Logistic Regression in a GIS Environment Farhad Hoseinali Graduate Student University of Tehran, Iran Email: fhoseinali@geomatics.ut.ac.ir Reza Farajloo lecturer Email: rezafarajloo@yahoo.com Mohammad Ali Rajabi Associate Professor University of Tehran Email: marajabi@ut.ac.ir Logistic regression can be used with spatial data in a Geospatial Information System (GIS) as a predictor. Most of the other regression methods are based on the assumption that the independent variables are normally distributed which is not always the case with the spatial data. However, logistic regression works without this assumption. In this paper, an application of logistic regression for mapping the potential of existing cupper in Ali-Abad (Taft, Yazd, Iran) mine is described. The basic information layers which are used include: geological map, geomagnetic data, geoelectric data and geochemical data. The other information are extracted from these data and are integrated through establishing a logistic model in a GIS environment. Data from boreholes are used to fit the model and find the coefficients as well as weight the spatial information layers. Then the model is used to determine the potential of existing cupper in the other parts of the region for which the borehole information have not been used. The results show that as a data driven model, logistic regression is comparable with some knowledge driven methods like Analytical Hierarchy Process (AHP), where experts decide on importance of information and weight of the information. Being a data driven method where the relative importance of data are determined by data itself, and having no assumption on the normality of the independent variables are two main advantageous of using logistic regression for prediction in a GIS environment.