An Association Rules Based Approach to Predict Semantic Land Use Evolution in the French City of Saint-Denis

This paper proposes a predictive approach of semantic land use changes based on data mining techniques, specifically association rules (AR). Its main idea is centered around studying the past to predict the future. In other words, applying association rules technique to discover rules governing the city's past land use changes, then use them to forecast future changes. Taking La Plaine Zone in the city of Saint-Denis as a study case, the authors applied the proposed approach, in the framework of a developed Qgis plugin (Predict). The results are then highlighted in a cartographic format. To assess the quality of semantic land use changes prediction using our predictive approach, the authors proposed the Prediction precision degree (P) as an evaluation metric. A resulting value of 66% is considered promising since in this case, the authors only relay on history of land use changes for the prediction process, while considering some other evolution factors could remarkably enhance the results.