| Resumen |
In some major cities there are stations for measuring atmospheric pollutants. These stations are often distributed in an irregular pattern. In order to predict pollutant's behavior, it is necessary to order data in a regular, uniform grid. For this, we employ expansion data algorithms. Our work centers on the software implementation and evaluation of three of these algorithms: Cressman, Voronoi and Kriging. For evaluation, we use real data of atmospheric pollutants, including the actual position of stations that measure air pollutants in Mexico City. We use actual values taken from different pollutants. |
