spatiotemporele

Spatiotemporele refers to the joint consideration of spatial and temporal dimensions in data and analysis. It captures how processes unfold across locations and through time, requiring the integration of geographical information with time-series data. The concept is widely used across disciplines to study dynamic phenomena.

In physics and cosmology, spatiotemporal ideas relate to spacetime, the four-dimensional continuum that combines three spatial dimensions with time. In earth sciences, spatiotemporal analysis models weather patterns, climate variability, and environmental change by linking where something happens with when it happens. In epidemiology, it helps track disease incidence and spread, accounting for spatial clustering and temporal trends. In ecology and urban planning, it models habitat use, migration, traffic flows, and urban growth over time.

Data are typically organized as spatiotemporal datasets: a spatial grid or set of locations, with measurements across successive time points. Analytical approaches include spatiotemporal statistics such as space-time autoregressive models and Bayesian hierarchical models, alongside machine learning methods that handle both space and time. Examples include sequence models with spatial context, convolutional neural networks extended to time (ConvLSTM), and spatiotemporal graph neural networks. Visualization often uses space-time heatmaps, animated maps, or three-dimensional representations where the third dimension is time.

Key challenges include nonstationarity across space and time, differences in data resolution, missing values, and scale dependence. Addressing these issues is essential for reliable inference and forecasting. In summary, spatiotemporele provides a framework for understanding dynamic, location-based processes by integrating where events occur with when they occur.