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Spatial Data Infrastructures, Cyberinfrastructure, and e-Science for GIScience
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Spatial Data Infrastructures, Cyberinfrastructure, and e-Science for GIScience

A special issue of ISPRS International Journal of Geo-Information (ISSN 2220-9964).

Deadline for manuscript submissions: closed (31 March 2013) | Viewed by 33498

Special Issue Editors

Prof. Sergio Rey

E-Mail Website
Guest Editor
School of Geographical Sciences and Urban Planning, Arizona State University, PO Box 875302, Tempe, AZ 85287, USA
Interests: open source geocomputation; spatial econometrics; spatial data analysis; economic geography; integrated multiregional modeling; regional science
Mr. Michael P. Finn

E-Mail Website
Guest Editor
Center of Excellence for Geospatial Information Science (CEGIS), U. S. Geological Survey (USGS), Denver Federal Center, Box 25046, Mail Stop 510 Denver, CO 80225, USA
Interests: high-performance computing and scientific applications for digital geospatial data; in geodesy, spatial coordinate systems, and map projections; in quantitative approaches to imaging in environmental modeling and Geographic Information Systems (GIS); and in data integration and generalization for GIS

Special Issue Information

Dear Colleagues,

Recent developments in geospatial and related technologies are having profound impacts on the field of geographic information science. This special issue takes stock of these impacts through contributions from leading GIScientists working at this scientific-technological interface. An overriding goal of this special issue will be to bring much needed clarity to the broadly defined and rapidly evolving areas of SDI, cyberinfrastructure, and e-Science to provide focus and guidance to GIScientists who want to make use of stirring new developments in Information and Communication Technology such as high speed networks, high performance computing, and distributed collaborative environments.

Prof. Sergio Rey
Mr. Michael P. Finn
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. ISPRS International Journal of Geo-Information is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • spatial data infrastructures
  • cyberinfrastructure
  • e-Science
  • geocomputation
  • geovisualization
  • geospatial workflows and workbenches
  • data collection and acquisition
  • data structures and algorithms
  • spatio-temporal databases
  • spatial analysis, data mining, and decision support systems
  • visualization theory and technology in real and virtual environments
  • cartography
  • location based services
  • uncertainty handling in spatial data
  • topology
  • geo-telematics
  • interoperability and open systems
  • applications of geoinformation technology (all possible domains)

Published Papers (4 papers)

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Research

409 KiB  
Article
The Semantics of Web Services: An Examination in GIScience Applications
by Xuan Shi
ISPRS Int. J. Geo-Inf. 2013, 2(3), 888-907; https://doi.org/10.3390/ijgi2030888 - 23 Sep 2013
Cited by 1 | Viewed by 5977
Abstract
Web service is a technological solution for software interoperability that supports the seamless integration of diverse applications. In the vision of web service architecture, web services are described by the Web Service Description Language (WSDL), discovered through Universal Description, Discovery and Integration (UDDI) [...] Read more.
Web service is a technological solution for software interoperability that supports the seamless integration of diverse applications. In the vision of web service architecture, web services are described by the Web Service Description Language (WSDL), discovered through Universal Description, Discovery and Integration (UDDI) and communicate by the Simple Object Access Protocol (SOAP). Such a divination has never been fully accomplished yet. Although it was criticized that WSDL only has a syntactic definition of web services, but was not semantic, prior initiatives in semantic web services did not establish a correct methodology to resolve the problem. This paper examines the distinction and relationship between the syntactic and semantic definitions for web services that characterize different purposes in service computation. Further, this paper proposes that the semantics of web service are neutral and independent from the service interface definition, data types and platform. Such a conclusion can be a universal law in software engineering and service computing. Several use cases in the GIScience application are examined in this paper, while the formalization of geospatial services needs to be constructed by the GIScience community towards a comprehensive ontology of the conceptual definitions and relationships for geospatial computation. Advancements in semantic web services research will happen in domain science applications. Full article
(This article belongs to the Special Issue Spatial Data Infrastructures, Cyberinfrastructure, and e-Science for GIScience)
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2975 KiB  
Article
Multi-Source Data Processing Middleware for Land Monitoring within a Web-Based Spatial Data Infrastructure for Siberia
by Jonas Eberle, Siegfried Clausnitzer, Christian Hüttich and Christiane Schmullius
ISPRS Int. J. Geo-Inf. 2013, 2(3), 553-576; https://doi.org/10.3390/ijgi2030553 - 26 Jun 2013
Cited by 15 | Viewed by 9911
Abstract
Land monitoring is a key issue in Earth system sciences to study environmental changes. To generate knowledge about change, e.g., to decrease uncertaincy in the results and build confidence in land change monitoring, multiple information sources are needed. Earth observation (EO) satellites and [...] Read more.
Land monitoring is a key issue in Earth system sciences to study environmental changes. To generate knowledge about change, e.g., to decrease uncertaincy in the results and build confidence in land change monitoring, multiple information sources are needed. Earth observation (EO) satellites and in situ measurements are available for operational monitoring of the land surface. As the availability of well-prepared geospatial time-series data for environmental research is limited, user-dependent processing steps with respect to the data source and formats pose additional challenges. In most cases, it is possible to support science with spatial data infrastructures (SDI) and services to provide such data in a processed format. A data processing middleware is proposed as a technical solution to improve interdisciplinary research using multi-source time-series data and standardized data acquisition, pre-processing, updating and analyses. This solution is being implemented within the Siberian Earth System Science Cluster (SIB-ESS-C), which combines various sources of EO data, climate data and analytical tools. The development of this SDI is based on the definition of automated and on-demand tools for data searching, ordering and processing, implemented along with standard-compliant web services. These tools, consisting of a user-friendly download, analysis and interpretation infrastructure, are available within SIB-ESS-C for operational use. Full article
(This article belongs to the Special Issue Spatial Data Infrastructures, Cyberinfrastructure, and e-Science for GIScience)
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1809 KiB  
Article
A Geospatial Cyberinfrastructure for Urban Economic Analysis and Spatial Decision-Making
by Wenwen Li, Linna Li, Michael F. Goodchild and Luc Anselin
ISPRS Int. J. Geo-Inf. 2013, 2(2), 413-431; https://doi.org/10.3390/ijgi2020413 - 21 May 2013
Cited by 34 | Viewed by 10317
Abstract
Urban economic modeling and effective spatial planning are critical tools towards achieving urban sustainability. However, in practice, many technical obstacles, such as information islands, poor documentation of data and lack of software platforms to facilitate virtual collaboration, are challenging the effectiveness of decision-making [...] Read more.
Urban economic modeling and effective spatial planning are critical tools towards achieving urban sustainability. However, in practice, many technical obstacles, such as information islands, poor documentation of data and lack of software platforms to facilitate virtual collaboration, are challenging the effectiveness of decision-making processes. In this paper, we report on our efforts to design and develop a geospatial cyberinfrastructure (GCI) for urban economic analysis and simulation. This GCI provides an operational graphic user interface, built upon a service-oriented architecture to allow (1) widespread sharing and seamless integration of distributed geospatial data; (2) an effective way to address the uncertainty and positional errors encountered in fusing data from diverse sources; (3) the decomposition of complex planning questions into atomic spatial analysis tasks and the generation of a web service chain to tackle such complex problems; and (4) capturing and representing provenance of geospatial data to trace its flow in the modeling task. The Greater Los Angeles Region serves as the test bed. We expect this work to contribute to effective spatial policy analysis and decision-making through the adoption of advanced GCI and to broaden the application coverage of GCI to include urban economic simulations. Full article
(This article belongs to the Special Issue Spatial Data Infrastructures, Cyberinfrastructure, and e-Science for GIScience)
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313 KiB  
Article
Pioneering GML Deployment for NSDI — Case Study of USTIGER/GML
by Lingling Guo
ISPRS Int. J. Geo-Inf. 2013, 2(1), 82-93; https://doi.org/10.3390/ijgi2010082 - 18 Feb 2013
Cited by 5 | Viewed by 6307
Abstract
The National Spatial Data Infrastructure (NSDI) is defined as the technologies, policies and people necessary to promote sharing of geospatial data throughout all levels of government, the private and non-profit sectors and the academic community. The US Census Bureau is the federal agency [...] Read more.
The National Spatial Data Infrastructure (NSDI) is defined as the technologies, policies and people necessary to promote sharing of geospatial data throughout all levels of government, the private and non-profit sectors and the academic community. The US Census Bureau is the federal agency lead for administrative units data, one of the seven data themes identified by the NSDI framework. The administrative unit is a unit with administrative responsibilities. These units are organized as nodes/lines/areas feature data. The OpenGIS Geography Markup Language (GML) is the XML grammar to express the geographic features. This study at the US Census Bureau investigates how the general-purpose GML standard could be leveraged and extended to describe the most comprehensive geographic dataset with national coverage in the US. Challenges and problems in dealing with data volume, GML document structure, GML schema design and GML document naming are analyzed, followed by proposed solutions proven for feasibility. Our results show that one key point in making a successful GML deployment for NSDI is to reflect the characteristics of the geographic data through a carefully designed GML schema, structure and organization. The lessons learned may be useful to others transforming NSDI framework data and other large geospatial datasets into GML structures. Full article
(This article belongs to the Special Issue Spatial Data Infrastructures, Cyberinfrastructure, and e-Science for GIScience)
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