Data revealing spatial soil heterogeneity can be obtained in an economically feasible manner using on-the-go proximal soil sensing (PSS) platforms. Gathered georeferenced measurements demonstrate changes related to physical and chemical soil attributes across an agricultural field. However, since many PSS measurements are affected by multiple soil properties to different degrees, it is important to assess soil heterogeneity using a multilayer approach. Thus, analysis of multiple layers of geospatial data leads to: 1) delineation of relatively heterogeneous field areas characterized by a particular combination of individual sensor measurements, and 2) identification of field locations representing these different combinations to be used for traditional soil sampling and analysis required for site-specific sensor calibration. The objective of this research was to develop an algorithm that would accomplish both functions. It was expected that delineated field areas would be spatially contiguous with relatively low variance for each sensor measurement. The algorithm was based on the adapted stepwise grouping method using a neighborhood search analysis (NSA). In addition, a circular area search method was implemented to define field locations that best represent each delineated field partition. The algorithm was evaluated using PSS data of varying quality from over 20 agricultural fields from Eastern Canada. To demonstrate its performance for this conference paper, field elevation, apparent soil electrical conductivity and soil pH maps from two experimental sites were used. D-optimality criterion was applied to individual sensor values corresponding to the set of selected representative field locations to evaluate the quality of these selections.