 Temporal data sets present a time sequential picture and help to understand processes and phenomena Temporal data is data that includes a time stamp. It contains information on ‘when’ the data was collected. Every spatial data set theoretically has a time associated with its collection and therefore can be referred to as a spatio-temporal data set. However, a single data set only represents a snap-shot in time. When several spatial datasets are acquired at different times, it generates time sequential data (truly temporal data).
Temporal data analyses are based on the basic hypothesis that data that are close to each other in time domain are likely to be more correlated to each other than data that are farther spaced in time domain. The temporal component adds a whole new dimensionality to data. Whereas spatial data analyses help to understand spatial patterns, temporal analyses help to understand processes operating over time.
The purpose of temporal data analyses is to reconstruct and understand the time dependent variable (process) and possibly predict the future course of this process based on the previously occurring pattern. However, temporal data analyses can be complicated by the fact that the time sequential data can be continuous or discontinuous. Research in temporal data analyses is still in its infancy. Current day GIS tools have limited capabilities to handle spatio-temporal datasets. Temporal data analyses are often conducted outside of popular GIS tool boxes (software packages) and more stand-alone modeling tools are used for this purpose.
Presenting temporal data sets also adds a whole new dimensionality to the field of data visualization.
Reference: Thomas Ott and Frank Swiaczny, 2001, Time-integrative Geographic Information Systems - Management and Analysis of Spatio-Temporal Data
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