How are digital technologies influencing geospatial technology trends?

How are digital technologies influencing geospatial technology trends?


Geospatial technology trends — the cross linkages of geospatial and digital technologies, and how it rides on other platforms to produce knowledge or deliver complicated solutions?

Definitions can be a tricky thing. Especially in the world of geospatial where the technology merges with everything to finally empower the processes. If one were to define Geospatial technology with the typical dictionary definition of “any technology that enables the creation, management, analysis and visualization of Geospatial data”, then would it be correct to identify that any field/domain that uses spatial information and maps, as part of the Geospatial Industry? And as a natural extension, would it be accurate to identify anyone who works with spatial data as a Geospatial professional?

Most applications and technologies today use location data for some purpose or the other. Self-driving cars, UAVs, Wearables, Augmented Reality, Internet of Things all use spatial data and maps for a wide variety of purposes and applications.

The GeoBuiz 2018 report from Geospatial Media and Communications explains the relevance of geospatial information and technologies in the digital world. The report not only explains the geospatial ecosystem but also its cross linkages with other technologies, and how geospatial either gets integrated or rides on other platforms to produce knowledge or deliver complicated solutions to human problems

Geospatial technology trends
Geospatial in digital world

The diagram above explains the geospatial ecosystem in a digital environment.  Pillar I of the geospatial ecosystem lists out the technology segments that are involved in spatial data collection and analytics, namely, GNSS and Positioning, GIS and Spatial Analytics, Earth Observation and 3D Scanning. The second column lists out various platforms that facilitate the outreach of geospatial technologies in their process of reaching out to users, standards, open data, and interconnected systems. The third column stands for the processes on which geospatial rides — business intelligence, digital engineering, and workflow automation. Finally, we come to the mediums that are used to deliver the end-to-end solutions — enterprise technologies, social media, mobile apps and web portals. The common drivers in this entire process are Internet of Things (IoT), Artificial Intelligence, Cloud, Wireless and Broadband and Big Data. The end users of the solutions created could be citizens, governments or private enterprises, who all can be clubbed together as Geo Users.

The Facilitators

Standards: One interesting aspect of the recent technology advancements is the ability of interoperability, or the capacity of different and diverse systems, data, platforms, processes and services to efficiently and effectively work together. Interoperability between heterogeneous computer systems is essential to providing geospatial data, maps, cartographic and decision support services, and analytical functions. A well-defined geospatial data management and analysis standards addresses the interoperability and redundancy issues with different datasets, thus increasing efficiencies and effective utilization for the stakeholders. Geospatial interoperability is dependent on standards, which are essential to advancing data access and collaborations in e-Government, natural hazards, weather and climate, exploration, and global earth observation.

A study by NASA’s Geospatial Interoperability Office — Geospatial Interoperability Return on Investment Study — back in 2005 found that projects adopting and implementing geospatial interoperability standards saved 26.2% compared to projects that relied upon a proprietary standards. For every $4.00 spent on projects based on proprietary platforms, the same value could be achieved with $3.00 if the project were based on open standards.

Open Data: The value of open data is well established now and ranges from improved efficiency of public administrations and economic growth in the private sector to wider social welfare. A McKinsey report says open data — public information and shared data from private sources — can help create $3 trillion a year of value in seven areas of the global economy – Education, Transportation, Consumer Products, Electricity, Oil & Gas, Health Care and  Consumer Finance.

Governments across the world are moving toward opening their datasets. While the US government launched an open data initiative in 2011, the European Union is working on regulations to unlock the data held by European institutions. Between 2016 and 2020, the market size of open data for the EU 28+ is expected to increase 36.9% from €55.3 billion to €75.7 billion.

If geospatial systems have to remain relevant in a fast-changing world, then data sources that go beyond imagery and maps must become a part of the analysts’ armory. To make sense of this huge amount data — 2.5 quintillion bytes of data are created every day — the ability to link information on the Web will be increasingly important in the coming years. To this end, we may see data increasingly being distributed as ‘linked data’ in the coming five to ten years. Linked data offers the opportunity to connect data to other pieces of data on the Web, contextualizing and adding value to the information that already exists. The UNGGIM’s report ‘Future Trends In Geospatial Information Management: The Five To Ten Year Vision‘ in 2016 lists Open and Linked Data as one of the most prominent trends for the future.

Interconnected Systems: The digital world is enabled by a number of interconnected systems, acting as a key facilitator of collection, processing and communication of georeferenced datasets. As the world is beginning to reject systems that create silos or force inefficient workflows, interconnected systems are the key to making disparate solutions fit the customer workflow rather than the other way around. Since there are too many permutations to simply make everything ‘plug and play’, interconnected systems are foreseen to be a dominating trend across the industry for the next five years. It is critical that different subsystems are integrated seamlessly to deliver the maximum value of location insights efficiently on time.

Further, geospatial capabilities are integrated into all kinds of systems wherein they don’t exist as separate industries. Further, geospatial capabilities are being integrated into all kinds of systems wherein they don’t exist as separate industries. The expansion of the definition of what constitutes geospatial content is driven by tremendous innovation in the technologies used to generate them or the ones it rides on — faster processors, better displays, wireless networks, online databases, fixed and mobile sensors etc. So what we are witnessing is that these disparate technologies grow new branches and spawn new hybrids as inventive minds seek new solutions.

The Processes

Business Intelligence (BI): About 80% of all data stored in corporate databases has a spatial component. Traditional means of presenting data to users have been long reports either with graphs and pie charts or in a spreadsheet fashion. Now, given the complex interrelationships of multidimensional data, Integrating spatial data and visualization technology can provide appropriate visualizations for giving accurate, high impact insights to business intelligence users.

Humans think visually, therefore spatially. Therefore, BI environments must provide visualization techniques based on spatial relationships. In this background, spatial data is becoming a boon to analysts, wherein the definition of a modern warehouse should include “space-centric” along with traditional characteristics such as “integrated” and “time-variant.”

Digital Engineering: The key to successful digital engineering — or Building Information Modelling (BIM) — lies in proper management of data. Aligning that data across multiple project stakeholders and project lifecycle phases provides a platform on which the model can be built. That said, digital engineering is not limited to just creating models. Unlocking knowledge and insight, and creating the platform for true collaboration are its mainstays.

Efficient management of geospatial information is fundamental to the success of engineering projects across the private and public sectors, underpinning a wide range of activities — from sustainable development, conservation and environmental monitoring to infrastructure planning, implementation and asset management. If BIM is about the purposeful management of information throughout the project life cycle — for infrastructure as well as buildings — then geospatial data will become a significant aspect of that.

Workflow AutomationAs the debate rages on over robotics and automation will take away jobs — Oxford University researchers have estimated that 47% of US jobs could be automated within the next two decades — in the geospatial space too, unmanned and remote autonomous systems and interactive robots are boldly going where no one has gone before.

The growing adoption of unmanned platforms such as UAVs and robotics has taken hold in the public safety and security space. From tactical military situations to routine security patrolling, these platforms have the potential to transform traditional security and public safety processes. Automation is already addressing challenges in risky or complicated operations where continuous human presence is either undesirable or impossible, such as such as mining, engineering systems or utility maintenance. However, automation of remote operations cannot rely on remote control alone. Remote control can only be the first step in any autonomous operation, where gradually more and more work will be automated, and the role of the human operator will just be limited to supervising or monitoring largely autonomous operations.

The Mediums

Enterprise Systems: Like all other enterprise systems — essentially large-scale application software packages that support information flows, business processes and data analytics — enterprise GIS is a system that is integrated for the entire organization to enable all its employees to manage, share, use, create, visualize, analyze and disseminate spatial data. GIS-based data service levels may be formal or informal agreements between departments, divisions, or external agencies such that the enterprise GIS is the application system that holds a process together.

Social Media: Ubiquity of location and smartphones in every hand has led to an ever-increasing amount of spatially located information on social media — in most cases, without it even being a conscious decision of the user. In addition to an exponential increase in the availability of geo-referenced information, as the use of social media for providing real-time information and expanded functionality increases, it offers newer opportunities for location-based service providers to further detect patterns and behavior prediction.

Mobile Apps: The growth of apps has been driven by the evolution of mobile devices like tablets and smartphones. Typically, a user may want to know directions to a restaurant. All the user has to do is open the app on his smartphone, select his location and the name of the restaurant. The app does the rest and provides the answer on the device by showing the route on a map with alternate routes depending on the choice of transport. What is new in the apps world are professional apps that cater to users from the fields of science, engineering, defense, homeland security, administration and business. Today, soldiers, police personnel, firefighters and rescue teams require apps to navigate areas under threat or damage. Professionals do not have to go back to their desktops for analysis thus enabling in situ decision making and viewing the results in real time or near real time. Apps are platform and software neutral and have to follow open standards to be acceptable in the marketplace. This is an excellent opening for independent developers.

Web Portals: Geospatial Web portals, often referred to as geoportals, are a popular way of delivering geographic information and associated services, such as, updates, analysis, etc., via the Internet. Geoportals are important for effective use of GIS and are a key element of Spatial Data Infrastructure any nation. Geographic information providers, including government agencies and commercial sources, use geoportals to publish geospatial metadata of their geographic information. Geoportals can help avoid duplicated efforts, inconsistencies, delays, confusion, and wasted resources.


The Drivers

Internet of Things: Fundamentally, IoT is about devices connected to the Internet or Machine-to-Machine (M2M) communication. But, the real value of this technology is not based in the tons of data these sensors and devices are gathering every second of the minute. Its true potential depends on that data’s analysis, and on how Cloud-based applications leverage that information. Currently, the Internet is human-orientated. The change toward machine learning or the IoT will need to take into account devices which are, for all intents and purposes, autonomous and act independently whether or not any person or any system is actively using them. UNGGIM predicts that in the next 5 to 10 years, we may see significant developments in the architecture of the Internet.

Needless to say, this kind of next-generation market opportunity is pulling in tens of millions in venture funding for startups as well. In 2014 alone, over $1.6 billion was invested into IoT companies by venture capitalists. 2020 will be a big year to watch out for while talking about the Internet of Things. As many as 50 billion devices will be connected to the Internet by 2020, while there is expected to be a rise of over 285% in connected devices by that time.

Artificial Intelligence (AI): AI encompasses many technologies like cognitive computing, machine learning, language processing, neural networks, data analysis, information retrieval, genetic and evolutionary computation, knowledge discovery, machine vision, and, of course, the latest catch word, deep learning. All these techniques have one common thread: how to mimic human faculties like vision, thought processes and reasoning using computers.

In geospatial systems, for example, one of the accepted methods of classification of remotely sensed data for thematic mapping is using AI software called Neural Networks. Companies like DigitalGlobe and Airbus Defence & Space are already taking help of artificial intelligence and deep learning to process large volumes of satellite imagery to identify objects and patterns automatically in huge volumes of satellite imagery. The next step will be the ability to handle information in a manner that enables a personalized experience, as is illustrated by Apple’s Siri or the Google Assistant.

A report on Preparing for the Future of AI US President’s National Science and Technology Council states that rapid growth of AI has dramatically increased the need for people with relevant skills to support and advance the field.

Artificial intelligence could dramatically boost economic growth and productivity by up to 40% in 2035 and economic growth in the US could increase from 2.6% to 4.6% over the same period with the adoption of AI technologies. Among the countries that stand to make the largest gains in productivity from AI in 2035 are Sweden, Finland, the US and Japan.

Cloud: Geospatial systems are solutions in search of innovative technologies. The emergence of Cloud computing provides a platform for the evolution in Big Data Analytics for geospatial data with an elastic, on-demand computing platform to integrate — observation systems, parameter extracting algorithms, phenomena simulations, analytical visualization and decision support, and to provide social impact and user feedback — the essential elements of the geospatial sciences.

In recent years, EO data have become available from governmental agencies as a result of the ever-increasing technological capabilities of the Web. We have also witnessed IT companies like Microsoft, Amazon and Google investing heavily in Web keeping distribution and analysis of geospatial data in mind.

Wireless & Broadband: A seamless and robust linkage between Internet GIS and mobile GIS is essential to provide spatial awareness to stakeholders and decision makers. An integrated spatial decision support system relies on three major components – Internet GIS, Mobile GIS and broadband wireless communication networks. Each component needs to be customized in order to provide real-time or near real time GIS functions.

Further, location-based services depend on two cutting-edge technologies — wireless location and mobile Internet. Recent developments in the processing capabilities of smartphones and other mobile computing devices have contributed to making LBS much more accessible to many users. Recent efforts by IT majors have led to the sharing of much more location information globally and support the concept of LBS in many parts of the world where comprehensive spatial databases are either still being developed or are too expensive to gain access to.

Big Data: In its report on Big Data, McKinsey Global Institute estimates that location data level stood at 1 petabyte in 2009 and had a growth rate of 20% a year. This did not include data from RFID sensors. It also does not include ‘dark data’, that is, data collected by researchers and lying in private archives.

As location intelligence becomes more and more relevant across industries, Big Data and its analytics is what the future holds. Big Data characterized by five Vs — Volume, Velocity, Variety, Veracity and Value. While Volume is easily understood, Velocity, Variety, Veracity and Value lie in our ability to take fast-moving data and convert it into something meaningful through analytics.

Traditional geospatial data, which includes remotely sensed data, is structured and stored for analysis post facto in analytical systems like GIS. However, modern data with useful geospatial content like photos, social media chats, video, voice and messages now constitutes almost 80% of the total data. In its unstructured form, it cannot be used in conventional analytic systems like GIS because the sheer volume far exceeds the data storage capacity available. It also has a high velocity, but its veracity may require curation. Big Data improves innovation, sustainability and translates into billions in savings.

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