Log in

Relevant bibliographies by topics / Spatial interpolation

Contents

Journal articles
Dissertations / Theses
Books
Book chapters
Conference papers
Reports

Academic literature on the topic 'Spatial interpolation'

Author: Grafiati

Published: 4 June 2021

Last updated: 11 September 2023

Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles

Select a source type:

Consult the lists of relevant articles, books, theses, conference reports, and other scholarly sources on the topic 'Spatial interpolation.'

Next to every source in the list of references, there is an 'Add to bibliography' button. Press on it, and we will generate automatically the bibliographic reference to the chosen work in the citation style you need: APA, MLA, Harvard, Chicago, Vancouver, etc.

You can also download the full text of the academic publication as pdf and read online its abstract whenever available in the metadata.

Journal articles on the topic "Spatial interpolation"

1

Earshia V., Diana, and Sumathi M. "Interpolation of Low-Resolution Images for Improved Accuracy Using an ANN Quadratic Interpolator." International Journal on Recent and Innovation Trends in Computing and Communication 11, no. 4s (April 3, 2023): 135–40. http://dx.doi.org/10.17762/ijritcc.v11i4s.6319.

Full text

Abstract:

The era of digital imaging has transitioned into a new one. Conversion to real-time, high-resolution images is considered vital. Interpolation is employed in order to increase the number of pixels per image, thereby enhancing spatial resolution. Interpolation's real advantage is that it can be deployed on user end devices. Despite raising the number of pixels per inch to enhances the spatial resolution, it may not improve the image's clarity, hence diminishing its quality. This strategy is designed to increase image quality by enhancing image sharpness and spatial resolution simultaneously. Proposed is an Artificial Neural Network (ANN) Quadratic Interpolator for interpolating 3-D images. This method applies Lagrange interpolating polynomial and Lagrange interpolating basis function to the parameter space using a deep neural network. The degree of the polynomial is determined by the frequency of gradient orientation events within the region of interest. By manipulating interpolation coefficients, images can be upscaled and enhanced. By mapping between low- and high-resolution images, the ANN quadratic interpolator optimizes the loss function. ANN Quadratic interpolator does a good work of reducing the amount of image artefacts that occur during the process of interpolation. The weights of the proposed ANN Quadratic interpolator are seeded by transfer learning, and the layers are trained, validated, and evaluated using a standard dataset. The proposed method outperforms a variety of cutting-edge picture interpolation algorithms..

APA, Harvard, Vancouver, ISO, and other styles

2

Etherington, Thomas R. "Discrete natural neighbour interpolation with uncertainty using cross-validation error-distance fields." PeerJ Computer Science 6 (July 13, 2020): e282. http://dx.doi.org/10.7717/peerj-cs.282.

Full text

Abstract:

Interpolation techniques provide a method to convert point data of a geographic phenomenon into a continuous field estimate of that phenomenon, and have become a fundamental geocomputational technique of spatial and geographical analysts. Natural neighbour interpolation is one method of interpolation that has several useful properties: it is an exact interpolator, it creates a smooth surface free of any discontinuities, it is a local method, is spatially adaptive, requires no statistical assumptions, can be applied to small datasets, and is parameter free. However, as with any interpolation method, there will be uncertainty in how well the interpolated field values reflect actual phenomenon values. Using a method based on natural neighbour distance based rates of error calculated for data points via cross-validation, a cross-validation error-distance field can be produced to associate uncertainty with the interpolation. Virtual geography experiments demonstrate that given an appropriate number of data points and spatial-autocorrelation of the phenomenon being interpolated, the natural neighbour interpolation and cross-validation error-distance fields provide reliable estimates of value and error within the convex hull of the data points. While this method does not replace the need for analysts to use sound judgement in their interpolations, for those researchers for whom natural neighbour interpolation is the best interpolation option the method presented provides a way to assess the uncertainty associated with natural neighbour interpolations.

APA, Harvard, Vancouver, ISO, and other styles

3

Stein, A. "Spatial Interpolation." Biometrics 50, no. 2 (June 1994): 592. http://dx.doi.org/10.2307/2533421.

Full text

APA, Harvard, Vancouver, ISO, and other styles

4

Caloiero, Tommaso, Gaetano Pellicone, Giuseppe Modica, and Ilaria Guagliardi. "Comparative Analysis of Different Spatial Interpolation Methods Applied to Monthly Rainfall as Support for Landscape Management." Applied Sciences 11, no. 20 (October 14, 2021): 9566. http://dx.doi.org/10.3390/app11209566.

Full text

Abstract:

Landscape management requires spatially interpolated data, whose outcomes are strictly related to models and geostatistical parameters adopted. This paper aimed to implement and compare different spatial interpolation algorithms, both geostatistical and deterministic, of rainfall data in New Zealand. The spatial interpolation techniques used to produce finer-scale monthly rainfall maps were inverse distance weighting (IDW), ordinary kriging (OK), kriging with external drift (KED), and ordinary cokriging (COK). Their performance was assessed by the cross-validation and visual examination of the produced maps. The results of the cross-validation clearly evidenced the usefulness of kriging in the spatial interpolation of rainfall data, with geostatistical methods outperforming IDW. Results from the application of different algorithms provided some insights in terms of strengths and weaknesses and the applicability of the deterministic and geostatistical methods to monthly rainfall. Based on the RMSE values, the KED showed the highest values only in April, whereas COK was the most accurate interpolator for the other 11 months. By contrast, considering the MAE, the KED showed the highest values in April, May, June and July, while the highest values have been detected for the COK in the other months. According to these results, COK has been identified as the best method for interpolating rainfall distribution in New Zealand for almost all months. Moreover, the cross-validation highlights how the COK was the interpolator with the best least bias and scatter in the cross-validation test, with the smallest errors.

APA, Harvard, Vancouver, ISO, and other styles

5

Al-husban, Yusra. "Comparison of Spatial Interpolation Methods for Estimating the Annual Rainfall in the Wadi Al-Mujib Basin in Jordan." Jordan Journal of Social Sciences 15, no. 2 (September 29, 2022): 198–208. http://dx.doi.org/10.35516/jjss.v15i2.490.

Full text

Abstract:

Accurate rainfall data are essential for environmental applications in the actual assessment of the geographical distribution of rainfall. Interpolation methods are usually applied to monitor the spatial distribution of the rainfall data. There are many spatial interpolation methods, but none of them can achieve in all cases the best results. In this study, three different interpolation methods were investigated with regard to their suitability for producing a spatial rainfall distribution. Rainfall data from 14 meteorological stations were spatially interpolated using three common interpolation techniques: inverse distance weighting (IDW), ordinary kriging (OK), and kernel smoothing (KS) were compared and assessed against station rainfall data and modeled rainfall. Cross-validation was applied to evaluate the accuracy of interpolation methods in terms of the root-mean-square error (RMSE). The best results were obtained by the lowest RMSE for interpolating the precipitation (RMSE) = 100.86542, while the inverse distance weighting (IDW) performed the worst, and are least efficient with the largest RMSE=103.43; in addition, the kernel smoothing with the least minimum (-) and maximum (+) error is -92.38 mm and 313.33 mm.

APA, Harvard, Vancouver, ISO, and other styles

6

DeGaetano, Arthur T., Brian N. Belcher, and William Noon. "Temporal and Spatial Interpolation of the Standardized Precipitation Index for Computational Efficiency in the Dynamic Drought Index Tool." Journal of Applied Meteorology and Climatology 54, no. 4 (April 2015): 795–810. http://dx.doi.org/10.1175/jamc-d-14-0088.1.

Full text

Abstract:

AbstractThe feasibility of interpolating gamma-distribution parameters between different precipitation accumulation intervals (durations) is statistically evaluated. The interpolation of these parameters for a specific accumulation interval, but ending on different dates, is similarly assessed. Such interpolation increases the computational efficiency of drought-monitoring tools that require calculation of the standardized precipitation index (SPI) for any user-specified accumulation period on any given day. Spatial interpolation of the distribution parameters is also assessed. Given a 60-yr period of record, few statistically significant differences were found between gamma-distribution percentiles interpolated between fixed base durations and those computed directly. Shorter interpolation intervals (generally 30 days) were required for the shortest (e.g., 30 days) durations, whereas interpolation over periods of as long as 180 days could be used for the longest (between 360 and 720 days) durations. Interpolating the distribution parameters to different ending dates on the basis of those computed for the end of each month was also appropriate. The spatial interpolation of gamma-distribution parameters, although viable in practice for monitoring large-scale drought conditions, was associated with larger SPI differences than was the spatial interpolation of the SPI index itself or the interpolation of historical precipitation and the subsequent calculation of gamma-distribution parameters on the basis of these values.

APA, Harvard, Vancouver, ISO, and other styles

7

Van der Steen, A., B. Heeg, F. De Charro, and BA Van Hout. "PMC10 SPATIAL INTERPOLATION." Value in Health 10, no. 6 (November 2007): A453. http://dx.doi.org/10.1016/s1098-3015(10)65564-7.

Full text

APA, Harvard, Vancouver, ISO, and other styles

8

Wickramathilaka, Nevil, Uznir Ujang, Suhaibah Azri, and Tan Liat Choon. "Calculation of Road Traffic Noise, Development of Data, and Spatial Interpolations for Traffic Noise Visualization in Three-dimensional Space." Geomatics and Environmental Engineering 17, no. 5 (August 28, 2023): 61–85. http://dx.doi.org/10.7494/geom.2023.17.5.61.

Full text

Abstract:

Road traffic noise visualization is vital in three-dimensional (3D) space. Designing noise observation points (NOPs) and the developments of spatial interpolations are key elements for the visualization of traffic noise in 3D. Moreover, calculating road traffic noise levels by means of a standard noise model is vital. This study elaborates on the developments of data and spatial interpolations in 3D noise visualization. In 3D spatial interpolation, the value is interpolated in both horizontal and vertical directions. Eliminating flat triangles is vital in the vertical direction. Inverse distance weighted (IDW), kriging, and triangular irregular network (TIN) are widely used to interpolate noise levels. Because these interpolations directly support the interpolation of three parameters, the developments of spatial interpolations should be applied to interpolate noise levels in 3D. The TIN noise contours are primed to visualize traffic noise levels while IDW and kriging provide irregular contours. Further, this study has identified that the TIN noise contours fit exactly with NOPs in 3D. Moreover, advanced kriging interpolation such as empirical Bayesian kriging (EBK) also provides irregular shape contours and this study develops a comparison for such contours. The 3D kriging in EBK provides a significant approach to interpolate noise in 3D. The 3D kriging voxels show a higher accurate visualization than TIN noise contours.

APA, Harvard, Vancouver, ISO, and other styles

9

Flannigan, M. D., and B. M. Wotton. "A study of interpolation methods for forest fire danger rating in Canada." Canadian Journal of Forest Research 19, no. 8 (August 1, 1989): 1059–66. http://dx.doi.org/10.1139/x89-161.

Full text

Abstract:

Canadian fire control agencies use either simple interpolation methods or none at all in estimating fire danger between weather stations. We compare several methods of interpolation and use the fire weather index in the North Central Region of Ontario as a case study. Our work shows that the second order least square polynomial, the smoothed cubic spline, and the weighted interpolations had the lowest residual sum of squares in our verification scheme. These methods fit the observed data at both high and low fire weather index values. The highly variable nature of the spatial distribution of summer precipitation amount is the biggest problem in interpolating between stations. This factor leads to highly variable fire weather index fields that are the most difficult to interpolate. The use of radar and (or) satellite data could help resolve precipitation patterns with greater precision. These interpolation methods could easily be implemented by fire control agencies to gain a better understanding of fire danger in the region.

APA, Harvard, Vancouver, ISO, and other styles

10

Etzel, K. R., and J. M. McCarthy. "Interpolation of Spatial Displacements Using the Clifford Algebra of E4." Journal of Mechanical Design 121, no. 1 (March 1, 1999): 39–44. http://dx.doi.org/10.1115/1.2829427.

Full text

Abstract:

In this paper we show that the Clifford Algebra of four dimensional Euclidean space yields a set of hypercomplex numbers called “double quaternions.” Interpolation formulas developed to generate Bezier-style quaternion curves are shown to be applicable to double quaternions by simply interpolating the components separately. The resulting double quaternion curves are independent of the coordinate frame in which the key frames are specified. Double quaternions represent rotations in E4 which we use to approximate spatial displacements. The result is a spatial motion interpolation methodology that is coordinate frame invariant to a desired degree of accuracy within a bounded region of three dimensional space. Examples demonstrate the application of this theory to computing distances between spatial displacement, determining the mid-point between two displacements, and generating the spatial motion interpolating a set of key frames.

APA, Harvard, Vancouver, ISO, and other styles

More sources

Dissertations / Theses on the topic "Spatial interpolation"

1

Martin, Peter. "Spatial interpolation in other dimensions /." Connect to this title online, 2004. http://hdl.handle.net/1957/4063.

Full text

APA, Harvard, Vancouver, ISO, and other styles

2

Gholmi, Allan. "Evaluating spatial mapping using interpolation techniques." Thesis, Linköpings universitet, Institutionen för datavetenskap, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-139704.

Full text

Abstract:

In this thesis, the inverse distance weighting, different kriging methods, ordinary least squares and two variants of the geographically weighted regression was used to evaluate the spatial mapping abilities on an observed dataset and a simulated dataset. The two datasets contain the same bioclimatic variable, near-surface air temperature, uniformly distributed over the whole world. The observed dataset is the observed temperature of a global atmospheric reanalysis produced by ECMWF and the other being simulated temperature produced by SMHI’s climate model EC-earth 3.1. The data, initially containing space-time information during the time period 1993-2010 displayed no significant temporal variation when using a spatio-temporal variogram. However, each year displayed its own variation so each year was split where the different methods were used on the observed dataset to estimate a surface for each year that was then used to make comparisons to the simulated data. CLARA clustering was done on the observed geographical dataset in the hope to force the inverse distance weighting and the kriging methods to estimate a locally varying mean. However, the variograms produced displayed an irregular trend that would lead to inaccurate kriging weights. Geometric anisotropy variogram analysis was accounted for that displayed moderate anisotropy. Results show that the geographically weighted regression family outperformed the rest of the used methods in terms of root mean squared error, mean absolute error and bias. It was able to create a surface that had a high resemblance to the observed data.

APA, Harvard, Vancouver, ISO, and other styles

3

Schmidt, Alexandra Mello. "Bayesian spatial interpolation of environmental monitoring stations." Thesis, University of Sheffield, 2001. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.370075.

Full text

APA, Harvard, Vancouver, ISO, and other styles

4

Gorsich, David John 1968. "Nonparametric modeling of dependencies for spatial interpolation." Thesis, Massachusetts Institute of Technology, 2000. http://hdl.handle.net/1721.1/9029.

Full text

Abstract:

Thesis (Ph.D.)--Massachusetts Institute of Technology, Dept. of Mathematics, 2000.
Includes bibliographical references (p. 140-148).
Crucial in spatial interpolation of stochastic processes is the determination of the underlying dependency of the data. The dependency can be represented by an underlying covariogram, variogram, or generalized covariogram. Estimating this function in a nonparametric way is the theme of this thesis. If the function can be found accurately, then kriging is the optimal linear interpolation technique. A nev,· technique for variogram model selection using the derivative of the empirical variogram and non-negative least squares is discussed. The eigenstructure of the spatial design matrix, the key matrix in Matheron's variogram estimator is determined. Then a nonparametric estimator of the variogram and covariogram of a spatial stochastic process is found. The optimal node selection is determined as well as conditions when the spectral coefficients can be found without a non-linear algorithm. A method of extending isotropic positive definite functions in ]Rd is determined in order to avoid a Gibbs effect on the Fourier-Bessel expansion. Finally, a nonparametric estimator of the generalized covariance is discussed.
by David John Gorsich.
Ph.D.

APA, Harvard, Vancouver, ISO, and other styles

5

Cui, Haiyan. "Robustness and Bayesian analysis of spatial interpolation." Diss., The University of Arizona, 1995. http://hdl.handle.net/10150/187077.

Full text

Abstract:

Kriging is a well known spatial interpolation technique widely used in earth science and environment sciences, the variogram plays a central role in the kriging predictor. In this dissertation, we will mainly study two problems which are closely related to the kriging predictor. The first one is how the variogram affects the kriging predictor and how this effect is qualified. The second one is how to approach kriging with an uncertain variogram, which includes both the functional form and the parameters in the variogram. For the first problem, some investigation of robustness of kriging predictor have been done by some authors. And for the second one, two frameworks have been used to approach the kriging with uncertain variogram in recent years. For the formal approach, the Bayesian framework is used to achieve the goal, and for the latter one, the fuzzy set theory is used, which mainly means that the kriging with an uncertain variogram is represented by the calculated membership function for each kriged value. The object of this dissertation is to extend the robustness results of kriging, to generalize the robustness concept to the cross-validation method, and to study the robustness of the cross-validation. We define the influence function of kriging and cross-validation technique and derive their influence functions in terms of perturbation of variogram and sample configuration. We will derive some different Bayesian kriging models under different assumptions and study their properties. We will also modify the fuzzy kriging model. Moreover we discuss the relationship between Bayesian kriging and fuzzy kriging and relate the fuzzy kriging to the robustness of kriging. Finally, in this work, we will show the power of Bayesian kriging and display its advantage as an interpolation technique for the analysis of spatial data. This is done through the presentation of a case study.

APA, Harvard, Vancouver, ISO, and other styles

6

Davies, Helen Catherine. "Bovine TB in badgers : a spatial analysis." Thesis, University of Bristol, 2003. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.289778.

Full text

APA, Harvard, Vancouver, ISO, and other styles

7

Atalay-Satoglu, Fatma Betul. "Spatial decompositions for geometric interpolation and efficient rendering." College Park, Md. : University of Maryland, 2004. http://hdl.handle.net/1903/1812.

Full text

Abstract:

Thesis (Ph. D.) -- University of Maryland, College Park, 2004.
Thesis research directed by: Computer Science. Title from t.p. of PDF. Includes bibliographical references. Published by UMI Dissertation Services, Ann Arbor, Mich. Also available in paper.

APA, Harvard, Vancouver, ISO, and other styles

8

Höglund, Melker. "Machine Learning Methods for Spatial Interpolation of Wind." Thesis, KTH, Skolan för teknikvetenskap (SCI), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-275743.

Full text

Abstract:

In this study, two popular machine learning approaches and a number of common simple spatial interpolation techniques are applied to spatial estimation of wind field observations in Sweden. Specifically, neural network and random forest models using geographical coordinates as input variables are considered. Furthermore, the addition of elevation as a secondary input is studied. The accuracy of the methods is assessed using a leave-one-out cross-validation scheme. Visual examination of the resulting interpolation fields and interpolation errors is used as an additional point of comparison. The results show that random forests with elevation included as a secondary input produces the smallest errors of all methods tested. It is thus concluded that it is possible to achieve greater accuracy using machine learning based models than simple traditional interpolation methods.
Denna studie jämför två populära maskininlärningsmetoder samt ett antal vanliga enklare metoder för interpolation av vindfältsobservationer från Sverige. Specifikt betraktas neurala nätverk och random forests, med huvudsakligen geografiska koordinater som indata. Vidare studeras även dessa modeller med höjd över havet av observationerna som ytterligare indata. Noggrannheten av metoderna undersöks med hjälp av leave-one-out-korsvalidering. Interpolationsresultaten samt interpolationsfelen studeras även visuellt som ytterligare jämförelsepunkt. Resultaten visar att random forests med höjddata inkluderad producerar de minsta felen av alla testade metoder. Från detta dras slutsatsen att det är möjligt att uppnå bättre noggrannhet med interpolationsmetoder baserade på maskininlärning jämfört med traditionella metoder.

APA, Harvard, Vancouver, ISO, and other styles

9

McNeill, Lindsay. "Topics in interpolation and smoothing of spatial data." Doctoral thesis, University of Cape Town, 1994. http://hdl.handle.net/11427/15969.

Full text

Abstract:

Bibliography: p. 176-187.
This thesis addresses a number of special topics in spatial interpolation and smoothing. The motivation for the thesis comes from two projects, one being to extend the availability of a daily rainfall model for southern Africa to sites at which little or no rainfall data is available, using data from nearby sites, and the other arising from a need to improve the species abundance estimates used to produce maps for the Southern African Bird Atlas Project in areas where the original presence/absence data is sparse. Although problems of spatial interpolation and smoothing have been the subject of much research in recent years, leading to the development of the specialised discipline of geostatistics, these two problems have features which render the available methodology inappropriate in certain respects. The semi-variogram plays a central role in geostatistical work. In both of the applications considered here, the raw semi-variogram is 'contaminated' by error, but the error variance varies widely between data points, so that the spatial autocorrelation structure of the underlying error-free variable is blurred. An adjusted semi-variogram, which removes the effect of the measurement error, is defined and incorporated into the kriging equations. A number of measures have been proposed for kriging in the presence of trend, ranging from explicit modelling of a deterministic trend function to 'moving window' kriging, which assumes local stationarity as an approximation. The former approach is often inappropriate over large non-homogenous regions, while the latter approach tends to underestimate the kriging variance. As an alternative strategy it is proposed here that the trend function be considered as another random variable, with a long-range spatial autocorrelation. This approach is simple to implement, and can also be used as a basis for filtering the data to separate trend from local or high-frequency variation. The daily rainfall model is based on a Fourier series representation giving rise to amplitude and phase parameters; the latter are circular in nature, and not amenable to analysis by standard techniques. This thesis describes a method of interpolation and smoothing, analogous to kriging, which is appropriate for unit vector data available at a number of spatial locations. The cumulated values of species counts in the SABAP are essentially binomially distributed and thus again specialised techniques are required for interpolation. New geostatistical methods which cater for both binomial and Poisson data are presented. Another problem arises from the need to improve interpolated values of the rainfall model parameters by incorporating information on altitude. Although a number of approaches are possible, for example, using co-kriging or kriging with external drift, difficulties are caused by the complexity of the relationship between the rainfall at a point and the surrounding topography. This problem is overcome by the use of orthogonal functions of altitude to model the patterns of topography.

APA, Harvard, Vancouver, ISO, and other styles

10

Khosravan, Najafabadi Shohreh. "Optimal vector interpolation of asynoptic spatial survey of vector quantities for interpolating ADCP water velocity measurements." Thesis, University of Ottawa (Canada), 2006. http://hdl.handle.net/10393/27381.

Full text

Abstract:

In fields of study such as geophysics and hydraulics, many random variables are vector quantities, not scalars. Vector quantities require statistical techniques that are independent of choice of coordinate system. In this research a new optimal vector interpolation method, suitable for interpolation of asynoptically measured spatial vector fields, was developed and tested. The new method was compared to scalar interpolation by kriging. The test data were spatial Acoustic Doppler Current Profiler (ADCP) surveys of depth average fluvial water velocity in reaches upstream and downstream of a bridge. The interpolation procedures were evaluated by interpolating the fields with various amounts of data removal, and comparing to the actual measured field using a vector correlation coefficient previously developed by Crosby et al. (1993). The new optimal vector interpolation method was superior to kriging when all data were utilized (upstream reach) and for data removal rates of up to 30% (downstream reach).

APA, Harvard, Vancouver, ISO, and other styles

More sources

Books on the topic "Spatial interpolation"

1

Stein, Michael L. Interpolation of Spatial Data. New York, NY: Springer New York, 1999. http://dx.doi.org/10.1007/978-1-4612-1494-6.

Full text

APA, Harvard, Vancouver, ISO, and other styles

2

Dobesch, Hartwig, Pierre Dumolard, and Izabela Dyras, eds. Spatial Interpolation for Climate Data. London, UK: ISTE, 2007. http://dx.doi.org/10.1002/9780470612262.

Full text

APA, Harvard, Vancouver, ISO, and other styles

3

Janis, Michael J. Multivariate spatial interpolation of monthly precipitation. Elmer, N.J: C.W. Thornthwaite Associates, Laboratory of Climatology, 1995.

Find full text

APA, Harvard, Vancouver, ISO, and other styles

4

Stein, Michael Leonard. Interpolation of spatial data: Some theory for kriging. New York: Springer, 1999.

Find full text

APA, Harvard, Vancouver, ISO, and other styles

5

Robeson, Scott M. Spatial interpolation, network bias, and terrestrial air temperature variability. Elmer, N.J: C.W. Thornthwaite Associates, Laboratory of Climatology, 1993.

Find full text

APA, Harvard, Vancouver, ISO, and other styles

6

Allanson, Paul. Resolving the spatial limitations of parish agricultural census data by areal interpolation. Newcastle upon Tyne: Countryside Change Unit, Dept. of Agricultural Economics & Food Marketing, University of Newcastle upon Tyne, 1991.

Find full text

APA, Harvard, Vancouver, ISO, and other styles

7

Hartwig, Dobesch, Dumolard Pierre, and Dyras Izabela, eds. Spatial interpolation for climate data: The use of GIS in climatology and meterology. Newport Beach, CA: ISTE Ltd, 2007.

Find full text

APA, Harvard, Vancouver, ISO, and other styles

8

Stein, Michael L. Interpolation of Spatial Data. Springer, 2012.

Find full text

APA, Harvard, Vancouver, ISO, and other styles

9

Dyras, Isabela, Hartwig Dobesch, Pierre Dumolard, and Izabela Dyras. Spatial Interpolation for Climate Data. Wiley & Sons, Incorporated, John, 2010.

Find full text

APA, Harvard, Vancouver, ISO, and other styles

10

Stein, Michael L. Interpolation of Spatial Data: Some Theory for Kriging. Springer London, Limited, 2012.

Find full text

APA, Harvard, Vancouver, ISO, and other styles

More sources

Book chapters on the topic "Spatial interpolation"

1

Bajjali, William. "Spatial Interpolation." In Springer Textbooks in Earth Sciences, Geography and Environment, 219–34. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-61158-7_13.

Full text

APA, Harvard, Vancouver, ISO, and other styles

2

Bhattacharjee, Shrutilipi, Soumya Kanti Ghosh, and Jia Chen. "Spatial Interpolation." In Studies in Computational Intelligence, 19–41. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-8664-0_2.

Full text

APA, Harvard, Vancouver, ISO, and other styles

3

Shekhar, Shashi, and Hui Xiong. "Spatial Interpolation." In Encyclopedia of GIS, 1101. Boston, MA: Springer US, 2008. http://dx.doi.org/10.1007/978-0-387-35973-1_1277.

Full text

APA, Harvard, Vancouver, ISO, and other styles

4

Davis, Jerry D. "Spatial Interpolation." In Introduction to Environmental Data Science, 205–24. Boca Raton: Chapman and Hall/CRC, 2023. http://dx.doi.org/10.1201/9781003317821-11.

Full text

APA, Harvard, Vancouver, ISO, and other styles

5

Pebesma, Edzer, and Roger Bivand. "Spatial Interpolation." In Spatial Data Science, 165–80. Boca Raton: Chapman and Hall/CRC, 2023. http://dx.doi.org/10.1201/9780429459016-12.

Full text

APA, Harvard, Vancouver, ISO, and other styles

6

Wang, Fahui, and Lingbo Liu. "Spatial Smoothing and Spatial Interpolation." In Computational Methods and GIS Applications in Social Science, 65–92. 3rd ed. Boca Raton: CRC Press, 2023. http://dx.doi.org/10.1201/9781003292302-4.

Full text

APA, Harvard, Vancouver, ISO, and other styles

7

Liu, Lingbo, and Fahui Wang. "Spatial Smoothing and Spatial Interpolation." In Computational Methods and GIS Applications in Social Science - Lab Manual, 63–86. Boca Raton: CRC Press, 2023. http://dx.doi.org/10.1201/9781003304357-3.

Full text

APA, Harvard, Vancouver, ISO, and other styles

8

Bărbulescu, Alina. "Spatial Interpolation with Applications." In Studies on Time Series Applications in Environmental Sciences, 159–87. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-30436-6_7.

Full text

APA, Harvard, Vancouver, ISO, and other styles

9

Gao, Jay. "Geostatistics and Spatial Interpolation." In Fundamentals of Spatial Analysis and Modelling, 159–88. Boca Raton: CRC Press, 2021. http://dx.doi.org/10.1201/9781003220527-5.

Full text

APA, Harvard, Vancouver, ISO, and other styles

10

Li, Jin. "Mathematical spatial interpolation methods." In Spatial Predictive Modeling with R, 53–66. Boca Raton: Chapman and Hall/CRC, 2022. http://dx.doi.org/10.1201/9781003091776-4.

Full text

APA, Harvard, Vancouver, ISO, and other styles

Conference papers on the topic "Spatial interpolation"

1

Fazio, Vinicius Sousa, and Mauro Roisenberg. "Spatial interpolation." In the 28th Annual ACM Symposium. New York, New York, USA: ACM Press, 2013. http://dx.doi.org/10.1145/2480362.2480364.

Full text

APA, Harvard, Vancouver, ISO, and other styles

2

Li, Jia, Jiatian Li, Xiaoqing Zuo, and Ping Duan. "Natural neighbors interpolation method for correcting IDW." In International Symposium on Spatial Analysis, Spatial-temporal Data Modeling, and Data Mining, edited by Yaolin Liu and Xinming Tang. SPIE, 2009. http://dx.doi.org/10.1117/12.838426.

Full text

APA, Harvard, Vancouver, ISO, and other styles

3

COBOS, FERNANDO. "LOGARITHMIC INTERPOLATION SPACES." In Spectral Theory and Nonlinear Analysis with Applications to Spatial Ecology. WORLD SCIENTIFIC, 2005. http://dx.doi.org/10.1142/9789812701589_0002.

Full text

APA, Harvard, Vancouver, ISO, and other styles

4

Wen Wang, Zuqiang Xiong, Huamin Li, and Dongyin Li. "Comparison discrete smooth interpolation and traditional spatial interpolation." In 2011 International Conference on Remote Sensing, Environment and Transportation Engineering (RSETE). IEEE, 2011. http://dx.doi.org/10.1109/rsete.2011.5964246.

Full text

APA, Harvard, Vancouver, ISO, and other styles

5

Ohashi, Orlando, and Luis Torgo. "Spatial Interpolation Using Multiple Regression." In 2012 IEEE 12th International Conference on Data Mining (ICDM). IEEE, 2012. http://dx.doi.org/10.1109/icdm.2012.48.

Full text

APA, Harvard, Vancouver, ISO, and other styles

6

Duan, Ping, Jiatian Li, Xiaoqing Zuo, and Jia Li. "A new interpolation model of convex hull in Delaunay triangulation." In International Symposium on Spatial Analysis, Spatial-temporal Data Modeling, and Data Mining, edited by Yaolin Liu and Xinming Tang. SPIE, 2009. http://dx.doi.org/10.1117/12.838405.

Full text

APA, Harvard, Vancouver, ISO, and other styles

7

Liu, Fucheng, Xuezhao He, and Li Zhou. "Application of generalized regression neural network residual kriging for terrain surface interpolation." In International Symposium on Spatial Analysis, Spatial-temporal Data Modeling, and Data Mining, edited by Yaolin Liu and Xinming Tang. SPIE, 2009. http://dx.doi.org/10.1117/12.837425.

Full text

APA, Harvard, Vancouver, ISO, and other styles

8

Faerman, Evgeniy, Manuell Rogalla, Niklas StrauB, Adrian Kruger, Benedict Blumel, Max Berrendorf, Michael Fromm, and Matthias Schubert. "Spatial Interpolation with Message Passing Framework." In 2019 International Conference on Data Mining Workshops (ICDMW). IEEE, 2019. http://dx.doi.org/10.1109/icdmw.2019.00030.

Full text

APA, Harvard, Vancouver, ISO, and other styles

9

Polyak, M. D., and I. U. Gorchakov. "SPATIAL INTERPOLATION ALGORITHMS FOR BUILDING HEATMAPS." In PROCESSING, TRANSMISSION AND PROTECTION OF INFORMATION IN COMPUTER SYSTEMS. St. Petersburg State University of Aerospace Instrumentation, 2020. http://dx.doi.org/10.31799/978-5-8088-1452-3-2020-1-80-85.

Full text

APA, Harvard, Vancouver, ISO, and other styles

10

Gharavi, Hamid, and Shaoshuai Gao. "Spatial interpolation algorithm for error concealment." In ICASSP 2008 - 2008 IEEE International Conference on Acoustics, Speech and Signal Processing. IEEE, 2008. http://dx.doi.org/10.1109/icassp.2008.4517819.

Full text

APA, Harvard, Vancouver, ISO, and other styles

Reports on the topic "Spatial interpolation"

1

Niemann, Jeffrey D. Scaling Properties and Spatial Interpolation of Soil Moisture. Fort Belvoir, VA: Defense Technical Information Center, August 2004. http://dx.doi.org/10.21236/ada426497.

Full text

APA, Harvard, Vancouver, ISO, and other styles

2

Rutherford, Paula. Spatial and Temporal Interpolation Analysis Process of Shock Loading. Office of Scientific and Technical Information (OSTI), July 2022. http://dx.doi.org/10.2172/1876754.

Full text

APA, Harvard, Vancouver, ISO, and other styles

3

Jiang, Wenping, and Jin Li. The effects of spatial reference systems on the predictive accuracy of spatial interpolation methods. Geoscience Australia, 2014. http://dx.doi.org/10.11636/record.2014.001.

Full text

APA, Harvard, Vancouver, ISO, and other styles

4

Goff, John A. Spatial Variability and Robust Interpolation of Seafloor Sediment Properties Using the SEABED Data Bases. Fort Belvoir, VA: Defense Technical Information Center, January 2006. http://dx.doi.org/10.21236/ada522852.

Full text

APA, Harvard, Vancouver, ISO, and other styles

5

Jenkins, Chris. Spatial Variability and Robust Interpolation of Seafloor Sediment Properties Using the SEABED Data Bases. Fort Belvoir, VA: Defense Technical Information Center, January 2006. http://dx.doi.org/10.21236/ada522854.

Full text

APA, Harvard, Vancouver, ISO, and other styles

6

Jenkins, Chris. Spatial Variability and Robust Interpolation of Seafloor Sediment Properties Using the SEABED Data Bases. Fort Belvoir, VA: Defense Technical Information Center, September 2005. http://dx.doi.org/10.21236/ada572342.

Full text

APA, Harvard, Vancouver, ISO, and other styles

7

Goff, John A. Spatial Variability and Robust Interpolation of Seafloor Sediment Properties Using the SEABED Data Bases. Fort Belvoir, VA: Defense Technical Information Center, September 2005. http://dx.doi.org/10.21236/ada572625.

Full text

APA, Harvard, Vancouver, ISO, and other styles

8

Fuentes, Montserrat, and Adrian E. Raftery. Model Validation and Spatial Interpolation by Combining Observations with Outputs from Numerical Models via Bayesian Melding. Fort Belvoir, VA: Defense Technical Information Center, November 2001. http://dx.doi.org/10.21236/ada459748.

Full text

APA, Harvard, Vancouver, ISO, and other styles

9

Kingston, A. W., A. Mort, C. Deblonde, and O H Ardakani. Hydrogen sulfide (H2S) distribution in the Triassic Montney Formation of the Western Canadian Sedimentary Basin. Natural Resources Canada/CMSS/Information Management, 2021. http://dx.doi.org/10.4095/329266.

Full text

Abstract:

The Montney Formation is a highly productive hydrocarbon reservoir that is of great economic importance to Canada, however production is often dogged by the presence of hydrogen sulfide (H2S), a highly toxic and corrosive gas. Mapping H2S distribution across the Montney basin in the Western Canadian Sedimentary Basin (WCSB) is fundamental to understanding the processes responsible for its occurrence. We derive a Montney-specific dataset of well gas and water geochemistry from the publically available archives of the Alberta Energy Regulator (AER) and British Columbia Oil and Gas Commission (BCOGC) conducting quality assurance and control procedure before spatial interpolation. Empirical Bayesian Kriging is used to interpolate H2S across the whole Montney basin resulting in maps of H2S from hydrocarbon gas, condensates, and water; along with maps of sulfate and chloride ions in water. These interpolations illustrate the heterogeneous distribution of H2S across the basin with the highest concentrations in the Grande Prairie area along with several other isolated regions. Maps of H2S in gas, condensates, and water exhibit similar trends in H2S concentrations, which with future research may help elucidate the origin of H2S in the Montney.

APA, Harvard, Vancouver, ISO, and other styles

10

Downing, W. Logan, Howell Li, William T. Morgan, Cassandra McKee, and Darcy M. Bullock. Using Probe Data Analytics for Assessing Freeway Speed Reductions during Rain Events. Purdue University, 2021. http://dx.doi.org/10.5703/1288284317350.

Full text

Abstract:

Rain impacts roadways such as wet pavement, standing water, decreased visibility, and wind gusts and can lead to hazardous driving conditions. This study investigates the use of high fidelity Doppler data at 1 km spatial and 2-minute temporal resolution in combination with commercial probe speed data on freeways. Segment-based space-mean speeds were used and drops in speeds during rainfall events of 5.5 mm/hour or greater over a one-month period on a section of four to six-lane interstate were assessed. Speed reductions were evaluated as a time series over a 1-hour window with the rain data. Three interpolation methods for estimating rainfall rates were tested and seven metrics were developed for the analysis. The study found sharp drops in speed of more than 40 mph occurred at estimated rainfall rates of 30 mm/hour or greater, but the drops did not become more severe beyond this threshold. The average time of first detected rainfall to impacting speeds was 17 minutes. The bilinear method detected the greatest number of events during the 1-month period, with the most conservative rate of predicted rainfall. The range of rainfall intensities were estimated between 7.5 to 106 mm/hour for the 39 events. This range was much greater than the heavy rainfall categorization at 16 mm/hour in previous studies reported in the literature. The bilinear interpolation method for Doppler data is recommended because it detected the greatest number of events and had the longest rain duration and lowest estimated maximum rainfall out of three methods tested, suggesting the method balanced awareness of the weather conditions around the roadway with isolated, localized rain intensities.

APA, Harvard, Vancouver, ISO, and other styles

We offer discounts on all premium plans for authors whose works are included in thematic literature selections. Contact us to get a unique promo code!