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The methods are also very time-saving, so the whole analytical process is much better and quicker with this software. It allows users to gain valuable and more profound insights by analyzing vast and complex datasets. It also enables users to see the connection between different datasets by observing the trends in the analysis. Find and keep casual relationships in time series data: There are a vast number of time series data in the usual datasets.
This software places many time series into TCM which then finds the casual relationships and allows the program to determine the best predictor for each included set. Locate and observe datasets and geographical locations: IBM SPSS Statistics features geospatial analytics options which allow users to find relationships between any dataset that is tied to a graphical area. Generalized Spatial Association Rule: GSAR allows users to find associations between non-spatial and spatial attributes.
There is also the use of historical data related to location, time an event had happened there and the type of event. This feature is used to a great extent in different security organizations against crime and various researchers and medical councils against the outbreak of any disease like dengue etc.
Spatiotemporal Prediction: STP is employed to fit linear models for various measurements that are taken over time at different locations in 2D and 3D. With this feature, users can then predict changes that happen at those places in the future.
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