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Uncertainty Modeling in Spatial Data Analysis

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

Deadline for manuscript submissions: closed (30 November 2021) | Viewed by 27084

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Special Issue Editors

Prof. Dr. Jamal Jokar Arsanjani

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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
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Prof. Dr. Mahmoud Reza Delavar

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Guest Editor

Center of Excellence in Geomatic Engineering in Disaster Management, School of Surveying and Geospatial Eng., College of Eng., University of Tehran, Tehran, Iran
Interests: spatial data quality; data reliability; data trustworthiness; data liability; heterogeneous data integration; fitness for use; smart data fusion
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Dr. Gerhard Navratil

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Guest Editor

Department for Geodesy and Geoinformation, TU Wien, A-1040 Vienna, Austria
Interests: land administration; cadastre; land use planning; property valuation; data quality; navigation; spatial decision making; volunteered geographic information
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Dr. Ana-Maria Olteanu-Raimond

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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 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

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

Published Papers (9 papers)

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Research

18 pages, 7036 KiB

Open AccessArticle

Uncertainty of Historic GLAD Forest Data in Temperate Climates and Implications for Forest Change Modelling

by Clare Price and Paul Elsner

ISPRS Int. J. Geo-Inf. 2022, 11(3), 177; https://doi.org/10.3390/ijgi11030177 - 05 Mar 2022

Cited by 1 | Viewed by 2159

Abstract

Forest loss and degradation are central problems in the context of climate change and biodiversity conservation. The identification of areas of loss relies on accurate base maps. Central datasets in this context are the products of the Global Land Analysis & Discovery (GLAD) project. Although the GLAD forest cover products are primarily intended to serve as a near real-time flag for areas of forest loss, its historic datasets are increasingly also being used in ways that go beyond this initial focus. To date, very little information is available on the performance of GLAD data in temperate regions. This study aims to address this research gap by comparing the GLAD baseline forest cover maps for the years 2000 and 2010 with UK national forest datasets. The results showed substantial commission errors, which highlight potential problems when deviating from the GLAD datasets’ intended use. GLAD data appear to be less useful in regions with a high proportion of medium to low-density canopy cover. In such cases, its application in forest models should only be used in conjunction and cross-calibration with good quality reference data. Full article

(This article belongs to the Special Issue Uncertainty Modeling in Spatial Data Analysis)

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24 pages, 9318 KiB

Open AccessArticle

Accounting for Local Geological Variability in Sequential Simulations—Concept and Application

by Adrian Linsel, Sebastian Wiesler, Joshua Haas, Kristian Bär and Matthias Hinderer

ISPRS Int. J. Geo-Inf. 2020, 9(6), 409; https://doi.org/10.3390/ijgi9060409 - 26 Jun 2020

Cited by 5 | Viewed by 2954

Abstract

Heterogeneity-preserving property models of subsurface regions are commonly constructed by means of sequential simulations. Sequential Gaussian simulation (SGS) and direct sequential simulation (DSS) draw values from a local probability density function that is described by the simple kriging estimate and the local simple kriging variance at unsampled locations. The local simple kriging variance, however, does not necessarily reflect the geological variability being present at subsets of the target domain. In order to address that issue, we propose a new workflow that implements two modified versions of the popular SGS and DSS algorithms. Both modifications, namely, LVM-DSS and LVM-SGS, aim at simulating values by means of introducing a local variance model (LVM). The LVM is a measurement-constrained and geology-driven global representation of the locally observable variance of a property. The proposed modified algorithms construct the local probability density function with the LVM instead of using the simple kriging variance, while still using the simple kriging estimate as the best linear unbiased estimator. In an outcrop analog study, we can demonstrate that the local simple kriging variance in sequential simulations tends to underestimate the locally observed geological variability in the target domain and certainly does not account for the spatial distribution of the geological heterogeneity. The proposed simulation algorithms reproduce the global histogram, the global heterogeneity, and the considered variogram model in the range of ergodic fluctuations. LVM-SGS outperforms the other algorithms regarding the reproduction of the variogram model. While DSS and SGS generate a randomly distributed heterogeneity, the modified algorithms reproduce a geologically reasonable spatial distribution of heterogeneity instead. The new workflow allows for the integration of continuous geological trends into sequential simulations rather than using class-based approaches such as the indicator simulation technique. Full article

(This article belongs to the Special Issue Uncertainty Modeling in Spatial Data Analysis)

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11 pages, 3434 KiB

Open AccessArticle

Optimal Lowest Astronomical Tide Estimation Using Maximum Likelihood Estimator with Multiple Ocean Models Hybridization

by Mohammed El-Diasty

ISPRS Int. J. Geo-Inf. 2020, 9(5), 327; https://doi.org/10.3390/ijgi9050327 - 17 May 2020

Cited by 2 | Viewed by 3065

Abstract

Developing an accurate Lowest Astronomical Tide (LAT) in a continuous form is essential for many maritime applications as it can be employed to develop an accurate continuous vertical control datum for hydrographic surveys applications and to produce accurate dynamic electronic navigation charts for safe maritime navigation by mariners. The LAT can be developed in a continuous (surface) using an estimated LAT surface model from the hydrodynamic ocean model along with coastal discrete LAT point values derived from tide gauges data sets to provide the corrected LAT surface model. In this paper, an accurate LAT surface model was developed for the Red Sea case study using a Maximum Likelihood Estimator (MLE) with multiple hydrodynamic ocean models hybridization, namely, WebTide, FES2014, DTU10, and EOT11a models. It was found that the developed optimal hybrid LAT model using MLE with multiple hydrodynamic ocean models hybridization ranges from 0.1 m to 1.63 m, associated with about 2.4 cm of uncertainty at a 95% confidence level in the Red Sea case study area. To validate the accuracy of the developed model, the comparison was made between the optimal hybrid LAT model developed from multiple hydrodynamic ocean models hybridization using the MLE method with the individual LAT models estimated from individual WebTide, FES2014, DTU10, or EOT11a ocean models based on the associated uncertainties estimated at a 95% confidence level. It was found that the optimal hybrid LAT model accuracy is superior to the individual LAT models estimated from individual ocean models with an improvement of about 50% in average, based on the estimated uncertainties. The importance of developing optimal LAT surface model using the MLE method with multiple hydrodynamic ocean models hybridization in this paper with few centimeters level of uncertainty can lead to accurate continuous vertical datum estimation that is essential for many maritime applications. Full article

(This article belongs to the Special Issue Uncertainty Modeling in Spatial Data Analysis)

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18 pages, 4558 KiB

Open AccessFeature PaperArticle

Assessment of Enhanced Dempster-Shafer Theory for Uncertainty Modeling in a GIS-Based Seismic Vulnerability Assessment Model, Case Study—Tabriz City

by Mahmoud Reza Delavar and Mansoureh Sadrykia

ISPRS Int. J. Geo-Inf. 2020, 9(4), 195; https://doi.org/10.3390/ijgi9040195 - 26 Mar 2020

Cited by 15 | Viewed by 3143

Abstract

Earthquake is one of the natural disasters which threaten many lives every year. It is impossible to prevent earthquakes from occurring; however, it is possible to predict the building damage, human and property losses in advance to mitigate the adverse effects of the catastrophe. Seismic vulnerability assessment is a complex uncertain spatial decision making problem due to intrinsic uncertainties such as lack of complete data, vagueness in experts’ comments and uncertainties in the numerical data/relations. It is important to identify and model the incorporated uncertainties of seismic vulnerability assessment in order to obtain realistic predictions. Fuzzy sets theory can model the vagueness in weights of the selected criteria and relationships of the criteria with building damage. Dempster’s combination rule is useful for fusion of information on the vulnerability of the buildings which leads to decreased uncertainty of the results. However, when there is a conflict among information sources, classical Dempster rule of combination is not efficient. This paper analyses the uncertainty sources in a geospatial information system (GIS)-based seismic vulnerability assessment of buildings and then focuses on assessing the efficiency of Dempster rule of combination in the fusion of the information sources for the seismic vulnerability assessment. Tabriz, a historical and earthquake prone city in the north west of Iran was selected as the study area. The results verified that some inconsistencies among information sources exist which are important to be considered while proposing a method for the fusion of the information in order to obtain vulnerability assessments with less uncertainty. Based on the assessed building damage, the number of probable victims was estimated. The produced physical and social seismic vulnerability maps provide the required information for urban planners and administrators to reduce property and human losses through pre-earthquake mitigation and preparedness plans efficiently. Full article

(This article belongs to the Special Issue Uncertainty Modeling in Spatial Data Analysis)

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24 pages, 3342 KiB

Open AccessArticle

Remote Sensing Data Assimilation in Dynamic Crop Models Using Particle Swarm Optimization

by Matthias P. Wagner, Thomas Slawig, Alireza Taravat and Natascha Oppelt

ISPRS Int. J. Geo-Inf. 2020, 9(2), 105; https://doi.org/10.3390/ijgi9020105 - 10 Feb 2020

Cited by 21 | Viewed by 3425

Abstract

A growing world population, increasing prosperity in emerging countries, and shifts in energy and food demands necessitate a continuous increase in global agricultural production. Simultaneously, risks of extreme weather events and a slowing productivity growth in recent years has caused concerns about meeting the demands in the future. Crop monitoring and timely yield predictions are an important tool to mitigate risk and ensure food security. A common approach is to combine the temporal simulation of dynamic crop models with a geospatial component by assimilating remote sensing data. To ensure reliable assimilation, handling of uncertainties in both models and the assimilated input data is crucial. Here, we present a new approach for data assimilation using particle swarm optimization (PSO) in combination with statistical distance metrics that allow for flexible handling of model and input uncertainties. We explored the potential of the newly proposed method in a case study by assimilating canopy cover (CC) information, obtained from Sentinel-2 data, into the AquaCrop-OS model to improve winter wheat yield estimation on the pixel- and field-level and compared the performance with two other methods (simple updating and extended Kalman filter). Our results indicate that the performance of the new method is superior to simple updating and similar or better than the extended Kalman filter updating. Furthermore, it was particularly successful in reducing bias in yield estimation. Full article

(This article belongs to the Special Issue Uncertainty Modeling in Spatial Data Analysis)

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23 pages, 24203 KiB

Open AccessArticle

Uncertainty Visualization of Transport Variance in a Time-Varying Ensemble Vector Field

by Ke Ren, Dezhan Qu, Shaobin Xu, Xufeng Jiao, Liang Tai and Huijie Zhang

ISPRS Int. J. Geo-Inf. 2020, 9(1), 19; https://doi.org/10.3390/ijgi9010019 - 01 Jan 2020

Cited by 3 | Viewed by 2644

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 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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19 pages, 2656 KiB

Open AccessArticle

UTSM: A Trajectory Similarity Measure Considering Uncertainty Based on an Amended Ellipse Model

by Ning Guo, Shashi Shekhar, Wei Xiong, Luo Chen and Ning Jing

ISPRS Int. J. Geo-Inf. 2019, 8(11), 518; https://doi.org/10.3390/ijgi8110518 - 16 Nov 2019

Cited by 7 | Viewed by 3237

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 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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22 pages, 4931 KiB

Open AccessArticle

A Drift-of-Stay Pattern Extraction Method for Indoor Pedestrian Trajectories for the Error and Accuracy Assessment of Indoor Wi-Fi Positioning

by Dengbo Yu, Qingwu Hu and Shaohua Wang

ISPRS Int. J. Geo-Inf. 2019, 8(11), 468; https://doi.org/10.3390/ijgi8110468 - 23 Oct 2019

Cited by 1 | Viewed by 2200

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 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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15 pages, 3687 KiB

Open AccessArticle

A Multilevel Mapping Strategy to Calculate the Information Content of Remotely Sensed Imagery

by Shimin Fang, Xiaoguang Zhou and Jing Zhang

ISPRS Int. J. Geo-Inf. 2019, 8(10), 464; https://doi.org/10.3390/ijgi8100464 - 22 Oct 2019

Cited by 1 | Viewed by 2372

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 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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