Both are the same techniques, this tool is available in ARC GIS software

In IDW only known z values and distance weights are used to determine unknown areas. ... Kriging is most appropriate when you know there is a spatially correlated distance or directional bias in the data.

https://pro.arcgis.com/en/pro-app/tool-reference/3d-analyst/comparing-interpolation-methods.htm

Dear Subhash Chavare

IDW is one of the deterministic methods while Kriging is a geostatistics method. Both methods rely on the similarity of nearby sample points to create the surface. Deterministic techniques use mathematical functions for interpolation. Geostatistics rely on both statistical and mathematical methods, which can be used to create surfaces and assess the uncertainty of the predictions.

for more details, you can read attached the (Using_ArcGIS_geostatistical_analyst)

IDW is simpler than kriging because it calculates that unknown values based on the average, but kriging is advanced used when the spatial correlation is found and used in many fields with high accuracy than IDW

kriging is a stochastic interopolation based on the variogram which gives the spatial structure of the studied variable, kriging, in addition to the interpolated variable, calculates the precision of the interpolation by estimating the standard deviation of the interpolated variable. So with kriging you have two maps, one for the studied variable and another for the standard deviation. On the other hand IDW is a simple analytical interpolation technique based on the distance between the point to be estimated and the measured points.

Essentially all the responses are wrong or at least incomplete. The explanation in the ArcGIS documentation is wrong.

See GEOSTATISTICS by P. Delfiner and J.P. Chiles (2nd edition) John Wiley and sons

Kriging assumes that the distance or direction between sample points reflects a spatial correlation that can be used to explain variation in the surface. Kriging is similar to IDW in that it weights the surrounding measured values to derive a prediction for an unmeasured location.

In IDW method, it is assumed substantially that the rate of correlations and similarities between neighbors is proportional to the distance between them that can be defined as a distance reverse function of every point from neighboring points. Ordinary Kriging is one of the most basic of Kriging methods. It provides an estimate at an unobserved location of variable z, based on the weighted average of adjacent observed sites within a given area.

Kriging is one of the most complex interpolators. It applies sophisticated statistical methods that consider the unique characteristics of the dataset. IDW takes the concept of spatial autocorrelation literally. It assumes that the nearer a sample point is to the cell whose value is to be estimated, the more closely the cell’s value will resemble the sample point’s value

hi dear

please have a look to the following references:

Article A comparison of spatial interpolation methods to estimate co...

Article Comparison of deterministic and stochastic methods to predic...

https://www.aspexit.com/spatial-data-interpolation-tin-idw-kriging-block-kriging-co-kriging-what-are-the-differences/

https://www.springerprofessional.de/en/comparison-of-geostatistical-and-deterministic-interpolation-to-/17632462

There are still problems or erroneous statements in most of the "answers" or comments.

There is no mention of Tobler's Law in the derivation of the equations used to determine the weights in the kriging estimator. IDW is only heuristic, i,e, there is no theory or basis for the formula used to determine the weights in IDW

Mondal's comment is meaningless since there are no "known weights"

There are many empirical comparisons of different interpolation methods, each based on using one particular data set. They do not provide a comparison of the "methods" only a comparison of the numerical results for one data set and dependent on how well the person or group has applied the respective methods.

Note that the original question was "what is the difference between IDW and kriging", not which is better. IDW is not based on any assumptions other than that the weights are obtained by inverse distance weighting, a non-testable assumption. In contrast kriging is based on certain specific statistical assumptions. Since the data is only one non-random sample from a partial realization full statistical testing is not possible but it is possible to compute statistics to determine whether the statistical assumptions are reasonable.

ArcGIS Geostatistical Analyst complements Spatial Analyst. Most of the interpolation methods available in Spatial Analyst are represented in ArcGIS Geostatistical Analyst as well, but in Geostatistical Analyst, there are many more statistical models and tools, and all their parameters can be manipulated to derive optimum surfaces. Additionally, Geostatistical Analyst provides exploratory spatial data analysis tools not available in Spatial Analyst, such as an interactive wizard that simplifies the interpolation process and provides users with surface previews before applying them. Spatial Analyst has many functions in other areas, such as map algebra, combinational operators, and data conversion.

ArcGIS Geostatistical Analyst expands the number of deterministic and geostatistical interpolation methods and provides many additional options. In particular, Geostatistical Analyst provides a variety of different output surfaces such as prediction, probability, quantile, and error of predictions. Surfaces can be displayed as grids, contours, filled contours, and hillshades or any combination of these renderings. These surfaces can be exported in raster and shapefile formats for working together with other extensions such as ArcGIS Spatial Analyst. ArcGIS Geostatistical Analyst also includes an interactive set of exploratory spatial data analysis tools for exploring the distribution of the data, identifying local and global outliers, looking for global trends, and understanding spatial dependence in the data.

Our paper describes in detail the differences and applicability of several geostatistical interpolation techniques.

https://www.researchgate.net/publication/350062502_Spatial-Temporal_Characterization_of_Rainfall_in_Pakistan_during_the_past_Half-Century_1961-2020?_sg=qRZHEaSdYhnGyB2_swzgd46tWOT392H12cD6hljZ1xOqDiE3g2FkStITPOU318fGWEuaJa5Cs8OaVtJY6pw2ya_Hmvtbst7IIc80WIkN.fOqTvFduaeSmv8CVx8iruCtpjoVQslpvHaab0wKRN3mOaC-Xmurf5M8ZjVGTsA9KpmE_BxdE87B0IxZUvKxoSg

IDW is the deterministic method while Kriging is a geostatistics method. IDW assesses the predicted value by taking an average of all the known locations and allocating greater weights to adjacent points. Both methods rely on the similarity of nearby sample points to create the surface. Deterministic techniques use mathematical functions for interpolation. Geostatistics relies on both statistical and mathematical methods, which can be used to create surfaces and assess the uncertainty of the predictions.

for more details, you can read (Using_ArcGIS_geostatistical_analyst)

Hi, interpolation with IDW is one of the definite methods, but in kriging method, it is geostatistical methods and it is based on statistical methods.....

In definite methods, the model is based on mathematical functions and the procedure is performed on the sample points, but the geostatistical methods are based on the first principle of geography, ie the phenomena are more similar to each other .

In this paper they analyze the differences Article Spatial Interpolation of Reference Evapotranspiration in Ind...

just now, i am starting to learn about transformers

i found that , at all events IDW is an exact interpolation, whereas kriging and spline are inexact interpolations