Aerospace & defenseJuly 10, 2023

Geospatial Analytics: Empowering consumer-focused enterprises

Location intelligence is allowing organizations to look at value creation in a different light and discover new and better ways of serving consumers by considering ‘where.’
Avatar Tony Velocci

To food shoppers, American grocery giant Kroger may appear basically no different from any other supermarket. However, what they may not know is that the store is actually in the vanguard of a quiet but profound technology revolution. It has started using driverless trucks to transport goods between an automated e-commerce warehouse in Dallas and multiple grocery stores in that geographic market.

With trucks provided through a partnership with Gatik, autonomous box trucks will deliver fresh products from a Kroger Customer Fulfillment Center in Dallas, Texas to multiple retail locations. Each truck features a cold chain-capable 20-foot box designed to transport all types of goods quickly and safely.

Why should consumers care?

Not only will it provide them access to products faster and more reliably than ever before, but Kroger believes autonomous delivery will reduce costs and offer dedicated capacity. That’s a big deal in the wake of widespread COVID-related product shortages and supply chain problems.

For Kroger, perhaps the biggest challenge that autonomous vehicles may solve is the costs associated with hiring drivers, as well as the uncertainties of hiring and retaining enough of them. In the U.S., for example, the American Trucking Association estimates a shortage of more than 80,000 truck drivers.

Kroger is far from alone in its strategic pursuit. It’s following several other enterprises, including global shipping and mailing company Pitney Bowes, in opting to manage middle-mile logistics using self-driving vehicles. Separately, eight start-up companies have raised a collective $1.4 billion over the last two years for self-driving trucks, including Class 8 long-haul tractor-trailer rigs.

What do all of these forward-leaning initiatives have in common? They would be impossible to execute without something called geospatial analytics (GA).

What is Geospatial Analytics?

What is geospatial analytics? It uses data from multiple technologies—Global Positioning System (GPS), satellite imagery, ground- and space-based location sensors, social media, and mobile devices—to build data for understanding phenomena and finding trends in complex relationships between people and places.

This geo-referenced data can be applied to nearly any happening anywhere in the world. There are dozens of geospatial analytical applications or “use cases” that include agriculture, financial services and insurance; risk management; energy, resources and industrials; and of course transportation.

So rapidly are consumer use cases expanding that GA is revolutionizing the world more than most people probably realize even though it touches them every day, according to Roberta Rocca, senior manager at Accenture and industry expert in the field. From on-demand ride-hailing to mapping the most efficient delivery routes autonomously, digital services that are built on geospatial technology have quietly become indispensable to daily life.

What is driving the use of geospatial analytics?

Driving the growth of GA are four trends, according to industry observers:

  1. The volume and diversity of location data are exploding.
  2. The cost of acquiring and analyzing it is declining.
  3. Artificial intelligence (AI) is aiding in mining big data.
  4. Innovative applications of that data are constantly emerging.

Because of these trends, businesses across sectors are realizing the benefits of GA to speed up processes while cutting costs.

For example, one leading Indian bank was able to reduce the traditional credit decision-making process for farmers to just a few days from the industry average of 15 days or more by analyzing imagery instead of inspecting farmland in person. Another example is Lowe’s home and garden retailer, which optimized new-store locations by identifying trade areas with a favorable demographic profile.

Then, there is the California interior-design firm that’s using location data to support its shift to a hybrid workforce model. Through the ease of a mobile app, employees can view real-time and projected office occupancy and book open desks, with indoor wayfinding to help them navigate office floors, facilitating better space planning and social distancing adherence, as needed.

RBC, the largest Canadian bank, uses geospatial analytics to obtain a wealth of information on 22 of the world’s largest and most influential seaports. They’ve learned that two of them, both located in Southern California, faced unique and troubling challenges. RBC’s “turnaround” metric—the time required for a container ship to enter the port boundary, unload, reload containers and leave—had ballooned to 7.5 days versus pre-COVID norms of 3.5 days.

“AI and related technologies have supercharged organizations’ ability to analyze vast quantities of geospatial information,” said Matt Gentile, a Deloitte principal.

Spatial thinking has entered the mainstream and grown more comprehensive, declared Rocca,. Not only are companies going through a fundamental shift in the form of techniques, tools and how to apply them, but they’ve begun exploring more complicated markets.

Moreover, when equipped with location intelligence, organizations can flip conventional business problems on their heads by reframing them as problems of location. “Over the next several years, we’re likely to see a seamless integration of geospatial data services, enabling [geospatial analytics] to become even more capable,” Rocca said.

Adoption of geospatial technology around the world

Many organizations have been slow to experiment with GA due to the high cost of gathering data and drawing insights from it, but with the cost of sensors and other data-collection devices declining sharply, adoption is growing rapidly.

For example, in a treatise on the rise of spatial thinking, Bluetooth tags with integrated power-harvesting are projected to drop in price by two-thirds in the near term, according to Deloitte. The cost of location-enabled chips for cellular activity declined by 70% in the past five years. Even the cost of launching satellites has fallen dramatically over the past decade on a per-kilogram basis, which means there almost certainly will be more data-collecting satellite launches in coming years.

There’s no question that private-sector efforts to acquire geospatial data are increasing.

The global location-intelligence market is projected to grow at a compound annual growth rate of 15.6% from 2023 to 2030 as the penetration of smart devices and increasing investments in IoT and network services facilitate smarter applications and better network connectivity. Further, more than a third of large and mid-sized organizations are expected to be using location-intelligence software by next year compared to 10% in 2019, and by 2025 nearly half of connected IoT devices will be capable of sharing their location, up from 10% in 2000, according to Space Data Marketplace.    

Altogether, the market for geospatial data is forecast to reach $8 billion by 2029, up from $4.6 billion in 2019, and “use cases”—the practical application of GA—is increasing exponentially, according to Ron Epstein, chief aerospace analyst at Bank of America.

Seraphim Space, one of the world’s leading investors in space technology investment, has identified GA applications across practically every industry, according to Maureen Haverty, vice president. The biggest area of opportunity may be sustainability, she added.

Geospatial analytics has the ability to provide insights into complex system relationships that improve life on Earth— from smart mobility, disaster management, urban planning and development, air-quality management, conservation of the environment, and even smart agriculture.

Look at Hong Kong for example. Arup, a global firm of designers, engineers, architects, planners, consultants and technical specialists, developed a Green Resilient strategy aiming to promote a low-carbon economy, make business more productive, and improve the quality of urban life in Hong Kong. And, thanks to geospatial analytics, they have already made great strides.

Using Dassault Systèmes 3DEXPERIENCE platform on the cloud, Arup utilizes the power of virtual twin technology and spatial data on a singular collaborative platform to address urban challenges and promote sustainability and efficiency. With the vast amount of spatial data available to them and the ability to visualize the entire city in 3D, Hong Kong’s government is revolutionizing how they approach urban planning both above and underground—by improving how pedestrians move through throughout the city and improving water, electricity, and wastewater services.

Looking into the future of GA technology

However, there is a catch to making these innovative projects a reality: Only innovative organizations that can distill actionable intelligence from geospatial data and exploit it to make better decisions and provide new consumer services are likely to thrive in the world of big data.

The problem is that the volume of information pouring in from multiple data sources is vast and finding the most promising use cases is like looking for a needle in a haystack, according to Epstein.

To help find those “needles,” organizations are employing AI and many of its subdomains, such as machine learning (ML) and computer vision. The goal: automate the extraction of data to deliver real-time geospatial insights more quickly, such as helping to deploy firefighters during rescue operations or enabling autonomous or remote navigation for air and ground vehicles.

“AI and ML have supercharged organizations’ ability to analyze vast quantities of geospatial data,” Gentile said.     

Many new tools that make use of GA are coming to market, especially industry-specific solutions that embed geospatial capabilities. Geospatial technology players, such as Dassault Systèmes, Esri and Descartes Labs, are modernizing their tools by launching software offerings on the cloud for flexible storage and processing power to handle the growing volume of data.

For their part, large-cloud computing and cloud-service providers, also known as “hyper-scalers”, have launched geospatial data and analytics offerings of their own – including services for embedding maps, geofencing and other location-based features into clients’ web and mobile applications. These enhanced capabilities will allow many more organizations to begin experimenting with geospatial analytics, including building custom geospatial consumer applications on vendors’ platforms.

Dassault Systèmes is realizing the power of combining geospatial data and virtual twin technology with the 3DEXPERIENCE platform on the cloud. By using spatial data to create a virtual twin of products, systems, systems of systems, and even the Earth, businesses can derive insights and predictions on how systems react as the geospatial data changes. For example, users can simulate and visualize how an entire city will be affected when a natural disaster strikes. Construction companies can then test the best ways to adapt their initial design, materials and architecture to fight this potential threat.

Furthermore, advances in AI, sensors and big data analytics are enabling the implementation of remote sensing technologies for plant, fruit, weed, pest and disease identification and management, according Panos M. Pardalos, distinguished professor at the University of Florida and an authority on data mining and massive computing.

“These technologies can help provide a unique opportunity for developing intelligent agricultural systems for precision applications that potentially could revolutionize the specialty crop industry,” he said.

In another example of the power of AI-enhanced location intelligence, the Woolsey Fire in California in 2018 damaged thousands of homes and properties owned by financial services company USAA. Processing that many claims could have taken months and required extensive manual effort to verify the claims’ validity if USAA had followed conventional thinking.

Instead, it looked to geospatial analysis for help. Using aerial drones, USAA captured high-resolution imagery of the affected areas. Then it used AI to scour the imagery and spatially verify properties that had been damaged or destroyed, shrinking its analysis to hours from months.

By thinking geospatially, USAA unlocked new value for stakeholders: faster claims for customers, greater efficiency for claims adjusters and more stability for communities.   

The story of geospatial analytics is about the evolution of how technology can be used to serve all kinds of consumers and, in the process, enrich the customer experience, according to Epstein.

He added: “It’s not just what you sell anymore, but also how customers engage with your products and services, and a shared foundation of geospatial data is fundamental to the new reality of the digital world.”

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