Using Data Mining for Predicting Cultivable Uncultivated Regions in the Middle East

Middle-East region is mostly characterized by a hot and dry climate, vast deserts and long coastlines. Deserts cover large areas, while agricultural lands are described as small areas of arable land under perennial grass pastures or crops. In view of the harsh climate and falling ground-water level, it is critical to identify which agriculture produce to grow, and where to grow it? The traditional methods used for this purpose are expensive, complex, prone to subjectivity, risky and are timeconsuming; this points to the need of exploring novel IT techniques using Geographic Information Systems (GIS). In this paper, we present a data-driven stand-alone flexible analysis environment i.e., Spatial Prediction and Overlay Tool (SPOT). SPOT is predictive spatial data mining GIS tool designed to facilitate decision support by processing and analysing agrometeorological and socio-economic thematic maps and generating crop cultivation geo-referenced prediction maps by predicative data mining. In this paper, we present a case study of Saudi Arabia by using decade old wheat cultivation data, and compare the historically uncultivated regions predicted by SPOT with their current cultivation status. The prediction results were found to be promising after verification in time and space using latest satellite imagery followed by on-site physical ground verification using GPS.