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Towards Knowledge-Based geospatial data integration and Visualization: A Case of Visualizing Urban Bicycling Suitability

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IEEE ACCESS 2020年 8卷 85473-85489页

作者： Huang, Weiming Kazemzadeh, Khashayar Mansourian, Ali Harrie, Lars Lund Univ Dept Phys Geog & Ecosyst Sci GIS Ctr S-22362 Lund Sweden Univ Calif Santa Barbara Dept Geog Ctr Spatial Studies Santa Barbara CA 93106 USA Lund Univ Div Transport & Rd Dept Technol & Soc Fac Engn S-22100 Lund Sweden

geospatial information plays an indispensable role in various interdisciplinary and spatially informed analyses. However, the use of geospatial information often entails many semantic intricacies relating to, among other issues, data integration and visualization. For the integration of data from different domains, merely using ontologies is inadequate for handling subtle and complex semantic relations raised by the multiple representations of geospatial data, as the domains have different conceptual views for modelling the geographic space. In addition, for geospatial data visualization-one of the most predominant ways of utilizing geospatial information-semantic intricacies arise as the visualization knowledge is difficult to interpret and utilize by non-geospatial experts. In this paper, we propose a knowledge-based approach using semantic technologies (coupling ontologies, semantic constraints, and semantic rules) to facilitate geospatial data integration and visualization. A traffic spatially informed study is developed as a case study: visualizing urban bicycling suitability. In the case study, we complement ontologies with semantic constraints for cross-domain data integration. In addition, we utilize ontologies and semantic rules to formalize geospatial data analysis and visualization knowledge at different abstraction levels, which enables machines to infer visualization means for geospatial data. The results demonstrate that the proposed framework can effectively handle subtle cross-domain semantic relations for data integration, and empower machines to derive satisfactory visualization results. The approach can facilitate the sharing and outreach of geospatial data and knowledge for various spatially informed studies.

关键词： geospatial data integration data visualization ontologies semantic constraints semantic rules

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geospatial data And Knowledge On The Web : Knowledge-Based geospatial data integration And Visualisation With Semantic Web Technologies

Geospatial Data And Knowledge On The Web : Knowledge-Based G...

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作者： Weiming Huang University of Lund

学位级别：博士

geospatial information is indispensable for various types of spatially informed analysis and decision-making, such as traffic analyses, and natural resource management. In addition, geospatial information is one of the most powerful information integrators to bridge diverse sources of information. Such natures of geospatial information entail the need of geospatial data integration and geospatial knowledge *** most of real-world applications, geospatial data from one single source can hardly suffice. Therefore, integrating multi-source geospatial data is a predominant need for a variety of applications. However, today’s solutions for geospatial data integration in spatial data infrastructures (SDIs) are inadequate, and the data are often stored in the so-called “data silos”, i.e. datasets are stored mostly isolated from each other. Semantic Web technologies provide a promising way for geospatial data integration on the Web. In this thesis, Semantic Web technologies are utilised to integrate multi-source geospatial data or integrated geospatial data with data from other domains. Paper I leveraged Linked data and ontologies to realise a relative positioning approach, which positions thematic data based on their relations with background data. The relatively positioned thematic data can be automatically synchronised in terms of their geometric representations in all scales to avoid substantial discrepancy. Paper II integrated distributed multi-scale building data and a heritage building dataset to accomplish a heritage building map with both fine geometries and thematic information of heritage building. Paper III identified that only using ontologies is inadequate for integrating geospatial data and data from other domains, where complex and subtle semantic relations often arise. Then, a knowledge-based framework coupling ontologies and semantic constraints is developed to tackle the complex semantic relations raised by multiple representations of geospatial da

关键词： Earth and Related Environmental Sciences geospatial data integration data visualisation Semantic Web Linked data ontology

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An approach for multi-scale urban building data integration and enrichment through geometric matching and semantic web

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CARTOGRAPHY AND GEOGRAPHIC INFORMATION SCIENCE 2022年第1期49卷 1-17页

作者： Memduhoglu, Abdulkadir Basaraner, Melih Harran Univ Dept Geomat Engn Div Cartog Sanliurfa Turkey Yildiz Tech Univ Dept Geomat Engn Div Cartog Istanbul Turkey

The advent of Web 2.0 has emerged abundant but often unstructured user-generated georeferenced data, such as those from Volunteered Geographic Information (VGI) initiatives. In many cases, these data can be considered as complementary to the authoritative geospatial data. With the increasing availability of multi-source geospatial data, the efforts for geospatial data integration have gained momentum, aiming at gathering maximum information to answer sophisticated questions that cannot be answered using a single data source. Although there are various approaches employed for this purpose with different degrees of success, semantic web methods and tools have not been tested sufficiently in this scope, particularly for multi-scale urban building data integration and enrichment. Attempting to fill this gap, in this study, multi-source and multi-scale urban building data were integrated with a geometric matching method based on the overlapping area, then a geospatial ontology was developed to define multi-scale representations and detailed cardinality relations of the building features. Finally, some features from the geospatial ontology were then linked to popular knowledge bases such as DBpedia and YAGO. For the exploitation on the web, query and visualization processes were demonstrated using sample questions. The semantic web enabled to model complex cardinality of relations between the features from three different building data sets using inferencing and Semantic Web Rule Language (SWRL). The study showed that integrating different geospatial data sets as a knowledge base can facilitate answering sophisticated questions from different users.

关键词： geospatial data integration geospatial data enrichment geospatial semantic web urban buildings multiple scales

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Spatial prediction of sparse events using a discrete global grid system;a case study of hate crimes in the USA

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International Journal of Digital Earth 2021年第6期14卷 789-805页

作者： Michael Jendryke Stephen C.McClure The State Key Laboratory of Information Engineering in Surveying Mappingand Remote SensingWuhan UniversityPeople’s Republic of China School of Resources and Environmental Science Wuhan UniversityPeople’s Republic of China

Spatial prediction of any geographic phenomenon can be an intractable *** sparse and uncertain spatial events related to many influencing factors necessitates the integration of multiple data *** present an innovative approach that combines data in a Discrete Global Grid System(DGGS)and uses machine learning for analysis.A DGGS provides a structured input for multiple types of spatial data,consistent over multiple *** data framework facilitates the training of an Artificial Neural Network(ANN)to map and predict a *** lag regression models(SLRM)are used to evaluate and rank the outputs of the *** our case study,we predict hate crimes in the *** crimes get attention from mass media and the scientific community,but data on such events is *** trained the ANN with data ingested in the DGGS based on a 50%sample of hate crimes as identified by the Southern Poverty Law Center(SPLC).Our spatial prediction is up to 78%accurate and verified at the state level against the independent FBI hate crime statistics with a fit of 80%.The derived risk maps are a guide to action for policy makers and law enforcement.

关键词： Discrete global grid system geospatial data integration artificial neural network spatial prediction sparse events hates crimes

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Spatio-Temporal Information Extraction Under Uncertainty Using Multi-Source data integration and Machine Learning: Applications To Human Settlement Modelling

Spatio-Temporal Information Extraction Under Uncertainty Usi...

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作者： Uhl, Johannes H. University of Colorado Boulder

学位级别：博士

Due to advances in information and communication technology, new ways of acquisition, storage, and analysis of digital data have emerged. This constitutes new opportunities, but also imposes challenges for many scientific disciplines, including the geospatial sciences, where the availability, accessibility, and spatio-temporal granularity and coverage of environmental, geographic, and socioeconomic data is steadily increasing. Multi-source data measuring identical or related processes typically increase the reliability of knowledge derived but also lead to higher levels of discrepancies. In order to fully benefit from the value of such multi-source data, the contained information needs to be extracted effectively and efficiently, employing adequate data integration, mining, and analysis techniques. This work demonstrates how the integration of coherent multi-source geospatial data supports information extraction and analysis to generate new knowledge of both, the data itself and the underlying phenomenon, exemplified by the spatio-temporal distribution of human settlements. I present three applications in the field of human settlement modelling where data integration is a key component for knowledge acquisition. These three applications consist of i) a deep-learning based classification framework for fully automated extraction of built-up areas from historical maps in the spatial domain, ii) a machine-learning based time series classification framework for estimating changes in built-up areas in the temporal domain, based on multispectral remote sensing time series data, and iii) a novel framework for an in-depth accuracy assessment of model-generated data, exemplified by the Global Human Settlement Layer, for a detailed analysis of data uncertainty in the spatio-temporal domain, as well as across different scales and aggregation levels, attempting to quantify the fitness-for-use of such data.

关键词： Remote sensing Geographic information science and geodesy Computer science Retrospective settlement modelling Accuracy assessment Built environment Convolutional neural networks geospatial data integration Remote sensing time series analysis

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Challenges and strategies for the visual exploration of complex environmental data

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International Journal of Digital Earth 2017年第10期10卷 1070-1076页

作者： Carolin Helbig Doris Dransch Michael Böttinger Colin Devey Antonie Haas Mario Hlawitschka Claudia Kuenzer Karsten Rink Christian Schäfer-Neth Gerik Scheuermann Tom Kwasnitschka Andrea Unger Helmholtz Centre for Environmental Research(UFZ) LeipzigGermany German Research Centre for Geosciences(GFZ) PotsdamGermany German Climate Computing Centre(DKRZ) HamburgGermany Helmholtz Centre for Ocean Research Kiel(GEOMAR) KielGermany Helmholtz Centre for Polar and Marine Research(AWI) BremerhavenGermany Faculty of Computer Science Mathematics and Natural SciencesLeipzig University of Applied Sciences(HTWK)LeipzigGermany German Aerospace Centre(DLR) OberpfaffenhofenGermany Institute of Computer Science Leipzig UniversityLeipzigGermany

In this opinion paper,we,a group of scientists from environmental-,geo-,ocean-and information science,argue visual data exploration should become a common analytics approach in Earth system science due to its potential for analysis and interpretation of large and complex spatio-temporal *** discuss the challenges that appear such as synthesis of heterogeneous data from various sources,reducing the amount of information and facilitating multidisciplinary,collaborative *** argue that to fully exploit the potential of visual data exploration,several bottlenecks and challenges have to be addressed:providing an efficient data management and an integrated modular workflow,developing and applying suitable visual exploration concepts and methods with the help of effective and tailored tools as well as generating and raising the awareness of visual data exploration and *** are convinced visual data exploration is worth the effort since it significantly facilitates insight into environmental data and derivation of knowledge from it.

关键词： Digital Earth geoinformatics geospatial data integration visualization

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BIG data MANAGEMENT OF SEA SURFACE WIND data

BIG DATA MANAGEMENT OF SEA SURFACE WIND DATA

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IEEE International Geoscience and Remote Sensing Symposium

作者： Felix R. Rodriguez Daniel Teomiro Villa Jaime Pina Cambero Diego J. Merino Fernandez Department of Computer Science and Telematic System Engineering School of Technology Cáceres Spain

Global wind data are increasingly used in many applications and research fields because it provides global coverage of ocean surface winds with an unprecedented view, and data are available at a sufficiently large spatial scale and high temporal resolution. NoSQL databases are increasingly used in Big data applications due to its simplicity and flexibility of the data models design, the effectiveness of recoveries, the control over the system availability, and their horizontal scaling. Thus, the integration of heterogeneous data sources in only one NoSQL database repository, to select and recover diverse regions of interest of geospatial wind data, to analyze them via data Mining applications, and to visualized their results, are the main objectives of this work.

关键词： geospatial big data Wind data geospatial data mining geospatial data integration NoSQL databases

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Semantic linking of complex properties,monitoring processes and facilities in web-based representations of the environment

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International Journal of Digital Earth 2016年第3期9卷 300-324页

作者： Adam M.Leadbetter Peter N.Vodden Marine Institute Co.GalwayIreland Centre for Ecology and Hydrology Lancaster Environment CentreLancasterUK

Where a virtual representation of the Earth must contain data values observed within the physical Earth system,data models are required that allow the integration of data across the silos of various Earth and environmental sciences *** a mapping between the well-defined terminologies of these silos is a stubborn *** paper presents a generalised ontology for use within Web 3.0 services,which builds on European Commission spatial data infrastructure *** presented ontology acknowledges that there are many complexities to the description of environmental properties which can be observed within the physical Earth *** ontology is shown to be flexible and robust enough to describe concepts drawn from a range of Earth science disciplines,including ecology,geochemistry,hydrology and *** paper also demonstrates the alignment and compatibility of the ontology with existing systems and shows applications in which the ontology may be deployed.

关键词： observable properties monitoring properties environmental monitoring facilities semantic web geospatial data integration

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Using Linked data in a heterogeneous Sensor Web: challenges, experiments and lessons learned

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International Journal of Digital Earth 2015年第1期8卷 17-37页

作者： Liang Yu Yong Liu Urban Systems Initiative Institute for Infocomm ResearchSingaporeSingapore Microsoft Corporation RedmondWAUSA

Abundant sensor data are now available online from a wealth of sources,which greatly enhance research efforts on the Digital *** combination of distributed sensor networks and expanding citizen-sensing capabilities provides a more synchronized image of earth’s social and physical ***,it remains difficult for researchers to use such heterogeneous Sensor Webs for scientific applications since data are published by following different standards and protocols and are in arbitrary *** this paper,we investigate the core challenges faced when consuming multiple sources for environmental applications using the Linked data *** design and implement a system to achieve better data interoperability and integration by republishing real-world data into linked geo-sensor *** contributions include presenting:(1)best practices of re-using and matching the W3C Semantic Sensor Network(SSN)ontology and other popular ontologies for heterogeneous data modeling in the water resources application domain,(2)a newly developed spatial analysis tool for creating links,and(3)a set of RESTful OGC Sensor Observation Service(SOS)like Linked data *** results show how a Linked Sensor Web can be built and used within the integrated water resource decision support application domain.

关键词： Linked data Sensor web geospatial data integration ontology integrated water resources decision support

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An Interdisciplinary Approach for a Water Sustainability Study

Papers in Applied Geography

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Papers in Applied Geography 2016年第2期2卷 189-200页

作者： Kong, Ningning Li, Qinghua Sangwan, Nikhil Kulzick, Robert Matei, Sorin Ariyur, Kartik Purdue University Libraries Purdue University West Lafayette IN United States School of Civil Engineering Purdue University Delon and Elizabeth Hampton Hall of Civil Engineering West Lafayette IN United States Department of Political Science Purdue University West Lafayette IN United States School of Communication at Purdue University West Lafayette IN United States School of Mechanical Engineering Purdue University West Lafayette IN United States

Interdisciplinary research is often challenging as a result of the different backgrounds of researchers from different disciplines. Such difficulty frequently hinders the understanding, communication, and collaboration among researchers from different disciplines. Meanwhile, research methods in geography can effectively link and synthesize different discipline-specific information. In this study, we show the application of geographic information systems (GIS) for the spatial analysis in a water sustainability project. We also show that GIS, as a spatial decision support system, could be effectively used for managing both hydrologic and social factors. With GIS, the project was able to integrate multidisciplinary data sets and visualize spatial information via a Web platform for sharing and communication. Our study demonstrated that geographic research methods could be effective when facilitating collaborations among the humanities, social science, and engineering to quantify social objectives and constraints, and providing insight in solving grand challenge problems. © 2016, © 2016 Applied Geography Conferences.

关键词： collaboration geospatial data integration Inter-discipline water sustainability

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