With a focus on mobile and web mapping, we propose several algorithms for on-the-fly generalization of point data, such as points of interest (POIs) or large point collections. In order to achieve real-time performance we use a quadtree data structure. With their hierarchical subdivision structure and progressive levels of detail, indices of the quadtree family lend themselves as auxiliary data structures to support algorithms for generalization operations, including selection, simplification, aggregation, and displace-ment of point data. The spatial index can further be used to generate several local and global measures that can then serve to make educated guesses on the density and prox-imity of points across map scales, and thus enable control of the operation of the general-ization algorithms. An implementation of the proposed algorithms has shown that thanks to the quadtree index, real-time performance can be achieved even for large point sets. Furthermore, the quadtree data structure can be extended into a caching structure, which can be used to store pre-computed generalizations; thus, a desired level of detail can simply be retrieved from cache.