Today we are announcing the new IBM SPSS Modeler 17.

SPSS Modeler is the leading data mining workbench for predictive analytics. It enables you to explore data, clean it and build predictive models.  IBM SPSS Modeler helps your users and systems make the right decision every time.

SPSS Modeler comes with a user-friendly interface that makes possible to analyze complex data sets and create powerful predictive models without programming. However the sophistication is there for those who want to take advantage of it. SPSS Modeler is coming with very powerful algorithms from IBM and also you can bring your own integrating with Open Source technologies such as R and Python.

Is this new release we are including Geospatial Analytics. We are adding support for geospatial data sources, geospatial data preparation capabilities and powerful mapping visualization.

You will find the following new SPSS nodes:

-Geospatial Source Node: Bring map or spatial data into your session. Accepts shape files (.shp) and also connection to ESRI server.

Reprojection Node: Use it to change the coordinate system of your geospatial data.

-Spatio-Temporal Prediction: Algorithm to analyze location data with time field associated. It will predict not only what is going to happen in the future but also where and when!

-Maps Visualization Node: Display geospatial data on a map as a series of layers.

Association Rules Node: We updated this powerful association algorithm and now it will find hidden connections also in geospatial data.

mapsSPSS

1 comment on"Introducing the new IBM SPSS Geospatial Analytics features"

  1. Shahinshah Faisal Azim October 16, 2018

    I want to conduct analyses of the ecological i.e. socioeconomic and environmental causes (independent variables) of obesity prevalence (dependent variable) in the USA. My unit of analysis is U.S. counties. I have compiled my dataset with FIPS codes from American Community Survey (ACS) and United States Department of Agriculture (USDA) Economic Research Service (ERS). I can see that there are spatial clusters in the prevalence of obesity rates across the USA, and expect to find spatial autocorrelation. Some authors who have conducted studies on such issues have calculated Moron’s I to find spatial autocorrelation factor for use in multiple regression. However, they have used geoda software prepared at the University of Chicago, but I am not familiar with this software and am comfortable with using SPSS. I was just concerned if SPSS can account for spatial autocorrelation while conducting analyses of my data. Can you please guide me on this issue. Will SPSS serve my purpose or I have to learn geoda software.

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