When you want to measure the distance between two points, do you reach for a ruler or a Vernier caliper, or the app in your mobile? This begs the question, what is by default? Could it be that what you are convenient with becomes ‘by default’? But default changes with the situation, the precision and the tools at hand. Therefore, if you are just measuring distance on a map, a ruler might do, but in a precision machine shop, you would go for a Vernier calliper. If you had neither, you might opt for the app.
Geospatial becomes by default when it is the right tool providing the right precision at the right time. How often do planners, decision makers and analysts reach for digital spatial data when they need some information? I believe the jury is still out on that. Digital spatial data is not easy to use. It needs expertise and tools which many or may not be readily available. In my last editorial, I had said that the time has come to define Geospatial 4.0, which would be appropriate for IR 4.0. It may be recalled that IR 4.0 is essentially coming together of machines and citizens in a synergetic manner such that the boundaries are blurred.
Perhaps the best example of such synergy is location applications, which enable citizens to navigate to points of interest using technologies like GPS, GIS and communications seamlessly, to satisfy their human need of, say, finding a Chinese restaurant. That is what I would call a geospatial by default usage. Location is not just for the common citizen but also for precision positioning required for surveying, precision agriculture and even in a battlefield. Which perhaps explains the brouhaha over the denial of the use of precision signals from Galileo, now that the UK is going to exit the European Union. Of course the UK could still use the US GPS, but precision GPS is not available by default, an issue that impacts mission-critical applications. Therefore, the call is out for a regional system for the UK.
Geospatial by default is subject to jurisdictional and regulatory issues, apart from the issues mentioned above, and perhaps the most important of issues that is forgotten in the euphoria of applications is the issue of privacy. It is well known that many countries have objected to street view images on the grounds of privacy. Blurring of car number plates and faces might be a solution, but what about security installations? As the UAV becomes ubiquitous, it also adds to the security and privacy issues. Would anyone be ready to have one’s residence imaged at centimeter resolution for a smart city project?
24X7 tracking of mobile phones might be very useful to monitor traffic, but becomes invasive when the tracking goes individual and maps out a personal journey, as is the case with Google. Willingly sharing one’s personal information, including place and time for a service, like roadside breakdown assistance, is one thing, but if that information is shared without permission to say a car dealer, then there is a problem. Many of these problems arise because of application of Artificial Intelligence on the huge volumes of data we generate unknowingly. Our digital life leaves a huge data trail from location to financial details and even food preferences.
In her book, The Big Nine: How the Tech Titans and Their Thinking Machines Could Warp Humanity, Amy Webb describes the way China is on the rampage with State supported AI. Among other things, it is also using AI to create an obedient populace where the ‘trustworthy’ will be allowed to roam ‘everywhere under heaven’ while the others will find it hard to ‘take a single step’. This is a very chilling application of location.
However, once these issues are understood, analyzed and corrective measures taken, then geospatial can well become the default in a wide range of citizen-oriented applications. Many NGOs are overcoming the paucity of spatial information using satellite imagery and tablets to serve marginalized rural communities. UAVs are being used to map remote areas within forests to establish the rights of tribal populations. But the greatest technology that will help realize Geospatial 4.0 is AI-based analytics. The ultimate goal will be a deep learning system that will learn on its own without human programmers having to train the system. That is when we will have achieved geospatial as default.