Time Series Spatial Data
Definition
Time series spatial data are datasets where each location has values through time—imagery stacks, climate grids, sensor networks, or repeated surveys. They require models and storage that honor both spatial dependence and temporal autocorrelation. Processing includes quality masking, temporal aggregation, detrending, anomaly detection, and event characterization. Visualizations such as small multiples, animated maps, and time–space cubes help users grasp patterns.
Application
Agriculture tracks crop phenology; public health watches disease waves; transportation monitors traffic speeds; and conservation tracks forest disturbance and recovery. Businesses rely on such data for demand forecasting and risk monitoring.
FAQ
What are advantages of time–space cubes?
They index data by x, y, and time, enabling fast queries of trajectories and hotspots, and they support algorithms that consider temporal context, not just space.
How should missing time steps be handled?
Use interpolation cautiously with uncertainty flags, or carry forward previous valid values only where the process changes slowly; never fabricate during rapid events.
What methods detect structural breaks versus noise?
CUSUM tests, Bayesian change-point detection, and robust regression identify sustained shifts distinct from random fluctuations.
How can seasonality be leveraged rather than treated as a nuisance?
Model seasonal components explicitly (STL decomposition) and assess anomalies relative to expected seasonal baselines for clearer signals.
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