Special Issue "Open Data and Robust & Reliable GIScience"

A special issue of Data (ISSN 2306-5729).

Deadline for manuscript submissions: 30 June 2019

Special Issue Editors

Guest Editor
Dr. Daniel Z. Sui

Department of Geography, The Ohio State University, Columbus, OH 43210-1361, USA
Website | E-Mail
Interests: GIScience; social media; volunteered geographic information; health; security implications of climate change
Guest Editor
Dr. Xinyue Ye

Department of Informatics, Urban Informatics & Spatial Computation Lab, New Jersey Institute of Technology, Newark, NJ 07102, USA
Website | E-Mail
Interests: GIS; spatial analysis; urban and regional modeling
Guest Editor
Dr. Jamal Jokar Arsanjani

Geographic Information Science, Department of Planning and Development, Aalborg University Copenhagen, A.C. Meyers Vænge 15, DK-2450 Copenhagen, Denmark
Website | E-Mail
Phone: 93562323
Interests: volunteered geographic information (VGI); big (geo) data; crowdsourced mapping; citizen science; geocomputation; digital earth; remote sensing and spatio-temporal monitoring of environment; data fusion; (geo)data quality

Special Issue Information

Dear Colleagues,

With the growing capability of generating, collecting, and storing individuals’ digital footprints and the emerging open culture, big data of various kinds are flooding everywhere.  Geospatial data are an important component of open data unfolding right in front of our eyes. GIS research is shifting towards analyzing ever-increasing amounts of large-scale, diverse data in an interdisciplinary, collaborative, and timely manner, towards enhancing the robustness and reliability of research. Open GIS should embrace eight dimensions related to data, software, hardware, standards, research, publication, funding, and education facilitated by web-based tools and the growing influence of the open culture.

In line with the spirit of crowdsourcing and citizen science, “robust and reliable” GIScience refers to GIScience research that is reproducible, replicable, and generalizable. Data should be legally and technically open to the scientific community, industry, and the public to use and republish. In other words, data should be provided in open machine-readable formats and readily located, along with the relevant metadata evaluating the reliability and quality of the data to promote increased data use and facilitated credibility determination. The open data initiatives encourage peer production, interactivity, and user-generated innovation, which has stimulated the sharing and distribution of information across communities and disciplines. Transparency and participation through data integration and dissemination across domains and boundaries will facilitate collaboration among researchers, private sectors, and civilian society. Robust and reliable research is the foundation of all scientific development and progress, which depends critically on the ability of researchers to build on prior work.

This Special Issue will provide a forum on addressing theoretical, methodological, and empirical frontiers in Robust and Reliable GIScience. In particular, we encourage (but are not limited to) the following topics:

  • Data fusion
  • Data mining
  • Methodological development to improve the robustness/reliability of GIScience research, especially in the context of reproducibility, replication, and generalizability
  • Multi-scale modeling of open data
  • Open data movement
  • Open data privacy
  • Open data theories
  • The extent of, causes of, or remedies for GIScience research that is neither replicable, reproducible, nor generalizable

Dr. Daniel Z. Sui
Dr. Xinyue Ye
Dr. Jamal Jokar Arsanjani
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 papers will be 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. Data is an international peer-reviewed open access quarterly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) is waived for well-prepared manuscripts submitted to this issue. 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

  • Data fusion
  • Data mining
  • Methodological development to improve the robustness/reliability of GIScience research, especially in the context of reproducibility, replication, and generalizability
  • Multi-scale modeling of open data
  • Open data movement
  • Open data privacy
  • Open data theories
  • The extent of, causes of, or remedies for GIScience research that is neither replicable, reproducible, nor generalizable

Published Papers (4 papers)

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Research

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Open AccessArticle Earth Observation for Citizen Science Validation, or Citizen Science for Earth Observation Validation? The Role of Quality Assurance of Volunteered Observations
Received: 28 August 2017 / Revised: 2 October 2017 / Accepted: 19 October 2017 / Published: 23 October 2017
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Abstract
Environmental policy involving citizen science (CS) is of growing interest. In support of this open data stream of information, validation or quality assessment of the CS geo-located data to their appropriate usage for evidence-based policy making needs a flexible and easily adaptable data
[...] Read more.
Environmental policy involving citizen science (CS) is of growing interest. In support of this open data stream of information, validation or quality assessment of the CS geo-located data to their appropriate usage for evidence-based policy making needs a flexible and easily adaptable data curation process ensuring transparency. Addressing these needs, this paper describes an approach for automatic quality assurance as proposed by the Citizen OBservatory WEB (COBWEB) FP7 project. This approach is based upon a workflow composition that combines different quality controls, each belonging to seven categories or “pillars”. Each pillar focuses on a specific dimension in the types of reasoning algorithms for CS data qualification. These pillars attribute values to a range of quality elements belonging to three complementary quality models. Additional data from various sources, such as Earth Observation (EO) data, are often included as part of the inputs of quality controls within the pillars. However, qualified CS data can also contribute to the validation of EO data. Therefore, the question of validation can be considered as “two sides of the same coin”. Based on an invasive species CS study, concerning Fallopia japonica (Japanese knotweed), the paper discusses the flexibility and usefulness of qualifying CS data, either when using an EO data product for the validation within the quality assurance process, or validating an EO data product that describes the risk of occurrence of the plant. Both validation paths are found to be improved by quality assurance of the CS data. Addressing the reliability of CS open data, issues and limitations of the role of quality assurance for validation, due to the quality of secondary data used within the automatic workflow, are described, e.g., error propagation, paving the route to improvements in the approach. Full article
(This article belongs to the Special Issue Open Data and Robust & Reliable GIScience)
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Other

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Open AccessData Descriptor UAV-Based 3D Point Clouds of Freshwater Fish Habitats, Xingu River Basin, Brazil
Received: 9 December 2018 / Revised: 31 December 2018 / Accepted: 7 January 2019 / Published: 10 January 2019
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Abstract
Dense 3D point clouds were generated from Structure-from-Motion Multiview Stereo (SFM-MVS) photogrammetry for five representative freshwater fish habitats in the Xingu river basin, Brazil. The models were constructed from Unmanned Aerial Vehicle (UAV) photographs collected in 2016 and 2017. The Xingu River is
[...] Read more.
Dense 3D point clouds were generated from Structure-from-Motion Multiview Stereo (SFM-MVS) photogrammetry for five representative freshwater fish habitats in the Xingu river basin, Brazil. The models were constructed from Unmanned Aerial Vehicle (UAV) photographs collected in 2016 and 2017. The Xingu River is one of the primary tributaries of the Amazon River. It is known for its exceptionally high aquatic biodiversity. The dense 3D point clouds were generated in the dry season when large areas of aquatic substrate are exposed due to the low water level. The point clouds were generated at ground sampling distances of 1.20–2.38 cm. These data are useful for studying the habitat characteristics and complexity of several fish species in a spatially explicit manner, such as calculation of metrics including rugosity and the Minkowski–Bouligand fractal dimension (3D complexity). From these dense 3D point clouds, substrate complexity can be determined more comprehensively than from conventional arbitrary cross sections. Full article
(This article belongs to the Special Issue Open Data and Robust & Reliable GIScience)
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Open AccessData Descriptor World Ocean Isopycnal Level Absolute Geostrophic Velocity (WOIL-V) Inverted from GDEM with the P-Vector Method
Received: 28 September 2017 / Revised: 21 December 2017 / Accepted: 2 January 2018 / Published: 7 January 2018
PDF Full-text (1001 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
Three-dimensional dataset of world ocean climatological annual and monthly mean absolute geostrophic velocity in isopycnal level (called WOIL-V) has been produced from the United States (U.S.) Navy’s Generalized Digital Environmental Model (GDEM) temperature and salinity fields (open access from the website http://data.nodc.noaa.gov/cgi-bin/iso?id=gov.noaa.nodc:9600094)
[...] Read more.
Three-dimensional dataset of world ocean climatological annual and monthly mean absolute geostrophic velocity in isopycnal level (called WOIL-V) has been produced from the United States (U.S.) Navy’s Generalized Digital Environmental Model (GDEM) temperature and salinity fields (open access from the website http://data.nodc.noaa.gov/cgi-bin/iso?id=gov.noaa.nodc:9600094) using the P-vector method. The data have horizontal resolution of 0.5° × 0.5°, and 222 isopycnal-levels. The total 13 data files include annual and monthly mean values. The WOIL-V is the only dataset of absolute geostrophic velocity in isopycnal level compatible to the GDEM (T, S) fields, and provides background ocean currents for oceanographic and climatic studies, especially in ocean modeling with the isopycnal coordinate system. Full article
(This article belongs to the Special Issue Open Data and Robust & Reliable GIScience)
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Open AccessData Descriptor CHASE-PL—Future Hydrology Data Set: Projections of Water Balance and Streamflow for the Vistula and Odra Basins, Poland
Received: 11 March 2017 / Revised: 9 April 2017 / Accepted: 10 April 2017 / Published: 26 April 2017
Cited by 2 | PDF Full-text (676 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
There is considerable concern that the water resources of Central and Eastern Europe region can be adversely affected by climate change. Projections of future water balance and streamflow conditions can be obtained by forcing hydrological models with the output from climate models. In
[...] Read more.
There is considerable concern that the water resources of Central and Eastern Europe region can be adversely affected by climate change. Projections of future water balance and streamflow conditions can be obtained by forcing hydrological models with the output from climate models. In this study, we employed the SWAT hydrological model driven with an ensemble of nine bias-corrected EURO-CORDEX climate simulations to generate future hydrological projections for the Vistula and Odra basins in two future horizons (2024–2050 and 2074–2100) under two Representative Concentration Pathways (RCPs). The data set consists of three parts: (1) model inputs; (2) raw model outputs; (3) aggregated model outputs. The first one allows the users to reproduce the outputs or to create the new ones. The second one contains the simulated time series of 10 variables simulated by SWAT: precipitation, snow melt, potential evapotranspiration, actual evapotranspiration, soil water content, percolation, surface runoff, baseflow, water yield and streamflow. The third one consists of the multi-model ensemble statistics of the relative changes in mean seasonal and annual variables developed in a GIS format. The data set should be of interest of climate impact scientists, water managers and water-sector policy makers. In any case, it should be noted that projections included in this data set are associated with high uncertainties explained in this data descriptor paper. Full article
(This article belongs to the Special Issue Open Data and Robust & Reliable GIScience)
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