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IJGI
Special Issues
Mapping, Modeling and Prediction with VGI
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Mapping, Modeling and Prediction with VGI

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

Deadline for manuscript submissions: closed (1 September 2021) | Viewed by 14803

Special Issue Editors

Dr. Guiming Zhang

E-Mail Website
Guest Editor
Department of Geography & the Environment, University of Denver, Denver, CO 80208, USA
Interests: environmental modeling; volunteered geographic information; geospatial big data; geo-computation; GIScience
Special Issues, Collections and Topics in MDPI journals
Dr. A-Xing Zhu

E-Mail Website
Guest Editor
Department of Geography, University of Wisconsin-Madison, 550 N. Park St., Madison, WI 53706, USA
Interests: geographic information systems (GIS) and remote sensing (RS) techniques; artificial intelligence/machine learning; fuzzy logic; watershed system modeling and scenario analysis; intelligent geocomputing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Empowered by geospatial technologies, such as location-aware smartphones, many ordinary citizens are now acting as human sensors and voluntarily contributing geo-referenced ground observations regarding a broad array of geographic phenomena. Such geospatial data contributed by citizen volunteers are collectively referred to as volunteered geographic information (VGI), which broadly encompasses geographic information created through public participatory geographic information systems, citizen science, crowdsourcing, and social media, amongst other mechanisms. VGI has been revolutionizing the way geographic data, information, and knowledge are generated and disseminated. Moreover, VGI is an important source of geospatial big data that could potentially shift geographical research from traditional approaches toward a new “data-intensive” or “data-driven” paradigm.

VGI has great potential to reveal the spatial and temporal dynamics of the physical and social geographic phenomena under observation. This potential is realized by using VGI observations for 1) mapping the spatial and temporal distributions of geographic phenomena, 2) modeling the underlying processes shaping the spatial and temporal patterns, and 3) model-based prediction of geographic phenomena in space and time. New theoretical perspectives and analytical methods are being developed to address challenges facing VGI (e.g., data quality issues, biases) and to facilitate VGI applications along these lines. Some examples of such VGI applications are geographic feature mapping, biodiversity mapping, land use and land cover mapping, disaster mapping, species distribution modeling and prediction, traffic congestion modeling, human mobility modeling, etc. Furthermore, we are still witnessing the continuing growth and expansion of VGI applications.

We invite research articles and reviews broadly fitting in the general theme of “Mapping, Modeling, and Prediction with VGI” to be published in this Special Issue. Submissions to the Special Issue are open to all interested scholars. 

Assis. Prof. Guiming Zhang
Prof. A-Xing Zhu
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

  • volunteered geographic information (VGI)
  • public participatory geographic information systems
  • crowdsourcing geographic information
  • citizen science
  • social media
  • spatial analysis
  • mapping
  • modeling and prediction

Published Papers (5 papers)

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Research

15 pages, 6814 KiB  
Article
Detecting and Visualizing Observation Hot-Spots in Massive Volunteer-Contributed Geographic Data across Spatial Scales Using GPU-Accelerated Kernel Density Estimation
by Guiming Zhang
ISPRS Int. J. Geo-Inf. 2022, 11(1), 55; https://doi.org/10.3390/ijgi11010055 - 12 Jan 2022
Cited by 11 | Viewed by 2863
Abstract
Volunteer-contributed geographic data (VGI) is an important source of geospatial big data that support research and applications. A major concern on VGI data quality is that the underlying observation processes are inherently biased. Detecting observation hot-spots thus helps better understand the bias. Enabled [...] Read more.
Volunteer-contributed geographic data (VGI) is an important source of geospatial big data that support research and applications. A major concern on VGI data quality is that the underlying observation processes are inherently biased. Detecting observation hot-spots thus helps better understand the bias. Enabled by the parallel kernel density estimation (KDE) computational tool that can run on multiple GPUs (graphics processing units), this study conducted point pattern analyses on tens of millions of iNaturalist observations to detect and visualize volunteers’ observation hot-spots across spatial scales. It was achieved by setting varying KDE bandwidths in accordance with the spatial scales at which hot-spots are to be detected. The succession of estimated density surfaces were then rendered at a sequence of map scales for visual detection of hot-spots. This study offers an effective geovisualization scheme for hierarchically detecting hot-spots in massive VGI datasets, which is useful for understanding the pattern-shaping drivers that operate at multiple spatial scales. This research exemplifies a computational tool that is supported by high-performance computing and capable of efficiently detecting and visualizing multi-scale hot-spots in geospatial big data and contributes to expanding the toolbox for geospatial big data analytics. Full article
(This article belongs to the Special Issue Mapping, Modeling and Prediction with VGI)
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24 pages, 2177 KiB  
Article
A Bayesian Approach to Estimate the Spatial Distribution of Crowdsourced Radiation Measurements around Fukushima
by Carolynne Hultquist, Zita Oravecz and Guido Cervone
ISPRS Int. J. Geo-Inf. 2021, 10(12), 822; https://doi.org/10.3390/ijgi10120822 - 6 Dec 2021
Viewed by 2141
Abstract
Citizen-led movements producing spatio-temporal big data are potential sources of useful information during hazards. Yet, the sampling of crowdsourced data is often opportunistic and the statistical variations in the datasets are not typically assessed. There is a scientific need to understand the characteristics [...] Read more.
Citizen-led movements producing spatio-temporal big data are potential sources of useful information during hazards. Yet, the sampling of crowdsourced data is often opportunistic and the statistical variations in the datasets are not typically assessed. There is a scientific need to understand the characteristics and geostatistical variability of big spatial data from these diverse sources if they are to be used for decision making. Crowdsourced radiation measurements can be visualized as raw, often overlapping, points or processed for an aggregated comparison with traditional sources to confirm patterns of elevated radiation levels. However, crowdsourced data from citizen-led projects do not typically use a spatial sampling method so classical geostatistical techniques may not seamlessly be applied. Standard aggregation and interpolation methods were adapted to represent variance, sampling patterns, and the reliability of modeled trends. Finally, a Bayesian approach was used to model the spatial distribution of crowdsourced radiation measurements around Fukushima and quantify uncertainty introduced by the spatial data characteristics. Bayesian kriging of the crowdsourced data captures hotspots and the probabilistic approach could provide timely contextualized information that can improve situational awareness during hazards. This paper calls for the development of methods and metrics to clearly communicate spatial uncertainty by evaluating data characteristics, representing observational gaps and model error, and providing probabilistic outputs for decision making. Full article
(This article belongs to the Special Issue Mapping, Modeling and Prediction with VGI)
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16 pages, 3743 KiB  
Article
The Societal Echo of Severe Weather Events: Ambient Geospatial Information (AGI) on a Storm Event
by Rafael Hologa and Rüdiger Glaser
ISPRS Int. J. Geo-Inf. 2021, 10(12), 815; https://doi.org/10.3390/ijgi10120815 - 2 Dec 2021
Viewed by 2588
Abstract
The given article focuses on the benefit of harvested Ambient Geographic Information (AGI) as complementary data sources for severe weather events and provides methodical approaches for the spatio-temporal analysis of such data. The perceptions and awareness of Twitter users posting about severe weather [...] Read more.
The given article focuses on the benefit of harvested Ambient Geographic Information (AGI) as complementary data sources for severe weather events and provides methodical approaches for the spatio-temporal analysis of such data. The perceptions and awareness of Twitter users posting about severe weather patterns were explored as there were aspects not documented by official damage reports or derived from official weather data. We analysed Tweets regarding the severe storm event Friederike to map their spatio-temporal patterns. More than 50% of the retrieved >23.000 tweets were geocoded by applying supervised information retrievals, text mining, and geospatial analysis methods. Complementary, central topics were clustered and linked to official weather data for cross-evaluation. The data confirmed (1) a scale-dependent relationship between the wind speed and the societal echo. In addition, the study proved that (2) reporting activity is moderated by population distribution. An in-depth analysis of the crowds’ central topic clusters in response to the storm Friederike (3) revealed a plausible sequence of dominant communication contents during the severe weather event. In particular, the merge of the studied AGI and other environmental datasets at different spatio-temporal scales shows how such user-generated content can be a useful complementary data source to study severe weather events and the ensuing societal echo. Full article
(This article belongs to the Special Issue Mapping, Modeling and Prediction with VGI)
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16 pages, 2351 KiB  
Article
The Cyberdivisions Produced by the Design of VGI under the Platform Economy: The Case of the Restaurant Sector in TripAdvisor
by Daniela Ferreira, Mário Vale and Renato Miguel Carmo
ISPRS Int. J. Geo-Inf. 2021, 10(11), 717; https://doi.org/10.3390/ijgi10110717 - 22 Oct 2021
Viewed by 1649
Abstract
There is increasing concern regarding the inequalities produced by digital platforms based on volunteered geographic information (VGI). Several forms of inequalities have been observed, namely the unequal spatial coverage and the uneven levels of usage even in territories with good coverage. However, VGI [...] Read more.
There is increasing concern regarding the inequalities produced by digital platforms based on volunteered geographic information (VGI). Several forms of inequalities have been observed, namely the unequal spatial coverage and the uneven levels of usage even in territories with good coverage. However, VGI platforms under the logic of platform economy have generated other forms of spatial inequality that require more attention. The cyberspace within VGI platforms is producing different cyberspatialities, especially with the platformisation processes that have made this type of inequality more evident. With this in mind, this paper aims to explore the making of cyberdivisions under the platform economy. We argue that the design of VGI within digital platforms is generating cyberdivisions in the urban economy. This research is particularly interested in exploring the restaurant sector in the TripAdvisor platform in the city of Lisbon. In this paper, we draw on a representative survey by questionnaire to restaurant firm owners. We obtained 385 responses out of a universe of 3453 restaurants. This sample provides a confidence level of 95% and a confidence interval of 5%. In addition, we webscraped data from TripAdvisor to assess its coverage in Lisbon. This study reveals that there are different forms of online presence and engagement which have generated cyberdivisions. Full article
(This article belongs to the Special Issue Mapping, Modeling and Prediction with VGI)
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19 pages, 6194 KiB  
Article
An Evaluation of Street Dynamic Vitality and Its Influential Factors Based on Multi-Source Big Data
by Xin Guo, Hongfei Chen and Xiping Yang
ISPRS Int. J. Geo-Inf. 2021, 10(3), 143; https://doi.org/10.3390/ijgi10030143 - 5 Mar 2021
Cited by 43 | Viewed by 4334
Abstract
Urban vitality is an important indicator of urban development capacity. Streets’ metrics can depict intro-urban fabrics and physiognomy in detail, and thus street vitality affected by street metrics is a concrete manifestation of urban vitality. However, few studies have evaluated dynamic vitality or [...] Read more.
Urban vitality is an important indicator of urban development capacity. Streets’ metrics can depict intro-urban fabrics and physiognomy in detail, and thus street vitality affected by street metrics is a concrete manifestation of urban vitality. However, few studies have evaluated dynamic vitality or explored how it is influenced by land use. To bridge this gap, we fully evaluated street dynamic vitality and explored how to enhance the street dynamic vitality by changing the distribution and combination of land use. Specifically, we examined the street dynamic vitality and land use diversity in the main urban zone of Xining city in China using mobile communication and point of interest data, adopted optimized K-means clustering to identify street dynamic vitality types, evaluated the classification result based on vitality intensity and vitality stability, and explored the link between land use and dynamic vitality. Since vitality intensity limitations were found in describing street dynamic vitality, it was necessary to introduce vitality stability. We also found a positive correlation between the vitality intensity and land use density, there were outstanding traffic facilities in high-intensity vitality streets, and improving the abundance and uniformity of land use was beneficial to increase vitality stability. Overall, describing street vitality from a dynamic perspective can improve resource utilization efficiency and rationally plan layouts. Full article
(This article belongs to the Special Issue Mapping, Modeling and Prediction with VGI)
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