As the first step in the decommissioning of sasCommunity.org the site has been converted to read-only mode.
Here are some tips for How to share your SAS knowledge with your professional network.
Detecting Patterns using Geo-temporal Analysis Techniques in Big Data
New innovative analytical techniques are necessary to extract patterns in Big Data which have temporal and geo-spatial attributes. An approach to this problem is required when geo-spatial time series datasets which have billions of rows and the precision of exact latitude and longitude data makes it extremely difficult to locate patterns of interest The usual temporal bins of years, months, days, hours and minutes often do not allow the analyst control of precision necessary to find patterns of interest. Geohashing is a string representation of two dimensional geometric coordinates. Time hashing is a similar representation which maps time to preserve all temporal aspects of date and time of the data into a one dimensional set of data points. Geohashing and time hashing are both forms of a Z-order curve which maps multidimensional data into single dimensions while preserving locality of the data points. This paper explores the use of a multi-dimensional Z-order curve combining both geohashing and time hashing that is known as geo-temporal hashing or space-time boxes using SAS®. This technique provides a foundation for reducing the data into bins that can yield new methods for pattern discovery and detection in Big Data.
NOTE: the rest of this article will be fleshed out shortly.
View the pdf for.
You can also see the PowerPoint Presentation.
The code for this technique will be added to this page shortly after SAS Global Forum 2014.