What's new in version 23?
Geospatial Association Rules
Using geospatial association rules, you can find patterns in data based on both the spatial and non-spatial properties. For example, you might identify patterns in crime data by location and demographic attributes. From these patterns, you can build rules that predict where certain types of crimes are likely to occur.
This procedure is available in the Base Statistics option.
Spatial Temporal Prediction
Spatial temporal prediction uses data that contains location data, input fields for prediction (predictors), a time field, and a target field. Each location has numerous rows in the data that represents the values of each predictor at each time interval at each location.
This procedure is available in the Base Statistics option.
Temporal Causal Models
Temporal causal modeling attempts to discover key causal relationships in time series data. In temporal causal modeling, you specify a set of target series and a set of candidate inputs to those targets. The procedure then builds an autoregressive time series model for each target and includes only those inputs that have a causal relationship with the target. This approach differs from traditional time series modeling where you must explicitly specify the predictors for a target series. Since temporal causal modeling typically involves building models for multiple related time series, the result is referred to as a model system.
Temporal causal modeling procedures are available in the Forecasting option.
Bulk Loading to a database
When you export data to a database, bulk loading submits data to the database in batches instead of one record at a time. This action can make the operation much faster, particularly for large data files.
Programmability enhancements
- You can now run R programs that use functions in the R Integration Package for IBM® SPSS® Statistics from any external R process, such as an R IDE or the R interpreter. You can also now run SPSS Statistics command syntax from R.
- Extension commands that are implemented in Python or R now support the use of the TO and ALL keywords in variable lists.
- IBM SPSS Statistics - Essentials for R and IBM SPSS Statistics - Essentials for Python now include many more extension commands, with associated custom dialogs. Also, help for all extension commands that are installed with Essentials for R and Essentials for Python is now available by pressing the F1 key in the syntax editor.