This paper presents a methodology for integrating a new parameter measuring spatial relationships in the hidden Markov models (HMM) in order to detect, interpret and predict changes in urban areas from satellite images. This approach is divided into three phases: the detection of different spatial relationships in the urban area; the training of a hidden Markov model using Baum-Welch learning algorithm, integrating the changing spatial relationships obtained through the Allen's temporal algebra; the interpretation of changes in urban area and the prediction of future changes. Simulated spatiotemporal changes on synthetic data show the interest of this method for the analysis of spatiotemporal changes of relations between objects. Results allows detection and prediction to be performed from the various time series of images for the observations of spatiotemporal events such as urban expansion. It is therefore reasonable to use this approach to interpret and estimate the movement of the urban area.