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

Disaster 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.     

Asynchronous Geospatial Processing: An Event‐Driven Push‐Based Architecture for the OGC Web Processing Service

Geospatial processing tasks like solar potential analyses or floodplain investigations within flood scenarios are often complex and deal with large amounts of data. If such analysis operations are performed in distributed web‐based systems, technical capabilities are mostly not sufficient. Major shortcomings comprise the potentially long execution times and the vast amount of messaging overhead that arise from common poll‐based approaches. To overcome these issues, an approach for an event‐driven architecture for web‐based geospatial processing is proposed within this article. First, this article presents a thorough qualitative discussion of different available technologies for push‐based notifications. The aim of this discussion is to find the most suitable push‐based messaging technologies for application with OGC Web Processing Services (WPS). Based on this, an event‐driven architecture for asynchronous geospatial processing with the WPS is presented, building on the Web Socket Protocol as the transport protocol and the OGC Event Service as the message‐oriented middleware. The proposed architecture allows pushing notifications to clients once a task has completed. This paradigm enables the efficient execution of web‐based geospatial processing tasks as well as the integration of geographical analyses into event‐driven real‐time workflows.     

Relaxation‐Based Point Feature Matching for Vector Map Conflation

With the rapid advance of geospatial technologies, the availability of geospatial data from a wide variety of sources has increased dramatically. It is beneficial to integrate / conflate these multi‐source geospatial datasets, since the integration of multi‐source geospatial data can provide insights and capabilities not possible with individual datasets. However, multi‐source datasets over the same geographical area are often disparate. Accurately integrating geospatial data from different sources is a challenging task. Among the subtasks of integration/conflation, the most crucial one is feature matching, which identifies the features from different datasets as presentations of the same real‐world geographic entity. In this article we present a new relaxation‐based point feature matching approach to match the road intersections from two GIS vector road datasets. The relaxation labeling algorithm utilizes iterated local context updates to achieve a globally consistent result. The contextual constraints (relative distances between points) are incorporated into the compatibility function employed in each iteration's updates. The point‐to‐point matching confidence matrix is initialized using the road connectivity information at each point. Both the traditional proximity‐based approach and our relaxation‐based point matching approach are implemented and experiments are conducted over 18 test sites in rural and suburban areas of Columbia, MO. The test results show that our relaxation labeling approach has much better performance than the proximity matching approach in both simple and complex situations.     

A Task-Based Ontology Approach to Automate Geospatial Data Retrieval

This paper presents a task‐based and Semantic Web approach to find geospatial data. The purpose of the project is to improve data discovery and facilitate automatic retrieval of data sources. The work presented here helps create the beginnings of a Geospatial Semantic Web. The intent is to create a system that provides appropriate results to application users who search for data when facing tasks such as emergency response or planning activities. In our task‐based system, we formalize the relationships between types of tasks, including emergency response, and types of data sources needed for those tasks. Domain knowledge, including criteria describing data sources, is recorded in an ontology language. With the ontology, reasoning can be done to infer various types of information including which data sources meet specific criteria for use in particular tasks. The vision presented here is that in an emergency, for example, a user accesses a Web‐based application and selects the type of emergency and the geographic area. The application then returns the types and locations (URLs) of the specific geospatial data needed. We explore the abilities and limitations of the OWL Web Ontology Language along with other Semantic Web technologies for this purpose.     

A map‐algebra‐based method for automatic change detection and spatial data updating across multiple scales

The availability of geospatial data has increased significantly over recent decades. As a result, the question of how to update spatial data across different scales has become an attractive topic. One promising strategy is to use an updated larger‐scale dataset as a reference for detecting and updating changed objects represented in a to‐be‐updated smaller‐scale dataset. For such an update method, an understanding of the different types of changes that can occur is crucial. Using polygonal building data as an example, this study examines the various possible changes from different perspectives, such as the reasons for their occurrence, the forms in which they manifest, and their effects on output. Then, we apply map algebra theory to establish a cartographic model for updating polygonal building data. Supported by concepts of map algebra, an update procedure involving change detection, filtering, and fusion is implemented through a series of set operations. In addition to traditional polygon overlay functions, the constrained Delaunay triangulation model and knowledge of map generalization procedures are employed to construct set operations. The proposed method has been validated through tests using real‐world data. The experimental results show that our method is effective for updating 1:10k map data using 1:2k map data.     

Fine‐scale intra‐ and inter‐city commercial store site recommendations using knowledge transfer

The rapid development of urban retail companies brings new opportunities to the Chinese economy. Due to the spatiotemporal heterogeneity of different cities, selecting a business location in a new area has become a challenge. The application of multi‐source geospatial data makes it possible to describe human activities and urban functional zones at fine scale. We propose a knowledge transfer‐based model named KTSR to support citywide business location selections at the land‐parcel scale. This framework can optimize customer scores and study the pattern of business location selection for chain brands. First, we extract the features of each urban land parcel and study the similarities between them. Then, singular value decomposition was used to build a knowledge‐transfer model of similar urban land parcels between different cities. The results show that: (1) compared with the actual scores, the estimated deviation of the proposed model decreased by more than 50%, and the Pearson correlation coefficient reached 0.84 or higher; (2) the decomposed features were good at quantifying and describing high‐level commercial operation information, which has a strong relationship with urban functional structures. In general, our method can work for selecting business locations and estimating sale volumes and user evaluations.     

Automatic search of geospatial features for disaster and emergency management

Although the fast development of OGC (Open Geospatial Consortium) WFS (Web Feature Service) technologies has undoubtedly improved the sharing and synchronization of feature-level geospatial information across diverse resources, literature shows that there are still apparent limitations in the current implementation of OGC WFSs. Currently, the implementation of OGC WFSs only emphasizes syntactic data interoperability via standard interfaces and cannot resolve semantic heterogeneity problems in geospatial data sharing. To help emergency responders and disaster managers find new ways of efficiently searching for needed geospatial information at the feature level, this paper aims to propose a framework for automatic search of geospatial features using Geospatial Semantic Web technologies and natural language interfaces. We focus on two major tasks: (1) intelligent geospatial feature retrieval using Geospatial Semantic Web technologies; (2) a natural language interface to a geospatial knowledge base and web feature services over the Semantic Web. Based on the proposed framework we implemented a prototype. Results show that it is practical to directly discover desirable geospatial features from multiple semantically heterogeneous sources using Geospatial Semantic Web technologies and natural language interfaces.     

A framework for connecting two interoperability universes: OGC Web Feature Services and Linked Data

Diverse studies have shown that about 80% of all available data are related to a spatial location. Most of these geospatial data are available as structured and semi‐structured datasets, and often use distinct data models, are encoded using ad‐hoc vocabularies, and sometimes are being published in non‐standard formats. Hence, these data are isolated within silos and cannot be shared and integrated across organizations and communities. Spatial Data Infrastructures (SDIs) have emerged and contributed to significantly enhance data discovery and accessibility based on OGC (Open Geospatial Consortium) Web services. However, finding, accessing, and using data disseminated through SDIs are still difficult for non‐expert users. Overcoming the current geospatial data challenges involves adopting the best practices to expose, share, and integrate data on the Web, that is, Linked Data. In this article, we have developed a framework for generating, enriching, and exploiting geospatial Linked Data from multiple and heterogeneous geospatial data sources. This proposal allows connecting two interoperability universes (SDIs, more specifically Web Feature Services, WFS, and Semantic Web technologies), which is evaluated through a study case in the (geo)biodiversity domain.     

Geospatial Agents, Agents Everywhere . . .

The use of the related terms “agent‐based”, “multi‐agent”, “software agent” and “intelligent agent” have witnessed significant growth in the Geographic Information Science (GIScience) literature in the past decade. These terms usually refer to both artificial life agents that simulate human and animal behavior and software agents that support human‐computer interactions. In this article we first comprehensively review both types of agents. Then we argue that both these categories of agents borrow from Artificial Intelligence (AI) research, requiring them to share the characteristics of and be similar to AI agents. We also argue that geospatial agents form a distinct category of AI agents because they are explicit about geography and geographic data models. Our overall goal is to first capture the diversity of, and then define and categorize GIScience agent research into geospatial agents, thereby capturing the diversity of agent‐oriented architectures and applications that have been developed in the recent past to present a holistic review of geospatial agents.     

“You know you can do this,right?”: developing geospatial technological pedagogical content knowledge and enhancing teachers’ cartographic practices with socio-environmental science investigations

Many barriers exist to K–12 classroom teachers’ adoption and implementation of geospatial technologies with their students. To address this circumstance, we have developed and implemented a geospatial curriculum approach to promote teachers’ professional growth with curriculum-linked professional development (PD) to support the adoption of socio-environmental science investigations (SESI) in an urban school environment that includes reluctant learners. SESI focus on social issues related to environmental science. The pedagogy is inquiry-driven, with students engaged in map-based mobile data collection and subsequent analysis with Web-based dynamic mapping software to answer open-ended questions. Working with four science and social studies teachers, we designed and implemented a sequence of three locally oriented, geospatial inquiry projects that were implemented with 140 9th grade students. We investigated how the geospatial curriculum approach impacted the teachers’ geospatial pedagogical content knowledge (PCK), their cartographic practices, and promoted geospatial thinking and analysis skills with their students. Findings revealed strong growth in teachers’ geospatial PCK, increased map use by teachers, use of maps as media for inquiry and not didactic instruction, and modeling to guide students’ geospatial analysis using GIS. Implications for PD to promote teachers’ geospatial PCK and in-class cartographic practices are discussed.     

Terra Populus’ architecture for integrated big geospatial services

Big geospatial data is an emerging sub‐area of geographic information science, big data, and cyberinfrastructure. Big geospatial data poses two unique challenges. First, raster and vector data structures and analyses have developed on largely separate paths for the last 20 years. This is creating an impediment to geospatial researchers seeking to utilize big data platforms that do not promote heterogeneous data types. Second, big spatial data repositories have yet to be integrated with big data computation platforms in ways that allow researchers to spatio‐temporally analyze big geospatial datasets. IPUMS‐Terra, a National Science Foundation cyberInfrastructure project, addresses these challenges by providing a unified framework of integrated geospatial services which access, analyze, and transform big heterogeneous spatio‐temporal data. As IPUMS‐Terra's data volume grows, we seek to integrate geospatial platforms that will scale geospatial analyses and address current bottlenecks within our system. However, our work shows that there are still unresolved challenges for big geospatial analysis. The most pertinent is that there is a lack of a unified framework for conducting scalable integrated vector and raster data analysis. We conducted a comparative analysis between PostgreSQL with PostGIS and SciDB and concluded that SciDB is the superior platform for scalable raster zonal analyses.     

A geospatial case‐based reasoning model for oil–gas reservoir evaluation

Delineating the distribution of oil and natural gas resources is a prerequisite of exploitation. The delineation methods usually include conventional techniques of reservoir evaluation and mathematical models. The conventional reservoir evaluation results generally depend on the experts' knowledge and experience in the field. The mathematical methods mostly require accurate models to be proposed. Considering spatial relationships and characteristics of geological reservoir problems, including nonlinearity, complexity and uncertainty, a novel model called geospatial case‐based reasoning for oil–gas reservoir evaluation was proposed in this article. The key components of the new model, including: (1) the joint representation of spatial relationship and attribute features; (2) the model of spatial relationship and attribute similarity joint reasoning; and (3) the methods of establishing weights for the spatial relationship and attribute features, are completely constructed. A case study of the proposed model for gas reservoir evaluation was carried out. Compared with the backpropagation artificial neural network (BP‐ANN) and the geological empirical evaluation (GEE) methods, the model presented in this article performs 6.38% and 46.81% better than BP‐ANN and GEE, respectively. Furthermore, its execution is simpler, more convenient, and importantly, its utilization hardly requires any professional knowledge of the field.     

Ontology‐supported Querying of Geographical Databases

Querying geographical information systems has been recognized as a difficult task for non‐expert users. Furthermore, user queries are often characterized by semantic aspects not directly managed by traditional spatial databases or GIS. Examples of such semantic geospatial queries are the use of implicit spatial relations between objects, or the reference of domain concepts not explicitly represented in data. To handle such queries, we envisage a system that translates natural language queries into spatial SQL statements on a database, thus improving standard GIS with new semantic capabilities. Within this general objective, the contribution of this article is to introduce a methodology to handle semantic geospatial queries issued over a spatial database. This approach captures semantics from an ontology built upon the spatial database and enriched by domain concepts and properties specifically defined to represent the localization of objects. Some examples of the use of the methodology in the urban domain are presented.     

A four‐dimensional agent‐based model: A case study of forest‐fire smoke propagation

Dynamic geospatial complex systems are inherently four‐dimensional (4D) processes and there is a need for spatio‐temporal models that are capable of realistic representation for improved understanding and analysis. Such systems include changes of geological structures, dune formation, landslides, pollutant propagation, forest fires, and urban densification. However, these phenomena are frequently analyzed and represented with modeling approaches that consider only two spatial dimensions and time. Consequently, the main objectives of this study are to design and develop a modeling framework for 4D agent‐based modeling, and to implement the approach to the 4D case study for forest‐fire smoke propagation. The study area is central and southern British Columbia and the western parts of Alberta, Canada for forest fires that occurred in the summer season of 2017. The simulation results produced realistic spatial patterns of the smoke propagation dynamics.     

Geo‐Social Model: A Conceptual Framework for Real‐time Geocollaboration

Synchronous geocollaboration helps geographically dispersed people to work together in a shared geospatial environment. Its real‐time nature, multiple users' interaction and diversity of work context impose some special social, organizational and technological requirements, making the development of such real‐time geocollaboration systems a challenging task. A conceptual framework is therefore needed to specify and describe what synchronous geocollaboration is, considering its social, spatial and technical aspects. The geo‐social model presented in this article describes a conceptual framework for synchronous geocollaboration systems addressing the above aspects, identifies the core elements of the system and describes how these elements collaborate with each other. This model is presented using application‐level ontology and is then applied to a multi‐agent system based prototype in which multiple users can interact and negotiate in a shared 3D geospatial environment.     

Cumulative spatial impact layers: A novel multivariate spatio‐temporal analytical summarization tool

Scientific inquiry often requires analysis of multiple spatio‐temporal datasets, ranging in type and size, using complex multi‐step processes demanding an understanding of GIS theory and software. Cumulative spatial impact layers (CSIL) is a GIS‐based tool that summarizes spatio‐temporal datasets based on overlapping features and attributes. Leveraging a recursive quadtree method, and applying multiple additive frameworks, the CSIL tool allows users to analyze raster and vector datasets by calculating data, record, or attribute density. Providing an efficient and robust method for summarizing disparate, multi‐format, multi‐source geospatial data, CSIL addresses the need for a new integration approach and resulting geospatial product. The built‐in flexibility of the CSIL tool allows users to answer a range of spatially driven questions. Example applications are provided in this article to illustrate the versatility and variety of uses for this CSIL tool and method. Use cases include addressing regulatory decision‐making needs, economic modeling, and resource management. Performance reviews for each use case are also presented, demonstrating how CSIL provides a more efficient and robust approach to assess a range of multivariate spatial data for a variety of uses.     

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

In 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.