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Open Data and Robust & Reliable GIScience
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Open Data and Robust & Reliable GIScience

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

Deadline for manuscript submissions: closed (30 June 2019) | Viewed by 38098

Special Issue Editors

Dr. Daniel Z. Sui

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

E-Mail Website
Guest Editor
Department of Informatics, Urban Informatics, Spatial Computation Lab, New Jersey Institute of Technology, Newark, NJ 07102, USA
Interests: geodesign; geographic information science; spatial network science; urban complexity; urban informatics
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Jamal Jokar Arsanjani

E-Mail Website
Guest Editor
Geoinformatics and Earth Observation Research Group, Department of Planning, Aalborg University Copenhagen, A.C. Meyers Vænge 15, DK-2450 Copenhagen, Denmark
Interests: citizen observations; earth observation; geocomputation; GEO-artificial intelligence; data quality; environmental monitoring and assessment
Special Issues, Collections and Topics in MDPI journals

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 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. Data 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 1600 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

  • 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

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Published Papers (6 papers)

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2635 KiB  
Article
Earth Observation for Citizen Science Validation, or Citizen Science for Earth Observation Validation? The Role of Quality Assurance of Volunteered Observations
by Didier G. Leibovici, Jamie Williams, Julian F. Rosser, Crona Hodges, Colin Chapman, Chris Higgins and Mike J. Jackson
Data 2017, 2(4), 35; https://doi.org/10.3390/data2040035 - 23 Oct 2017
Cited by 6 | Viewed by 6464
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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11 pages, 3182 KiB  
Data Descriptor
Sea Ice Climate Normals for Seasonal Ice Monitoring of Arctic and Sub-Regions
by Ge Peng, Anthony Arguez, Walter N. Meier, Freja Vamborg, Jake Crouch and Philip Jones
Data 2019, 4(3), 122; https://doi.org/10.3390/data4030122 - 10 Aug 2019
Cited by 5 | Viewed by 6075
Abstract
The climate normal, that is, the latest three full-decade average, of Arctic sea ice parameters is useful for baselining the sea ice state. A baseline ice state on both regional and local scales is important for monitoring how the current regional and local [...] Read more.
The climate normal, that is, the latest three full-decade average, of Arctic sea ice parameters is useful for baselining the sea ice state. A baseline ice state on both regional and local scales is important for monitoring how the current regional and local states depart from their normal to understand the vulnerability of marine and sea ice-based ecosystems to the changing climate conditions. Combined with up-to-date observations and reliable projections, normals are essential to business strategic planning, climate adaptation and risk mitigation. In this paper, monthly and annual climate normals of sea ice parameters (concentration, area, and extent) of the whole Arctic Ocean and 15 regional divisions are derived for the period of 1981–2010 using monthly satellite sea ice concentration estimates from a climate data record (CDR) produced by NOAA and the National Snow and Ice Data Center (NSIDC). Basic descriptions and characteristics of the normals are provided. Empirical Orthogonal Function (EOF) analysis has been utilized to describe spatial modes of sea ice concentration variability and how the corresponding principal components change over time. To provide users with basic information on data product accuracy and uncertainty, the climate normal values of Arctic sea ice extents (SIE) are compared with that of other products, including a product from NSIDC and two products from the Copernicus Climate Change Service (C3S). The SIE differences between different products are in the range of 2.3–4.5% of the CDR SIE mean. Additionally, data uncertainty estimates are represented by using the range (the difference between the maximum and minimum), standard deviation, 10th and 90th percentiles, and the first, second, and third quartile distribution of all monthly values, a distinct feature of these sea ice normal products. Full article
(This article belongs to the Special Issue Open Data and Robust & Reliable GIScience)
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20 pages, 5156 KiB  
Data Descriptor
Agro-Climatic Data by County: A Spatially and Temporally Consistent U.S. Dataset for Agricultural Yields, Weather and Soils
by Seong Do Yun and Benjamin M. Gramig
Data 2019, 4(2), 66; https://doi.org/10.3390/data4020066 - 8 May 2019
Cited by 11 | Viewed by 8017
Abstract
Agro-climatic data by county (ACDC) is designed to provide the major agro-climatic variables from publicly available spatial data sources to diverse end-users. ACDC provides USDA NASS annual (1981–2015) crop yields for corn, soybeans, upland cotton and winter wheat by county. Customizable growing degree [...] Read more.
Agro-climatic data by county (ACDC) is designed to provide the major agro-climatic variables from publicly available spatial data sources to diverse end-users. ACDC provides USDA NASS annual (1981–2015) crop yields for corn, soybeans, upland cotton and winter wheat by county. Customizable growing degree days for 1 °C intervals between −60 °C and +60 °C, and total precipitation for two different crop growing seasons from the PRISM weather data are included. Soil characteristic data from USDA-NRCS gSSURGO are also provided for each county in the 48 contiguous US states. All weather and soil data are processed to include only data for land being used for non-forestry agricultural uses based on the USGS NLCD land cover/land use data. This paper explains the numerical and geo-computational methods and data generating processes employed to create ACDC from the original data sources. Essential considerations for data management and use are discussed, including the use of the agricultural mask, spatial aggregation and disaggregation, and the computational requirements for working with the raw data sources. Full article
(This article belongs to the Special Issue Open Data and Robust & Reliable GIScience)
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8 pages, 6150 KiB  
Data Descriptor
UAV-Based 3D Point Clouds of Freshwater Fish Habitats, Xingu River Basin, Brazil
by Margaret Kalacska, Oliver Lucanus, Leandro Sousa, Thiago Vieira and Juan Pablo Arroyo-Mora
Data 2019, 4(1), 9; https://doi.org/10.3390/data4010009 - 10 Jan 2019
Cited by 5 | Viewed by 4638
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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8 pages, 1001 KiB  
Data Descriptor
World Ocean Isopycnal Level Absolute Geostrophic Velocity (WOIL-V) Inverted from GDEM with the P-Vector Method
by Peter C. Chu
Data 2018, 3(1), 1; https://doi.org/10.3390/data3010001 - 7 Jan 2018
Cited by 4 | Viewed by 4382
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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676 KiB  
Data Descriptor
CHASE-PL—Future Hydrology Data Set: Projections of Water Balance and Streamflow for the Vistula and Odra Basins, Poland
by Mikołaj Piniewski, Mateusz Szcześniak and Ignacy Kardel
Data 2017, 2(2), 14; https://doi.org/10.3390/data2020014 - 26 Apr 2017
Cited by 9 | Viewed by 6971
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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