This paper compares two techniques for browsing spatial objects in an R-tree based on their distances from a query object: the k-nearest neighbor algorithm and an incremental nearest neighbor algorithm. The k-nearest neighbor algorithm requires reinvocation for more neighbors, leading to redundant computations. In contrast, the incremental algorithm finds neighbors one by one, improving efficiency for distance browsing queries. The incremental algorithm is optimal with respect to the spatial data structure and performs better than the k-nearest neighbor algorithm for distance browsing. It is also more efficient than the k-nearest neighbor algorithm for the k-nearest neighbor problem, though not as much as for distance browsing. The algorithm is adapted to R-trees and shown to outperform existing k-nearest neighbor algorithms. Experiments demonstrate its superiority in performance. The paper also discusses related work, R-tree structure, the incremental algorithm, and its adaptation to R-trees. It presents an example of the algorithm's execution and discusses variants, including finding the farthest object and imposing distance constraints. The algorithm is shown to be applicable to various spatial data structures and can be extended to handle complex queries. The paper concludes that the incremental algorithm is optimal for spatial indexing and provides a more efficient solution for distance browsing queries.This paper compares two techniques for browsing spatial objects in an R-tree based on their distances from a query object: the k-nearest neighbor algorithm and an incremental nearest neighbor algorithm. The k-nearest neighbor algorithm requires reinvocation for more neighbors, leading to redundant computations. In contrast, the incremental algorithm finds neighbors one by one, improving efficiency for distance browsing queries. The incremental algorithm is optimal with respect to the spatial data structure and performs better than the k-nearest neighbor algorithm for distance browsing. It is also more efficient than the k-nearest neighbor algorithm for the k-nearest neighbor problem, though not as much as for distance browsing. The algorithm is adapted to R-trees and shown to outperform existing k-nearest neighbor algorithms. Experiments demonstrate its superiority in performance. The paper also discusses related work, R-tree structure, the incremental algorithm, and its adaptation to R-trees. It presents an example of the algorithm's execution and discusses variants, including finding the farthest object and imposing distance constraints. The algorithm is shown to be applicable to various spatial data structures and can be extended to handle complex queries. The paper concludes that the incremental algorithm is optimal for spatial indexing and provides a more efficient solution for distance browsing queries.