Capturing the Value of Place and Time with Geospatial-temporal Insights

IBM Research is introducing an experimental offering named IBM PAIRS Geoscope (Physical Analytics Integrated Data Repository & Services), a unique cloud-centric geospatial information and analytics service that can accelerate the discovery of new insights.

Terms like big data, analytics, data science, and the Internet of Things (IoT) have arisen in recent years to help explain a world awash in data. Fueled by increasingly sophisticated and affordable electronics, the exponential growth rates of data created each day is expected to continue unabated for years to come.

By Hendrik Hamann  
Senior Manager, Physical Analytics, IBM Research

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Virtually all human activities will be impacted by this age of data, and those who can quickly extract value from this superabundant resource will enjoy a decided advantage.

Extracting value from the vast and ever-growing stores of geospatial-temporal big data poses a significant challenge. This class of big data, so named because of its inherent link to place and time, includes satellite and aerial imagery, global-scale data and models (weather, climate, oceans, etc.), geo-referenced IoT/sensor networks, and big-event data captured on platforms like Twitter and GDELT.  

Such data is often freely available, but its massive size and the complexities associated with its preparation for use make it difficult to exploit and scale, especially for large areas and time-critical applications.

IBM PAIRS Geoscope arose from a project and engagement a few years ago with the E. & J. Gallo Winery. In an effort to conserve water while improving crop uniformity and yield, IBM and Gallo co-developed a precision irrigation system that incorporated a cloud-based communication network, hundreds of sensors and actuators, satellite imagery to measure the uniformity and health of the greenery, a complex model for estimating water loss from greenery and soil that required numerous meteorological and atmospheric parameters from a variety of sources, and a localized weather model to estimate future irrigation needs.

In addition to demonstrating a new form of a potentially commercial water-efficient drip irrigation technology, a two-season trial of this system on a ten-acre test ranch delivered a 26 percent increase in crop yield, a 50 percent increase in crop uniformity, and a doubling of a key crop quality index, all while using up to 22 percent less water.

This experience taught us that rapidly obtaining insights and value from an unwieldy mix of large geospatial-temporal datasets required new thinking on at least two fronts:

• First, geospatial-temporal datasets are often too large to transfer for analysis in a reasonable time. It is projected, for example, that data generation rates from just IoT alone could reach 600 ZB per year by 2020.
• Second, geospatial-temporal datasets exhibit a daunting array of complex formats. Understanding and curating this diversity can be an arduous task that hinders rapid analysis. On both fronts, significant and sometimes insurmountable bottlenecks are encountered when attempting to bring the data to the analytics.

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• GIS & Remote Sensing Technology
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• GIS for network
• GIS Spatial Analysis
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