Big Data is a big deal. Vast, complex data sets gleaned from networks of connectivity points contain valuable information that is rapidly changing how business is done. Individuals are also impacted by Big Data. Without it, many technological conveniences of everyday life would not exist.
In the interconnected world, data drives decisions. Increasingly, those decisions are made by “smart” computers and machines that can seamlessly apply human logic to the process. Advanced geospatial artificial intelligence (AI) has been a boon for GIS.
Machine Learning: The Logic Behind Spatial Analysis
A subset of AI, machine learning applies algorithms to location and historical data to expand learning. From there, it makes logical decisions based on those learnings.1
Machine learning has long been an integral tool within GIS spatial analysis, specifically as it relates to problem-solving in three key areas:
- Classification: Vector machine algorithms aid in creating land-cover classification layers, which discerns land types and can provide a comparative analysis of changes over time2
- Clustering: Input data points are analyzed for patterns that point to meaningful data versus random groupings of “noise”2
- Prediction algorithms: The ability to measure variables at different locations and apply geographically weighted regression analysis which allows local relationship models to be built and mapped instead of solely global versions2
All of these categories contribute to securing data-based predictions, but even the advanced logic of machine learning requires a human component to interpret the data and draw conclusions. The inevitable push toward autonomy led to deep learning, advanced AI that perhaps holds the greatest power to change GIS.
Deep Learning: Applying Brain Power
Deep learning is the ultimate in AI discernment as it most closely mimics the function of a human brain. Unlike machine learning that requires human assistance to complete tasks, deep learning structures algorithms to make self-actualized decisions.1
Popularly dubbed an “artificial neural network” because of its autonomous logic and layered response to stimuli, deep learning has transformed how Big Data is parsed and used in geospatial AI. It “thinks” about what input it’s “looking” at and can consistently group images, detailed roadways, buildings, and other location-specific data without human intervention. As a result, tremendous amounts of data can be aggregated and applied in near real-time.
Given the immediacy of need and reliance on GIS, the appeal of deep learning among digital mapping experts is obvious.
The Lessons from AI Learning
Being able to gather and use location-based data that is continually fed into algorithms through literally a world full of sensors is an amazing accomplishment of the Internet of Things (IoT).
The greatest value of machine learning and deep learning is the practicality they provide to spatial analysis in digital mapping. End users are assured of seamless data accuracy, quality, and freshness that they can leverage in a number of ways:
- Understanding traffic patterns and improving predictions
- Enabling more accurate maps in real-time to provide timely information such as traffic accidents, congestion, or re-routing
- Empowering decision-makers to locate, select, and expand into target-rich sites
- Improving last-mile tracking
- Advancing autonomous vehicle functionality
- Pushing out location-based marketing to encourage foot traffic and sales to people in the immediate area of a business
The “humanness” of AI helps GIS professionals properly manage the quantity, speed, and varying types of Big Data. In turn, the insights extracted from location-based data strengthen decisions and, ultimately, the value of digital mapping.
Learn more about spatial analysis in The Fundamentals of Geospatial AI: How Geographic Data is Changing Industries. Click the button below to download your copy now.
1Zendesk, Zendesk Deep Learning vs. Machine Learning: What's the difference?, June 25, 2021
2Esri, Where Deep Learning Meets GIS, June 2019