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Modern algorithmic spatial analytics is a relatively new field, though spatial analysis itself has a long history dating back to disease mapping circa 1854. Spatial analytics is now a key component in most data products, whether it’s location-based services or creating strategies mixing offline and online channels. Current implementations of spatial analytics relies on software that can access and merge both internal and external data, integrate specialized algorithms capable of building robust models, and being able to easily include spatial analytics in existing workflows.
Of particular interest is the ability to analyze geographic data. The number of applications using geographic data is rising rapidly and will continue to become more popular as technology improves. For example, shopping destinations use touch-screen kiosks that recognize your current location and, when prompted, provide the most expedient route to a retail destination based on variable & conditional factors. In another example, geovisualization uses spatial analytics and digital cartography to enable humans to perform geographic data mining in a three and four-dimensional space. As people continue to correlate spatial areas with data, spatial analytics methodologies will continue to play a significant role in data products for years to come.
Data Science Studio (DSS) is a powerful analytics platform that recognizes the importance of using spatial technology and content in your analytics projects. Some key spatial analytics features in DSS include:
In addition to map building, your models can be used to develop your own advanced spatial Web-based applications. Leverage the power of spatial analytics to build out Web applications that are highly accessible, based on real-time data, and engage with your users.
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