Time sequence evaluation is already difficult by itself — throw in geospatial information, and also you’ve received an entire new puzzle to resolve. Understanding spatio-temporal patterns is essential in fields like local weather science, city planning, epidemiology, and transportation. Conventional time sequence fashions usually ignore the spatial relationships between information factors, whereas classical spatial evaluation strategies don’t contemplate the time-dependent dynamics of occasions. So, how will we merge the perfect of each worlds?
By leveraging Python libraries like GeoPandas, Dask, XGBoost, and deep studying fashions, we will successfully course of and forecast spatio-temporal developments.
