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ABSTRACT
Surface interpolation is a crucial concept in spatial analysis and modeling, with applications in diverse fields. The choice of interpolation method depends on the specific characteristics of the data and the goals of the analysis. Continued research and innovation in surface interpolation techniques will likely play a significant role in addressing emerging challenges and advancing various scientific and technological fields. For this work, three interpolation methods were compared and analyzed as well as their results using data provided from the used to generate various DTMs namely kriging, natural neighbor, and local polynomial. We examine the application, algorithm, efficiency, and advantage of each technique. There is only the best option in specific situations; there is no one perfect way. Before choosing a spatial interpolation method that is comparatively best in a given case, one should thoroughly analyze the characteristics and theorems of each approach as well as the property and geographic analysis of data. Nonetheless, careful observations should be used to assess the result.
