In a context where managing multiple spatial datasets in transportation is increasingly complex, the analysis of such data faces significant challenges. The redundancy of repetitive tasks and time-consuming manipulations make analysis processes lengthy and costly, impeding the efficiency of mobility and transportation studies.
My research aims to address these issues by developing an innovative tool capable of facilitating the fusion and integration of various geospatial datasets in transportation. This tool will enable the efficient handling of multi-source and multi-scale data, including points (households), lines (transport networks), and polygons (land evaluation zones), while automating repetitive and time-intensive tasks.
The proposed approach also allows for the calculation and visualization of buffers at different scales, the extraction of relevant metrics such as density, averages, or standard deviations, and the intuitive presentation of results. This automation aims to optimize analysis processes, reduce delays, and enhance the accuracy of results.
By simplifying these complex manipulations, this research will contribute to a better understanding of spatial dynamics while providing a powerful tool for researchers and decision-makers in transportation.
