Interpolation techniques for spatial data have been applied frequently in various fields of geosciences. Although most conventional interpolation methods assume that it is sufficient to use first- and second-order statistics to characterize random fields, researchers have now realized that these methods ...
Mostafa Emadiand Majid Baghernejad.2014.Comparison of spatial interpolation techniques for mapping soil pH and salinity in agricultural coastal areas, Northern Iran. Archives of Agronomy and Soil Science. 60(9): 1315-1327.Emadi Mostafa, Baghernejad Majid. Comparison of spatial interpolation techniques...
ST imaging framework (STIM) borrows techniques from computer vision to register ST data into a CCS28. For both Eggplant and STIM, the identification of shared landmark locations may be difficult across slices from tissues without canonical structures, such as tumors. In this work, we present a...
The results, however, can also be considered a useful warning of the instability of interpolation techniques with limited/sparse data, particularly in the context of commercial software.doi:10.4296/cwrj23S. NaoumDepartment of Civil EngineeringI.K. Tsanis...
A deterministic interpolation can either force the resulting surface to pass through the data values or not. An interpolation technique that predicts a value that is identical to the measured value at a sampled location is known as an exact interpolator. An inexact interpolator pr...
One major issue is the uneven spatial resolution of mobile phone data due to the spatial configuration of mobile network base stations and its spatial interpolation. To date, different interpolation techniques are applied to transform mobile phone data into other spatial divisions. However, these do ...
There are two main groupings of interpolation techniques: deterministic and geostatistical. Deterministic interpolation techniques create surfaces from measured points, based on either the extent of similarity (inverse distance weighted) or the degree of smoothing (radial basis functions). G...
volumes of multiple spatial data types, automation can make a significant contribution to data processing in terms of cost and time required for data management, retrieval, analysis, and display in addition to providing capabilities which are not feasible, or not possible, using manual techniques. ...
and fish and wildlife studies. In the field of data science, one of the most common problems is incomplete or missing values in data. You can use spatial interpolation techniques to interpolate values for the missing observations. Interpolation can play an important role in data engineering, and...
Data redundancy: When data elements are organized in a regularly spaced system, there is a data point at the location of every grid cell, regardless of whether the data element is needed or not. Although, several compression techniques are available, the advantages of gridded data are lost ...