Smart city location concept

GIS makes the journey from 2D maps to location intelligence

Image credit: Sasinparaksa/Dreamstime

Applications that combine geographic information systems with emerging technologies like artificial intelligence and the Internet of Things are prompting operators to think about how they can use data to provide location-based services more sophisticated than the ones we already take for granted.

Once confined to an exclusive club of urban planners, mapping departments and analysts, the use of geographic information systems (GIS) is now prolific. GIS underpins many end-user applications that have become fundamental to governments and consumers. After more than a decade of rapidly growing smartphone usage and rolling out of Internet of Things (IoT) infrastructure, geospatial data and location-enabled applications are ubiquitous.

That’s because the collection, analysis and actioning of this data forms the basis for better decision-making that enhances citizen and customer service experiences, protects infrastructure and the natural world, and improves health and safety. None of this is easy. Separating the signal from the noise is necessary to determine why something has occurred and ultimately to influence what could and should occur in the future.

Consequently, GIS has moved well beyond the 2D map. It has evolved to more workflow-focused apps that power the entire enterprise with location intelligence.

A common struggle involves finding the answers to operational challenges hidden in the data to determine trends, while also discovering deviations from so called ‘pattern-of-life’ or expected data values, which signal anomalies that must be addressed. Many public authorities are tapping into this by carrying out various forms of GIS-based ‘smart monitoring’, through which IoT sensor data is collected, analysed and compared to historical data, for both real-time analytics and trends analysis. 

One such example is micro-mobility, a booming business that has arisen quickly and caused challenges for urban areas. Cities can track shared mobility vehicles, such as e-scooters, bikes and cars, then perform spatial and other data analysis to track patterns of movement and how events and city regulations influence behaviour. Another example is the growing problem of missing manhole covers, which poses safety hazards for drivers and pedestrians. By combining 3D city data with real-time sensor data of the infrastructure and setting up alert notifications and response workflows, cities can efficiently monitor and act when problems occur.

Location-based data can also help mitigate disasters and emergencies that put lives at risk. Increasingly, this is powered by machine learning, which automates processes like detecting changes and predicting future affects.

Prior to an incident, geospatial data helps identify areas of concern, such as regions with high fire probability, by using satellite imagery to extract vegetation and moisture content data and combine it with meteorological data. 3D modelling and flood forecasting based on historic and live geographic sensor data can help inform preventative measures to protect vulnerable communities from the disastrous effects of a flood and inform intelligent early warning systems.

These capabilities can also benefit organisations during an emergency. Of course, pinpointing a caller’s location is of utmost importance and forms the basis for effective emergency response, but there are even more advanced geospatial capabilities that can improve public safety, such as combining 2D and 3D city and building models, sensor feeds near the scene, and incident details to provide emergency services with better situational awareness. By previewing dangerous situations or damaged buildings, first responders can better protect citizens.

While these examples have involved specific use-cases, they don’t imply limited uses. Extensive collaboration between departments, systems and users is needed to make sure location-based data can be actioned correctly.

For example, an operational system for a transport authority would include not only the central server that interfaces to all the different databases and a 2D map-based presentation, but also the linear referencing system, a 3D digital twin of the entire city’s entire transport network, artificial intelligence (AI) to enrich information and analysis, asset management capabilities across desktop and mobile devices, browser-based business applications for different departments to analyse and report information, customer-facing dashboards and apps, and much more.

By combining all asset and spatial data into an integrated transport network information system and common operational picture, organisations can avoid data duplication and ensure users have access to accurate and up-to-date information and capabilities across the enterprise.

While GIS might seem simple, its evolution to enable true location intelligence now has a daily impact on citizens' lives that is far from trivial. It is a vital part of smart cities and safe societies, informing a growing number of technologies that monitor for and help mitigate situations that threaten safety and reduce the quality of services. By thinking beyond the 2D map, with applications that leverage IoT data, AI capabilities, and 3D digital realities, governments and other organisations can maximise their data, operationalise information and capabilities, and improve the services provided to citizens and customers.

Armin Hoff is vice-president of the Europe & Global Partner Programme at Hexagon’s Geospatial Division

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