Special Issue "Uncertainty Modeling in Spatial Data Analysis"

Special Issue Editors

Dr. Jamal Jokar Arsanjani
E-Mail Website
Guest Editor
Prof. Mahmoud Reza R. Delavar
E-Mail Website
Guest Editor
Center of Excellence in Geomatic Eng. in Disaster Management, School of Surveying and Geospatial Eng., College of Engineering, University of Tehran, Tehran, P.O.Box: 11155-4563,I.R. of Iran.
Tel. +98-21-61114257; Fax: +98 21 88008837
Interests: spatial data quality and uncertainty modeling; spatio-temporal GIS; spatial data fusion; disaster management; smart city; land administration system; SDI; spatio-temporal data mining
Dr. Gerhard Navratil
E-Mail
Guest Editor
Department for Geodesy and Geoinformation, Vienna University of Technology, Gusshausstr. 27-29/E120.2, A-1040 Vienna, Austria
Interests: land administration; cadastre; navigation; data quality; communication
Special Issues and Collections in MDPI journals
Dr. Ana-Maria Olteanu-Raimond
E-Mail Website
Guest Editor
Research Laboratory in Geographical Information Sciences and Technologies for the city and the sustainable territories (LaSTIG), Institut National de l’Information Geographique et Forestière (IGN), 73 avenue de Paris, 94 165 Saint-Mandé, France
Interests: heterogeneous data integration; spatial qualitative reasoning; data quality; volunteered geographic information (VGI); land use/ land cover monitoring; fuzzy models

Special Issue Information

Dear Colleagues,

At present, we are facing a revolution in geospatial data collection, which is resulting in an unprecedented amount of geospatial data. These are provided from a wide range of sources, e.g., public authorities, the private sector, crowdsourcing, and citizen science activities being captured through the classical and emerging instruments—e.g., space/air-borne sensors, geo-sensor networks, volunteers, and social media. Therefore, it is crucial and timely to develop and advance theories and methods to facilitate the integration, analysis, and validation of geospatial data, especially for the recent and emerging data sources. The ISPRS working group IV/3 focuses on spatial data fusion algorithms, spatial statistics, spatial analysis, data mining and optimization, data quality, and information uncertainty assessment.

This Special Issue aims at presenting an outlet for research and review articles that can address the following topics. The topics include but are not limited to:

  • Data mining methods applicable to spatial data;
  • Quality assessment of spatial data and products;
  • Data/information fusion;
  • Spatial data optimization;
  • Innovative algorithms and tools for interoperability of geodata;
  • Multi-criteria decision making methods;
  • Crowd-sourced data, volunteered geographic/geospatial information, citizen science;
  • Address challenges in big spatial data in GISciences;
  • Spatial statistics;
  • Uncertainty assessment and modeling;
  • Spatial decision support system and incomplete/imperfect data.

Papers must be original contributions, not previously published or submitted to other journals. Papers published or submitted for publication in conference proceedings may be considered if they are considerably extended and improved. Authors must use the provided Microsoft Word template or LaTeX template to prepare their manuscript and must follow the instructions for authors at https://www.mdpi.com/journal/ijgi/instructions.

Please send an email to the Guest Editors to let them know your interest in contributing a paper as soon as possible, preferably with a tentative title and a brief abstract.

Assoc. Prof. Jamal Jokar Arsanjani
Prof. Mahmoud Reza Delavar
Dr. Gerhard Navratil
Dr. Ana-Maria Olteanu-Raimond
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. 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 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

  • data quality
  • uncertainty assessment
  • data fusion
  • data mining
  • spatial statistics
  • big geodata
  • data interoperability

Published Papers (4 papers)

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Research

Open AccessArticle
Uncertainty Visualization of Transport Variance in a Time-Varying Ensemble Vector Field
ISPRS Int. J. Geo-Inf. 2020, 9(1), 19; https://doi.org/10.3390/ijgi9010019 - 01 Jan 2020
Abstract
Uncertainty analysis of a time-varying ensemble vector field is a challenging topic in geoscience. Due to the complex data structure, the uncertainty of a time-varying ensemble vector field is hard to quantify and analyze. Measuring the differences between pathlines is an effective way [...] Read more.
Uncertainty analysis of a time-varying ensemble vector field is a challenging topic in geoscience. Due to the complex data structure, the uncertainty of a time-varying ensemble vector field is hard to quantify and analyze. Measuring the differences between pathlines is an effective way to compute the uncertainty. However, existing metrics are not accurate enough or are sensitive to outliers; thus, a comprehensive tool for the further analysis of the uncertainty of transport patterns is required. In this paper, we propose a novel framework for quantifying and analyzing the uncertainty of an ensemble vector field. Based on the classical edit distance on real sequence (EDR) method, a robust and accurate metric was proposed to measure the pathline uncertainty. Considering the spatial continuity, we computed the transport variance of the neighborhood of a location, and evaluated the uncertainty correlation between each location and its neighborhood by using the local Moran’s I. Based on the proposed uncertainty measurements, a visual analysis system called UP-Vis (uncertainty pathline visualization) was developed to interactively explore the uncertainty. It provides an overview of the uncertainty and supports detailed exploration of transport patterns at a selected location, and allows for the comparison of transport patterns between a location and its neighborhood. Through pathline clustering, the major trends of the ensemble pathline at a location were extracted. Moreover, a glyph was designed to intuitively display the transport direction and diverging degree of each cluster. For the uncertainty analysis of the neighborhood, a comparison view was designed to compare the transport patterns between a location and its neighborhood in detail. A synthetic data set and weather simulation data set were used in our experiments. The evaluation and case studies demonstrated that the proposed framework can measure the uncertainty effectively and help users to comprehensively explore uncertainty transport patterns. Full article
(This article belongs to the Special Issue Uncertainty Modeling in Spatial Data Analysis)
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Open AccessArticle
UTSM: A Trajectory Similarity Measure Considering Uncertainty Based on an Amended Ellipse Model
ISPRS Int. J. Geo-Inf. 2019, 8(11), 518; https://doi.org/10.3390/ijgi8110518 - 16 Nov 2019
Abstract
Measuring the similarity between a pair of trajectories is the basis of many spatiotemporal clustering methods and has wide applications in trajectory pattern mining. However, most measures of trajectory similarity in the literature are based on precise models that ignore the inherent uncertainty [...] Read more.
Measuring the similarity between a pair of trajectories is the basis of many spatiotemporal clustering methods and has wide applications in trajectory pattern mining. However, most measures of trajectory similarity in the literature are based on precise models that ignore the inherent uncertainty in trajectory data recorded by sensors. Traditional computing or mining approaches that assume the preciseness and exactness of trajectories therefore risk underperforming or returning incorrect results. To address the problem, we propose an amended ellipse model, which takes both interpolation error and positioning error into account by making use of the motion features of the trajectory to compute the ellipse’s shape parameters. A specialized similarity measure method considering uncertainty called the Uncertain Trajectory Similarity Measure (UTSM) based on the model is also proposed. We validate the approach experimentally on both synthetic and real-world data and show that UTSM is not only more robust to noise and outliers, but also more tolerant of different sample frequencies and asynchronous sampling of trajectories. Full article
(This article belongs to the Special Issue Uncertainty Modeling in Spatial Data Analysis)
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Open AccessArticle
A Drift-of-Stay Pattern Extraction Method for Indoor Pedestrian Trajectories for the Error and Accuracy Assessment of Indoor Wi-Fi Positioning
ISPRS Int. J. Geo-Inf. 2019, 8(11), 468; https://doi.org/10.3390/ijgi8110468 - 23 Oct 2019
Abstract
The uncertainty of indoor Wi-Fi positioning is susceptible to many factors, such as sensor distribution, the internal environment (e.g., of a shopping mall), differences between receivers, and the flow of people. In this paper, an indoor pedestrian trajectory pattern mining approach for the [...] Read more.
The uncertainty of indoor Wi-Fi positioning is susceptible to many factors, such as sensor distribution, the internal environment (e.g., of a shopping mall), differences between receivers, and the flow of people. In this paper, an indoor pedestrian trajectory pattern mining approach for the assessment of the error and accuracy of indoor Wi-Fi positioning is proposed. First, the stay points of the customer were extracted from the pedestrian trajectories based on the spatiotemporal staying patterns of the customers in a shopping mall. Second, the drift points were distinguished from the stay points through analysis of noncustomer behavior patterns. Finally, the drift points were presented to calculate the errors in the pedestrian trajectories for the accuracy assessment of the indoor Wi-Fi positioning system. A one-month indoor pedestrian trajectories dataset from the Xinxiang Baolong shopping mall in Henan Province, China, was used for the assessment of the error and accuracy values with the proposed approach. The experimental results were verified by incorporating the distribution of the AP sensors. The proposed approach using big data pattern mining can explore the error distribution of indoor positioning systems, which can provide strong support for improving indoor positioning accuracy in the future. Full article
(This article belongs to the Special Issue Uncertainty Modeling in Spatial Data Analysis)
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Open AccessArticle
A Multilevel Mapping Strategy to Calculate the Information Content of Remotely Sensed Imagery
ISPRS Int. J. Geo-Inf. 2019, 8(10), 464; https://doi.org/10.3390/ijgi8100464 - 22 Oct 2019
Abstract
Considering the multiscale characteristics of the human visual system and any natural scene, the spatial autocorrelation of remotely sensed imagery, and the multilevel spatial structure of ground targets in remote sensing images, an information-measurement approach based on a single-level geometrical mapping model can [...] Read more.
Considering the multiscale characteristics of the human visual system and any natural scene, the spatial autocorrelation of remotely sensed imagery, and the multilevel spatial structure of ground targets in remote sensing images, an information-measurement approach based on a single-level geometrical mapping model can only reflect partial feature information at a single level (e.g., global statistical information and local spatial distribution information). The single mapping model cannot validly characterize the information of the multilevel and multiscale features of the spatial structures inherent in remotely sensed images. Additionally, the validity, practicability, and application range of the results of single-level mapping models are greatly limited in practical applications. In this paper, we present the multilevel geometrical mapping entropy (MGME) model to evaluate the information content of related attribute characteristics contained in remotely sensed images. Subsequently, experimental images with different types of objects, including reservoir area, farmland, water area (i.e., water and trees), and mountain area, were used to validate the performance of the proposed method. Experimental results show that the proposed method can not only reflect the difference in the information of images in terms of spectrum features, spatial structural features, and visual perception but also eliminates the inadequacy of a single-level mapping model. That is, the multilevel mapping strategy is feasible and valid. Additionally, the vector set of the MGME method and its standard deviation (Std) value can be used to further explore and study the spatial dependence of ground scenes and the difference in the spatial structural characteristics of different objects. Full article
(This article belongs to the Special Issue Uncertainty Modeling in Spatial Data Analysis)
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