Given the widespread adoption of connected devices and increasingly precise sensors, the volume of spatiotemporal data has grown significantly in recent years. This growth necessitates the development of tools capable of analyzing large amounts of spatial and temporal data to extract relevant information and train machine learning models. However, this data can be heterogeneous and come from various sources, such as weather measurements, traffic information, weather conditions, and road accidents. In particular, this thesis aims to improve the efficiency of an existing framework for spatiotemporal data analysis by parallelizing most of the process stages using the Spark framework. The goal is to make the processing of large amounts of heterogeneous data, including US road accident data and weather condition data, more efficient in order to extract correlations between spatial and temporal events.