Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
6.2
3.4
Journals
IJGI
Special Issues
Geographic Information Retrieval
share announcement

Geographic Information Retrieval

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

Deadline for manuscript submissions: closed (31 May 2016) | Viewed by 33981

Special Issue Editors

Prof. Dr. Kathleen Stewart

E-Mail Website
Guest Editor
Center for Geospatial Information Science, Department of Geographical Sciences, University of Maryland, College Park, MD 20742, USA
Interests: mobility; spatial accessibility; trajectory analytics; geospatial health analytics; mobility and health; geospatial ontologies
Prof. Alexander Klippel

E-Mail Website
Guest Editor
Department of Geography, The Pennsylvania State University, University Park, PA 16802, USA
Interests: spatial cognition; spatial language; formal models of commonsense knowledge; crowd science

Special Issue Information

Dear Colleagues,

References to geographic information are ubiquitous in text documents and other media. Geographic information retrieval (GIR) approaches address the challenges associated with accessing this information and making it available to users in a meaningful way. To obtain meaningful information from text sources, GIR not only focuses on traditional information retrieval and natural language processing methods, such as indexing, searching, browsing, crawling, and querying, but also methods and challenges specific to geographic information. While the area of geographic information retrieval is a rapidly expanding field of scientific research, there are still many aspects of this field that have open research questions. This Special Issue seeks original research contributions in all aspects of geographic information retrieval. The scope of submissions include, but is not limited to, the following topics:

  • Advances with respect to the geocoding of spatiotemporal references in text
  • Geospatial parsing of textual information
  • Disambiguation of spatiotemporal references in text
  • Methods and techniques for geospatial search
  • Construction, modeling, and the use of ontologies, gazetteers and geospatial thesauri for geographic information retrieval;
  • Construction and analysis of geospatial natural language in documents and queries
  • Spatiotemporal question/answering systems
  • Interfaces and human-computer-interaction specific to geographic information retrieval
  • Gold standards and evaluation approaches


Prof. Kathleen Stewart
Prof. Alexander Klippel
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.



Published Papers (4 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

Jump to: Review

4299 KiB  
Article
A Point-Set-Based Footprint Model and Spatial Ranking Method for Geographic Information Retrieval
by Yong Gao, Dan Jiang, Xiang Zhong and Jingyi Yu
ISPRS Int. J. Geo-Inf. 2016, 5(7), 122; https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi5070122 - 15 Jul 2016
Cited by 2 | Viewed by 4897
Abstract
In the recent big data era, massive spatial related data are continuously generated and scrambled from various sources. Acquiring accurate geographic information is also urgently demanded. How to accurately retrieve desired geographic information has become the prominent issue, needing to be resolved in [...] Read more.
In the recent big data era, massive spatial related data are continuously generated and scrambled from various sources. Acquiring accurate geographic information is also urgently demanded. How to accurately retrieve desired geographic information has become the prominent issue, needing to be resolved in high priority. The key technologies in geographic information retrieval are modeling document footprints and ranking documents based on their similarity evaluation. The traditional spatial similarity evaluation methods are mainly performed using a MBR (Minimum Bounding Rectangle) footprint model. However, due to its nature of simplification and roughness, the results of traditional methods tend to be isotropic and space-redundant. In this paper, a new model that constructs the footprints in the form of point-sets is presented. The point-set-based footprint coincides the nature of place names in web pages, so it is redundancy-free, consistent, accurate, and anisotropic to describe the spatial extents of documents, and can handle multi-scale geographic information. The corresponding spatial ranking method is also presented based on the point-set-based model. The new similarity evaluation algorithm of this method firstly measures multiple distances for the spatial proximity across different scales, and then combines the frequency of place names to improve the accuracy and precision. The experimental results show that the proposed method outperforms the traditional methods with higher accuracies under different searching scenarios. Full article
(This article belongs to the Special Issue Geographic Information Retrieval)
►▼ Show Figures

Figure 1

8106 KiB  
Article
A Spatial Data Infrastructure Integrating Multisource Heterogeneous Geospatial Data and Time Series: A Study Case in Agriculture
by Gloria Bordogna, Tomáš Kliment, Luca Frigerio, Pietro Alessandro Brivio, Alberto Crema, Daniela Stroppiana, Mirco Boschetti and Simone Sterlacchini
ISPRS Int. J. Geo-Inf. 2016, 5(5), 73; https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi5050073 - 21 May 2016
Cited by 34 | Viewed by 10075
Abstract
Currently, the best practice to support land planning calls for the development of Spatial Data Infrastructures (SDI) capable of integrating both geospatial datasets and time series information from multiple sources, e.g., multitemporal satellite data and Volunteered Geographic Information (VGI). This paper describes an [...] Read more.
Currently, the best practice to support land planning calls for the development of Spatial Data Infrastructures (SDI) capable of integrating both geospatial datasets and time series information from multiple sources, e.g., multitemporal satellite data and Volunteered Geographic Information (VGI). This paper describes an original OGC standard interoperable SDI architecture and a geospatial data and metadata workflow for creating and managing multisource heterogeneous geospatial datasets and time series, and discusses it in the framework of the Space4Agri project study case developed to support the agricultural sector in Lombardy region, Northern Italy. The main novel contributions go beyond the application domain for which the SDI has been developed and are the following: the ingestion within an a-centric SDI, potentially distributed in several nodes on the Internet to support scalability, of products derived by processing remote sensing images, authoritative data, georeferenced in-situ measurements and voluntary information (VGI) created by farmers and agronomists using an original Smart App; the workflow automation for publishing sets and time series of heterogeneous multisource geospatial data and relative web services; and, finally, the project geoportal, that can ease the analysis of the geospatial datasets and time series by providing complex intelligent spatio-temporal query and answering facilities. Full article
(This article belongs to the Special Issue Geographic Information Retrieval)
►▼ Show Figures

Graphical abstract

3076 KiB  
Article
Reconstructing Sessions from Data Discovery and Access Logs to Build a Semantic Knowledge Base for Improving Data Discovery
by Yongyao Jiang, Yun Li, Chaowei Yang, Edward M. Armstrong, Thomas Huang and David Moroni
ISPRS Int. J. Geo-Inf. 2016, 5(5), 54; https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi5050054 - 25 Apr 2016
Cited by 18 | Viewed by 5979
Abstract
Big geospatial data are archived and made available through online web discovery and access. However, finding the right data for scientific research and application development is still a challenge. This paper aims to improve the data discovery by mining the user knowledge from [...] Read more.
Big geospatial data are archived and made available through online web discovery and access. However, finding the right data for scientific research and application development is still a challenge. This paper aims to improve the data discovery by mining the user knowledge from log files. Specifically, user web session reconstruction is focused upon in this paper as a critical step for extracting usage patterns. However, reconstructing user sessions from raw web logs has always been difficult, as a session identifier tends to be missing in most data portals. To address this problem, we propose two session identification methods, including time-clustering-based and time-referrer-based methods. We also present the workflow of session reconstruction and discuss the approach of selecting appropriate thresholds for relevant steps in the workflow. The proposed session identification methods and workflow are proven to be able to extract data access patterns for further pattern analyses of user behavior and improvement of data discovery for more relevancy data ranking, suggestion, and navigation. Full article
(This article belongs to the Special Issue Geographic Information Retrieval)
►▼ Show Figures

Figure 1

Review

Jump to: Research

3806 KiB  
Review
Global Research on Artificial Intelligence from 1990–2014: Spatially-Explicit Bibliometric Analysis
by Jiqiang Niu, Wenwu Tang, Feng Xu, Xiaoyan Zhou and Yanan Song
ISPRS Int. J. Geo-Inf. 2016, 5(5), 66; https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi5050066 - 16 May 2016
Cited by 76 | Viewed by 12429
Abstract
In this article, we conducted the evaluation of artificial intelligence research from 1990–2014 by using bibliometric analysis. We introduced spatial analysis and social network analysis as geographic information retrieval methods for spatially-explicit bibliometric analysis. This study is based on the analysis of data [...] Read more.
In this article, we conducted the evaluation of artificial intelligence research from 1990–2014 by using bibliometric analysis. We introduced spatial analysis and social network analysis as geographic information retrieval methods for spatially-explicit bibliometric analysis. This study is based on the analysis of data obtained from database of the Science Citation Index Expanded (SCI-Expanded) and Conference Proceedings Citation Index-Science (CPCI-S). Our results revealed scientific outputs, subject categories and main journals, author productivity and geographic distribution, international productivity and collaboration, and hot issues and research trends. The growth of article outputs in artificial intelligence research has exploded since the 1990s, along with increasing collaboration, reference, and citations. Computer science and engineering were the most frequently-used subject categories in artificial intelligence studies. The top twenty productive authors are distributed in countries with a high investment of research and development. The United States has the highest number of top research institutions in artificial intelligence, producing most single-country and collaborative articles. Although there is more and more collaboration among institutions, cooperation, especially international ones, are not highly prevalent in artificial intelligence research as expected. The keyword analysis revealed interesting research preferences, confirmed that methods, models, and application are in the central position of artificial intelligence. Further, we found interesting related keywords with high co-occurrence frequencies, which have helped identify new models and application areas in recent years. Bibliometric analysis results from our study will greatly facilitate the understanding of the progress and trends in artificial intelligence, in particular, for those researchers interested in domain-specific AI-driven problem-solving. This will be of great assistance for the applications of AI in alternative fields in general and geographic information science, in particular. Full article
(This article belongs to the Special Issue Geographic Information Retrieval)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-4
Back to TopTop