We introduce a new method that provides accurate cost estimation for spatial selections, or window queries, by building wavelet-based histograms for spatial data. Our method is based upon two techniques: (a) A representation transformation in which geometric objects are represented by points in higher-dimensional space and window queries correspond to semi-infinite range-sum queries, and (b) Multiresolution wavelet decomposition that provides a time-efficient and space-efficient approximation of the underlying distribution of the multidimensional point data, especially for semi-infinite range-sum queries. We also show for the first time how a wavelet decomposition of a dense multidimensional array derived from a sparse array through a partial-sum computation can still be computed efficiently using sparse techniques by doing the processing implicitly on the original data. Our method eliminates the drawbacks of the partition-based histogram methods in previous work, and even with very small space allocation it gives excellent cost estimation over a broad range of spatial data distributions and queries.