Comparison of Spatial Interpolation Techniques Using Visualization and Quantitative Assessment

Abstract

Spatial interpolation has been widely and commonly used in many studies to create surface data based on a set of sampled points, such as soil properties, temperature, and precipitation. Currently, there are many commercial Geographic Information System (GIS) or statistics software ofering spatial interpolation functions, such as inverse distance weighted (IDW), kriging, spline, and others. To date, there is no "rule of thumb" on the most appropriate spatial interpolation techniques for certain situations, though general suggestions have been published. Many studies rely on quantitative assessment to determine the performance of spatial interpolation techniques. Most quantitative assessment methods provide a numeric index for the overall performance of an interpolated surface. Although it is objective and convenient, there are many facts or trends not captured by quantitative assessments. This study used 2D visualization and 3D visualization to identify trends not evident in quantitative assessment. This study also presented a special case, a closed system in which all interpolated surfaces should sum up to 100%, to demonstrate the interaction between interpolated surfaces that were created separately and independently.

A Scoping Review of Spatial Analysis Approaches Using Health Survey Data in Sub-Saharan Africa

“…Spatial interpolation or spatial prediction methods incorporate geographic information and values at a network of observed locations to estimate values at unobserved locations. In the traditional spatial analysis, the main spatial interpolation techniques include inverse distance weighting (IDW), Kriging, spline interpolation, and interpolating polynomials [171,172]. However, as the evidence shows, Bayesian spatial hierarchical modelling is becoming more effective than the conventional classical spatial analysis method, thanks to advanced computing power and Markov chain Monte Carlo (MCMC) methods [173].…”

“…This review also excluded published research work that used spatial analyses on sentinel surveillance data. For example, spatial autocorrelation and inverse distance-weighted interpolation were used in [171][172][173][174][175][176][177][178][179][180][181][182][183] when spatial statistics were used to analyze HIV data of pregnant women attending antennal clinics (not health surveys).…”

Spatializing the Coffee Yield Model SAFERNAC with Soil Fertility Data Across Tanzania

“…It has been used successfully in many areas such as geology, hydrology [22], environment [23], mining, climatology and meteorology [24]- [25], biology [26], forestry, agriculture [27]- [28], etc. Spatial interpolation uses a variety of methods, and in this case [29] noted that to-date there is no rule of thumb on the most appropriate interpolation technique for certain situations though general suggestions have been published. In this study inverse distance weighting (IDW), one of the most widely used interpolation technique [13] was selected.…”

A novel analysis of COVID 19 risk in India incorporating climatic and socioeconomic Factors

“…The re-gridding was performed using the inverse distance weighted average (IDWA) method ( Snell et al, 2000 ). The advantage of using IDW is that it simple, easy to comprehend and efficient in modelling the data which do not have outliers ( Wu and Hung, 2016 ). The chances of getting outliers is rare in the climate data which we have analyzed as a very short duration data has been used.…”