A comparison of spatial interpolation methods and a fuzzy areal evaluation scheme in environmental site characterization

Özdamar, Linet; Demirhan, Melek; Özpinar, A.

doi:10.1016/s0198-9715(99)00032-0

Cited by 13 publications

(6 citation statements)

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“…Many sampling modes are adopted in interpolation research, and they can be divided into two large categories according to the distribution of sampling points: uniform sampling and non-uniform sampling. Some scholars used less popular methods, such as profile sampling and asymptotic sampling; OÈzdamar et al applied herringbone-shaped, regular-grid, and layered random sampling modes in studying interpolation methods [21]. Demirhan et al used four modes to sample the area under study: herringbone-shaped, regular-grid, linear, and ring sampling modes [22].…”

Section: Data Preparation and Processingmentioning

confidence: 99%

Spatial Sampling Strategies for the Effect of Interpolation Accuracy

Zhang

Liu

et al. 2015

IJGI

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Spatial interpolation methods are widely used in various fields and have been studied by several scholars with one or a few specific sampling datasets that do not reflect the complexity of the spatial characteristics and lead to conclusions that cannot be widely applied. In this paper, three factors that affect the accuracy of interpolation have been considered, i.e., sampling density, sampling mode, and sampling location. We studied the inverse distance weighted (IDW), regular spline (RS), and ordinary kriging (OK) interpolation methods using 162 DEM datasets considering six sampling densities, nine terrain complexities, and three sampling modes. The experimental results show that, in selective sampling and combined sampling, the maximum absolute errors of interpolation methods rapidly increase and the estimated values are overestimated. In regular-grid sampling, the RS method has the highest interpolation accuracy, and IDW has the lowest interpolation accuracy. However, in both selective and combined sampling, the accuracy of the IDW method is significantly improved and the RS method performs worse. The OK method does not significantly change between the three sampling modes. The following conclusion can be obtained from the above analysis: the combined sampling mode is recommended for sampling, and more sampling points should be added in the ridges, OPEN ACCESS ISPRS Int. J. Geo-Inf. 2015, 4 2743 valleys, and other complex terrain. The IDW method should not be used in the regular-grid sampling mode, but it has good performance in the selective sampling mode and combined sampling mode. However, the RS method shows the opposite phenomenon. The sampling dataset should be analyzed before using the OK method, which can select suitable models based on the analysis results of the sampling dataset.

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Section: Data Preparation and Processingmentioning

confidence: 99%

Spatial Sampling Strategies for the Effect of Interpolation Accuracy

Zhang

Liu

et al. 2015

IJGI

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“…The five different categories of spatial interpolation methods identified by Ozdamar et al (1999) are as follows: point versus areal gradual versus abrupt interpolators global versus local approximate versus exact stochastic versus deterministic However these classical spatial interpolation methods use the crisp data and do not address circumstances such as luck or inappropriateness of sampling data or their inherent uncertainty and fuzziness. The next section discusses the fundamentals of fuzzy interpolation in dealing with crisp and uncertain data.…”

Section: Characteristics Of Geographical Data and Entitiesmentioning

confidence: 99%

Fuzzy Modeling with Spatial Information for Geographic Problems

Petry¹,

Robinson²,

Cobb³

2005

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ForewordThe capabilities of modern technology are rapidly increasing, spurred on to a large extent by the tremendous advances in communications and computing. Automated vehicles and global wireless connections are some examples of these advances. In order to take advantage of such enhanced capabilities, our need to model and manipulate our knowledge of the geophysical world, using compatible representations, is also rapidly increasing. In response to this one fundamental issue of great concern in modern geographical research is how to most effectively capture the physical world around us in systems like geographical information systems (GIS). Making this task even more challenging is the fact that uncertainty plays a pervasive role in the representation, analysis and use of geospatial information. The types of uncertainty that appear in geospatial information systems are not the just simple randomness of observation, as in weather data, but are manifested in many other forms including imprecision, incompleteness and granularization. Describing the uncertainty of the boundaries of deserts and mountains clearly require different tools than those provided by probability theory. The multiplicity of modalities of uncertainty appearing in GIS requires a variety of formalisms to model these uncertainties. In light of this it is natural that fuzzy set theory has become a topic of intensive interest in many areas of geographical research and applications This volume, Fuzzy Modeling with Spatial Information for Geographic Problems, provides many stimulating examples of advances in geographical research based on approaches using fuzzy sets and related technologies. It includes chapters on diverse research topics such as spatial directions, geographical interpolation, landscape features and spatial decision systems among others. The editors, Maria Cobb, Vince Robinson and Fred Petry provide a snapshot of current topics of research and should stimulate work in this area and hopefully encourage more cross-disciplinary efforts such as demonstrated by these chapters. The papers published in this volume should be of considerable interest to a broad spectrum of researchers in the fuzzy set and GIS areas as well as those engineers who make use of geospatial information in their applications and systems. PrefaceThis volume, the companion to Flexible Querying and Reasoning in Spatio-Temporal Databases edited by Rita De Caluwe, Guy De Tre, and Gloria Bordogna, focuses on advances in research on approaches to incorporating explicit handling of uncertainty, especially by fuzzy sets, to address geographic problems. Over the past several years interest in the use of fuzzy approaches has grown across a broad spectrum of fields that use spatial information to address geographic problems.The reasoning about geographic information representing regions, relations, and/or fields is fundamental to any progress in the application of fuzzy sets to modeling geographical problems. There are several papers in this volume that advance our understandin...

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“…Exact interpolation methods use the known data points to derive a surface that passes exactly through all the data points [26], while approximate interpolation methods construct interpolated surfaces that approximate but do not pass through known points [27]. In this sense, exact interpolation methods provide the exact values of the function at the known data points.…”

Section: Introductionmentioning

confidence: 99%

Statistical Assessment of Some Interpolation Methods for Building Grid Format Digital Bathymetric Models

et al. 2023

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As far as the knowledge of the seabed is concerned, both for safe navigation and for scientific research, 3D models, particularly digital bathymetric models (DBMs), are nowadays of fundamental importance. This work aimed to evaluate the quality of DBMs according to the interpolation methods applied to obtain grid format 3D surfaces from scattered sample points. Other complementary factors affecting DBM vertical accuracy, such as seabed morphological complexity and surveyed points sampling density, were also analyzed by using a factorial ANOVA experimental design. The experiments were performed on a multibeam dataset provided by the Italian Navy Hydrographic Institute (IIM) with an original resolution of 1 m × 1 m grid spacing, covering a surface of 0.24 km2. Six different sectors comprising different seabed morphologies were investigated. Eight sampling densities were randomly extracted from every sector, each with four repetitions. Finally, four different interpolation methods were tested, including: radial basis multiquadric function (RBMF), ordinary kriging (OK), universal kriging (UK) and Gaussian Markov random fields (GMRF). The results demonstrated that both RBMF and OK produced very accurate DBM in areas characterized by low levels of seabed ruggedness at sampling densities of only 0.0128 points/m2 (equivalent grid spacing of 8.84 m). In contrast, a higher density of 0.1024 points/m2 (3.13 m grid spacing) was required to produce accurate DBM in areas with more complex seabed topography. On the other hand, UK and GMRF were strongly influenced by morphology and sampling density, yielding higher vertical random errors and more prone to slightly overestimate seabed depths. In addition, sampling density and morphology were the factors that most influenced the vertical accuracy of the interpolated DBM. In this sense, the highly statistically significant influence of the interaction between sampling density and morphology on the vertical accuracy of the interpolated DBM confirms the need to perform a preliminary analysis of seabed morphological complexity in order to increase, if necessary, the number of surveyed points in cases of complex morphologies.

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A comparison of spatial interpolation methods and a fuzzy areal evaluation scheme in environmental site characterization

Cited by 13 publications

References 6 publications

Spatial Sampling Strategies for the Effect of Interpolation Accuracy

Spatial Sampling Strategies for the Effect of Interpolation Accuracy

Fuzzy Modeling with Spatial Information for Geographic Problems

Statistical Assessment of Some Interpolation Methods for Building Grid Format Digital Bathymetric Models

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