Special Issue "Geospatial Data"

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

Deadline for manuscript submissions: closed (31 December 2016).

Special Issue Editors

Prof. Dr. Jamal Jokar Arsanjani
Website1 Website2
Guest Editor
Dr. Marco Helbich
Website
Guest Editor
Department of Human Geography and Spatial Planning, Utrecht University, The Netherlands
Interests: spatial and spatiotemporal analyses; computational urban geography; GIS modeling; real estate economics; active transportation; built and natural environment; health geography
Special Issues and Collections in MDPI journals
Dr. Amin Tayyebi

Guest Editor
Monsanto Company, 800 North Lindbergh Blvd. St. Louis, MI 63167, USA
Interests: land-use and land-cover change; data mining; spatial decision support system; ecosystem services; Big data; Climate change; unmanned aerial vehicle; geomatic engineering
Dr. Amit Birenboim
Website
Guest Editor
Department of Human Geography and Spatial Planning, Faculty of Geosciences, Utrecht University, The Netherlands

Special Issue Information

Dear Colleagues,

Geographic data are produced or collected by scientists in different ways to study environmental problems. Although online geographic data are accessible across the globe, there is a lack of a unique platform that enables scientists to share geographic data produced locally. Such a lack of scientific data communication has limited scholars to share datasets in a professional and credible way. A large portion of innovative and novel ideas could not come to action due to unavailability of data or the scientific findings could have never been retested and verified, as the data have been always kept as a “black box”.

Thanks to the recent efforts on supporting reproducible research and credible scientific data, scholars may publish their data in the scientific media and give credit to their data collection and data processing efforts. There are two groups of geographic data that are encouraged to be shared with other scholars: 1) geographical data, which are newly generated from scratch, and 2) geographical data produced as outcome of data processing. Nowadays, scientific communities attempt to make the scientific data transparent, so that the behind-the-scenes of data is also shared and published.

We would like to invite you to submit articles addressing the process of geographic data collection, acquisition, processing, and management, so that these data will be (re)used by other scholars and add value to the preliminary published results from them. Potential datasets include, but are not limited to, data and methods on:

  • Earth observation systems (e.g., remote sensing, geo-sensor networks)
  • Citizen observatories
  • Public health
  • Biodiversity, farming, forestry, environment, and ecology
  • Climate change
  • Geological measurements
  • Social networks and social media (e.g., Twitter, Flickr, Instagram)
  • Open source data and open government data
  • Archaeology, culture, tourism
  • Point cloud data (e.g., LiDAR)
  • Natural hazards and disasters
  • Movement data (e.g., GPS)
  • Mobile phone data
  • Indoor and outdoor environments
  • Unmanned aerial vehicle and drones

 

Dr. Jamal Jokar Arsanjani
Dr. Marco Helbich
Dr. Amin Tayyebi
Dr. Amit Birenboim
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) for publication in this open access journal is 1000 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

  • Earth observation and citizen observation data
  • Geographical data (e.g., 2D, 3D, 4D data)
  • Big data
  • Data on biodiversity, farming, forestry, environment, and ecology
  • Climate change data
  • Social media data (e.g., Twitter, Flickr, Instagram)
  • Archaeological, cultural, tourism data
  • Point cloud data (e.g., LiDAR)
  • Natural hazards and disasters
  • GPS data
  • Mobile phone data
  • Public health data

Published Papers (9 papers)

Order results
Result details
Select all
Export citation of selected articles as:

Research

Jump to: Other

Open AccessArticle
The Effectiveness of Geographical Data in Multi-Criteria Evaluation of Landscape Services †
Data 2017, 2(1), 9; https://doi.org/10.3390/data2010009 - 06 Feb 2017
Cited by 7
Abstract
The aim of the paper is to map and evaluate the state of the multifunctional landscape of the municipality of Naples (Italy) and its surroundings, through a Spatial Decision-Making support system (SDSS) combining geographic information system (GIS) and a multi-criteria method an analytic [...] Read more.
The aim of the paper is to map and evaluate the state of the multifunctional landscape of the municipality of Naples (Italy) and its surroundings, through a Spatial Decision-Making support system (SDSS) combining geographic information system (GIS) and a multi-criteria method an analytic hierarchy process (AHP). We conceive a knowledge-mapping-evaluation (KME) framework in order to investigate the landscape as a complex system. The focus of the proposed methodology involving data gathering and processing. Therefore, both the authoritative and the unofficial sources, e.g., volunteered geographical information (VGI), are useful tools to enhance the information flow whenever quality assurance is performed. Thus, the maps of spatial criteria are useful for problem structuring and prioritization by considering the availability of context-aware data. Finally, the identification of landscape services (LS) and ecosystem services (ES) can improve the decision-making processes within a multi-stakeholders perspective involving the evaluation of the trade-off. The results show multi-criteria choropleth maps of the LS and ES with the density of services, the spatial distribution, and the surrounding benefits. Full article
(This article belongs to the Special Issue Geospatial Data)
Show Figures

Figure 1

Open AccessArticle
How to Make Sense of Team Sport Data: From Acquisition to Data Modeling and Research Aspects
Data 2017, 2(1), 2; https://doi.org/10.3390/data2010002 - 01 Jan 2017
Cited by 24
Abstract
Automatic and interactive data analysis is instrumental in making use of increasing amounts of complex data. Owing to novel sensor modalities, analysis of data generated in professional team sport leagues such as soccer, baseball, and basketball has recently become of concern, with potentially [...] Read more.
Automatic and interactive data analysis is instrumental in making use of increasing amounts of complex data. Owing to novel sensor modalities, analysis of data generated in professional team sport leagues such as soccer, baseball, and basketball has recently become of concern, with potentially high commercial and research interest. The analysis of team ball games can serve many goals, e.g., in coaching to understand effects of strategies and tactics, or to derive insights improving performance. Also, it is often decisive to trainers and analysts to understand why a certain movement of a player or groups of players happened, and what the respective influencing factors are. We consider team sport as group movement including collaboration and competition of individuals following specific rule sets. Analyzing team sports is a challenging problem as it involves joint understanding of heterogeneous data perspectives, including high-dimensional, video, and movement data, as well as considering team behavior and rules (constraints) given in the particular team sport. We identify important components of team sport data, exemplified by the soccer case, and explain how to analyze team sport data in general. We identify challenges arising when facing these data sets and we propose a multi-facet view and analysis including pattern detection, context-aware analysis, and visual explanation. We also present applicable methods and technologies covering the heterogeneous aspects in team sport data. Full article
(This article belongs to the Special Issue Geospatial Data)
Show Figures

Figure 1

Other

Jump to: Research

Open AccessData Descriptor
Long-Term Land Cover Data for the Lower Peninsula of Michigan, 2010–2050
Data 2017, 2(2), 16; https://doi.org/10.3390/data2020016 - 05 May 2017
Cited by 4
Abstract
Land cover data are often used to examine the impacts of landscape alterations on the environment from the local to global scale. Although various agencies produce land cover data at various spatial scales, data are still limited at the regional scale over extended [...] Read more.
Land cover data are often used to examine the impacts of landscape alterations on the environment from the local to global scale. Although various agencies produce land cover data at various spatial scales, data are still limited at the regional scale over extended timescales. This is a critical data gap since decision-makers often use future and long-term land cover maps to develop effective policies for sustainable environmental systems. As a result, land change science incorporates common data mining tools to create future land cover maps that extend over long timescales. This study applied one of the well-known land cover change models, called Land Transformation Model (LTM), to produce urbanization maps for the Lower Peninsula of Michigan in United States from 2010 to 2050 with five year intervals. Long-term urbanization data in the Lower Peninsula of Michigan can be used in various environmental studies such as assessing the impact of future urbanization on climate change, water quality, food security and biodiversity. Full article
(This article belongs to the Special Issue Geospatial Data)
Show Figures

Figure 1

Open AccessData Descriptor
Data on Healthy Food Accessibility in Amsterdam, The Netherlands
Data 2017, 2(1), 7; https://doi.org/10.3390/data2010007 - 26 Jan 2017
Cited by 2
Abstract
This data descriptor introduces data on healthy food supplied by supermarkets in the city of Amsterdam, The Netherlands. In addition to two neighborhood variables (i.e., share of autochthons and average housing values), the data comprises three street network-based accessibility measures derived from analyses [...] Read more.
This data descriptor introduces data on healthy food supplied by supermarkets in the city of Amsterdam, The Netherlands. In addition to two neighborhood variables (i.e., share of autochthons and average housing values), the data comprises three street network-based accessibility measures derived from analyses using a geographic information system. Data are provided on a spatial micro-scale utilizing grid cells with a spatial resolution of 100 m. We explain how the data were collected and pre-processed, and how alternative analyses can be set up. To illustrate the use of the data, an example is provided using the R programming language. Full article
(This article belongs to the Special Issue Geospatial Data)
Show Figures

Figure 1

Open AccessData Descriptor
Land Cover Data for the Mississippi–Alabama Barrier Islands, 2010–2011
Data 2016, 1(3), 16; https://doi.org/10.3390/data1030016 - 30 Sep 2016
Cited by 1
Abstract
Land cover on the Mississippi–Alabama barrier islands was surveyed in 2010–2011 as part of continuing research on island geomorphic and vegetation dynamics following the 2005 impact of Hurricane Katrina. Results of the survey include sub-meter GPS location, a listing of dominant vegetation species [...] Read more.
Land cover on the Mississippi–Alabama barrier islands was surveyed in 2010–2011 as part of continuing research on island geomorphic and vegetation dynamics following the 2005 impact of Hurricane Katrina. Results of the survey include sub-meter GPS location, a listing of dominant vegetation species and field photographs recorded at 375 sampling locations distributed among Cat, West Ship, East Ship, Horn, Sand, Petit Bois and Dauphin Islands. The survey was conducted in a period of intensive remote sensing data acquisition over the northern Gulf of Mexico by federal, state and commercial organizations in response to the 2010 Macondo Well (Deepwater Horizon) oil spill. The data are useful in providing ground reference information for thematic classification of remotely-sensed imagery, and a record of land cover which may be used in future research. Full article
(This article belongs to the Special Issue Geospatial Data)
Show Figures

Graphical abstract

Open AccessData Descriptor
Technical Guidelines to Extract and Analyze VGI from Different Platforms
Data 2016, 1(3), 15; https://doi.org/10.3390/data1030015 - 24 Sep 2016
Cited by 3
Abstract
An increasing number of Volunteered Geographic Information (VGI) and social media platforms have been continuously growing in size, which have provided massive georeferenced data in many forms including textual information, photographs, and geoinformation. These georeferenced data have either been actively contributed (e.g., adding [...] Read more.
An increasing number of Volunteered Geographic Information (VGI) and social media platforms have been continuously growing in size, which have provided massive georeferenced data in many forms including textual information, photographs, and geoinformation. These georeferenced data have either been actively contributed (e.g., adding data to OpenStreetMap (OSM) or Mapillary) or collected in a more passive fashion by enabling geolocation whilst using an online platform (e.g., Twitter, Instagram, or Flickr). The benefit of scraping and streaming these data in stand-alone applications is evident, however, it is difficult for many users to script and scrape the diverse types of these data. On 14 June 2016, a pre-conference workshop at the AGILE 2016 conference in Helsinki, Finland was held. The workshop was called “LINK-VGI: LINKing and analyzing VGI across different platforms”. The workshop provided an opportunity for interested researchers to share ideas and findings on cross-platform data contributions. One portion of the workshop was dedicated to a hands-on session. In this session, the basics of spatial data access through selected Application Programming Interfaces (APIs) and the extraction of summary statistics of the results were illustrated. This paper presents the content of the hands-on session including the scripts and guidelines for extracting VGI data. Researchers, planners, and interested end-users can benefit from this paper for developing their own application for any region of the world. Full article
(This article belongs to the Special Issue Geospatial Data)
Show Figures

Figure 1

Open AccessData Descriptor
A 1973–2008 Archive of Climate Surfaces for NW Maghreb
Data 2016, 1(2), 8; https://doi.org/10.3390/data1020008 - 27 Jun 2016
Cited by 3
Abstract
Climate archives are time series. They are used to assess temporal trends of a climate-dependent target variable, and to make climate atlases. A high-resolution gridded dataset with 1728 layers of monthly mean maximum, mean and mean minimum temperatures and precipitation for the NW [...] Read more.
Climate archives are time series. They are used to assess temporal trends of a climate-dependent target variable, and to make climate atlases. A high-resolution gridded dataset with 1728 layers of monthly mean maximum, mean and mean minimum temperatures and precipitation for the NW Maghreb (28°N–37.3°N, 12°W–12°E, ~1-km resolution) from 1973 through 2008 is presented. The surfaces were spatially interpolated by ANUSPLIN, a thin-plate smoothing spline technique approved by the World Meteorological Organization (WMO), from georeferenced climate records drawn from the Global Surface Summary of the Day (GSOD) and the Global Historical Climatology Network-Monthly (GHCN-Monthly version 3) products. Absolute errors for surface temperatures are approximately 0.5 °C for mean and mean minimum temperatures, and peak up to 1.76 °C for mean maximum temperatures in summer months. For precipitation, the mean absolute error ranged from 1.2 to 2.5 mm, but very low summer precipitation caused relative errors of up to 40% in July. The archive successfully captures climate variations associated with large to medium geographic gradients. This includes the main aridity gradient which increases in the S and SE, as well as its breaking points, marked by the Atlas mountain range. It also conveys topographic effects linked to kilometric relief mesoforms. Full article
(This article belongs to the Special Issue Geospatial Data)
Show Figures

Figure 1

Open AccessData Descriptor
Open-Access Geographic Data for the Argali Habitat in the Southeastern Tajik Pamirs
Data 2016, 1(1), 5; https://doi.org/10.3390/data1010005 - 12 May 2016
Cited by 1
Abstract
Seven Geographic Information System (GIS) layers comprise this dataset intended for understanding the Marco Polo argali habitat in the southeastern Tajikistan Pamirs (37°33′ N, 74°09′ E). Extensive remote sensing habitat data processing and field data analysis of the Marco Polo sheep study area [...] Read more.
Seven Geographic Information System (GIS) layers comprise this dataset intended for understanding the Marco Polo argali habitat in the southeastern Tajikistan Pamirs (37°33′ N, 74°09′ E). Extensive remote sensing habitat data processing and field data analysis of the Marco Polo sheep study area have yielded these layers that are now available online to download and for use by other researchers interested in studying the argali patterns and habitat suitability in the southeastern Tajik Pamirs. It is important to note that the layers were generated using a 30-m Landsat ETM image and field data from 2012. Full article
(This article belongs to the Special Issue Geospatial Data)
Open AccessData Descriptor
A Unified Cropland Layer at 250 m for Global Agriculture Monitoring
Data 2016, 1(1), 3; https://doi.org/10.3390/data1010003 - 19 Mar 2016
Cited by 37
Abstract
Accurate and timely information on the global cropland extent is critical for food security monitoring, water management and earth system modeling. Principally, it allows for analyzing satellite image time-series to assess the crop conditions and permits isolation of the agricultural component to focus [...] Read more.
Accurate and timely information on the global cropland extent is critical for food security monitoring, water management and earth system modeling. Principally, it allows for analyzing satellite image time-series to assess the crop conditions and permits isolation of the agricultural component to focus on food security and impacts of various climatic scenarios. However, despite its critical importance, accurate information on the spatial extent, cropland mapping with remote sensing imagery remains a major challenge. Following an exhaustive identification and collection of existing land cover maps, a multi-criteria analysis was designed at the country level to evaluate the fitness of a cropland map with regards to four dimensions: its timeliness, its legend, its resolution adequacy and its confidence level. As a result, a Unified Cropland Layer that combines the fittest products into a 250 m global cropland map was assembled. With an evaluated accuracy ranging from 82% to 95%, the Unified Cropland Layer successfully improved the accuracy compared to single global products. Full article
(This article belongs to the Special Issue Geospatial Data)
Show Figures

Figure 1

Back to TopTop