism, this paper proposed a method that applies the graph theory to domain of optimizing spatial query of massive geographical data, and established a geospatial data query model in order to settle a problem of lower spatial query efficiency in geospatial database. Combining with the practical applications, this paper did a conventional spatial query test and a spatial query based on geospatial data model respectively. The result is that the spatial query based on geospatial data query model has a better efficiency than that on conventional method. Besides, this model can greatly improve the spatial query performance and this improvement will be increasingly apparent as the data volume increases.

A Location Aware Mobile Tool for Direct and Indirect Climate Data Sensors

AbstractAccess to GIS data from mobile platforms continues to be a challenge and there is a wide range of fields where it is extremely useful. In this work, we combined three key aspects: climate data sensors, mobile platforms and spatial proximity operations. We published and made use of a web 2.0 network of climate data, where content is user‐collected, by means of their meteorological stations, and exposed as available information for the virtual community. Moreover, we enriched this data by giving the users the opportunity to directly inform the system with different climate measures. In general, management of this type of information from a mobile application could result in an important decision tool, as it enables us to provide climate‐related data according to a context and a geographical location. Therefore, we implemented a native mobile application for iPhone and iPad platforms by using ArcGIS SDK for iOS and by integrating a series of ArcGIS webmaps, which allows us to perform geospatial queries based on the user's location, offering, at the same time, access to all the data provided by the climate data sensor network and from direct users.

Visualization and level-of-detail of metadata for interactive exploration of Sensor Web

There are several issues with Web-based search interfaces on a Sensor Web data infrastructure. It can be difficult to (1) find the proper keywords for the formulation of queries and (2) explore the information if the user does not have previous knowledge about the particular sensor systems providing the information. We investigate how the visualization of sensor resources on a 3D Web-based Digital Earth globe organized by level-of-detail (LOD) can enhance search and exploration of information by easing the formulation of geospatial queries against the metadata of sensor systems. Our case study provides an approach inspired by geographical mashups in which freely available functionality and data are flexibly combined. We use PostgreSQL, PostGIS, PHP, and X3D-Earth technologies to allow the Web3D standard and its geospatial component to be used for visual exploration and LOD control of a dynamic scene. Our goal is to facilitate the dynamic exploration of the Sensor Web and to allow the user to seamlessly focus in on a particular sensor system from a set of registered sensor networks deployed across the globe. We present a prototype metadata exploration system featuring LOD for a multiscaled Sensor Web as a Digital Earth application.

TrajectoryLenses – A Set‐based Filtering and Exploration Technique for Long‐term Trajectory Data

AbstractThe visual analysis of spatiotemporal movement is a challenging task. There may be millions of routes of different length and shape with different origin and destination, extending over a long time span. Furthermore there can be various correlated attributes depending on the data domain, e.g. engine measurements for mobility data or sensor data for animal tracking. Visualizing such data tends to produce cluttered and incomprehensible images that need to be accompanied by sophisticated filtering methods. We present TrajectoryLenses, an interaction technique that extends the exploration lens metaphor to support complex filter expressions and the analysis of long time periods. Analysts might be interested only in movements that occur in a given time range, traverse a certain region, or end at a given area of interest (AOI). Our lenses can be placed on an interactive map to identify such geospatial AOIs. They can be grouped with set operations to create powerful geospatial queries. For each group of lenses, users can access aggregated data for different attributes like the number of matching movements, covered time, or vehicle performance. We demonstrate the applicability of our technique on a large, real‐world dataset of electric scooter tracks spanning a 2‐year period.

The Influence of Digital Surface Model Choice on Visibility‐based Mobile Geospatial Applications

AbstractIn this work we investigate the effectiveness of different types of visibility models for use within location‐based services. This article outlines the methodology and results for our experiments, which were designed to understand the accuracy and effects of model choices for mobile visibility querying. Harnessing a novel mobile media consumption and authoring application called Zapp, the levels of accuracy of various digital surface representations used by a line of sight visibility algorithm are extensively examined by statistically assessing randomly sampled viewing sites across the 1 km2 study area, in relation to points of interest (POI) across the University of Nottingham campus. Testing was carried out on three different surface models derived from 0.5 m LiDAR data by visiting physical sites on each surface model with 14 random point of interest masks being viewed from between 10 and 16 different locations, totalling 190 data points. Each site was ground‐truthed by determining whether a given POI could be seen by the user and could also be identified by the mobile device. Our experiments in a semi‐urban area show that choice of surface model has important implications for mobile applications that utilize visibility in geospatial query operations.

A cost-effective strategy for multi-scale photo-realistic building modeling and web-based 3-D GIS applications in real estate

Web-based 3-D GIS may be the most appropriate tool for decision makers in land management and development. It provides not only the basic GIS functions, but also visually realistic landscape and architectural detail. It also gives the user an immersive 3-D virtual reality environment through the Internet that is rather different from that obtained merely through text, pictures, or videos. However, in terms of high accuracy and level-of-detail (LOD), the generation of a fully photo-realistic city model is labor intensive and time consuming. At the same time, from the aspect of computer graphics, the result is simply a geometric model without thematic information. Thus, the objective of this study is to propose a cost-effective multi-scale building modeling strategy based on the 2-D GIS building footprint that has rich attributes and to realize its application in the real estate market through a web-based 3-D GIS platform. Generally, the data volume needed for a photo-realistic city model is huge, thus for the purpose of increasing Internet data streaming efficiency and reducing the building modeling cost, a multiple-scale building modeling strategy, including block modeling, generic texture modeling, photo-realistic economic modeling, and photo-realistic detailed modeling is proposed. Since 2-D building boundary polygons are popularly used and well attributed, e.g., as to number of stories, address, type, material, etc., we are able to construct the photo-realistic city model based on this. Meanwhile, the conventional 2-D spatial analysis can be maintained and extended to 3-D GIS in the proposed scheme. For real estate applications, a location query system for selecting the optimum living environment is established. Some geospatial query and analysis functionalities are realized, such as address and road-junction positioning and terrain profile analysis. An experimental study area of 11km2 in size is used to demonstrate that the proposed multi-scale building modeling strategy and its integration into a web-based 3-D GIS platform is both efficient and cost-effective.