MALDI Imaging is a promising new technology that allows for spatial heterogeneity in samples to be addressed. Signal intensity can be highly variable in MALDI Imaging experiments and adjusting for this variability can be difficult. We bypass the issue of signal intensity by considering presence/ absence data. This simplification allows proportions of occurrence, i.e. proportions of spectra with a peak at a particular m/z value, to be calculated. Using these proportions, signals that differentiate known spatial regions of interest can be identified by difference in proportion of occurrence statistics, or DIPPS. In many situations, this is ideal as it identifies a set of signals of interest that can serve as starting points for further analyses. We provide two example applications. The first example relates to an experiment designed to test if it is possible to detect N-linked glycans using MALDI Imaging by in situ application of PNGase. A control region of tissue was included, that was not treated with PNGase, and potential glycan signals are identified by occurring in the PNGase treated region but not the control region. The second example is an application to ovarian cancer tissue, where the interest is to identify tumour-specific signals. The tumour region is not annotated to begin with, and so is first separated by k-means clustering. Further analyses, including MS/MS identification, have been used to validate selected results in both example applications.