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Defining the Geospatial Industry

The first known use of the term “Geographic Information System” can be traced back to Dr. Roger Tomlinson’s research paper “A Geographic Information System for Regional Planning” in 1968. Since then GIS has evolved from being a niche scientific tool to a mass-market technology.

Today, GIS is being used in different industries and applications. Regardless whether the famous quote “80% of all data in the world has a spatial relation” is accurate or not; it is true that there are more than 770 million smartphones (as of 2013) with GPS capabilities and the number of smartphones with GPS/GNSS sensors have being growing each year.

The large scale use of GPS/GNSS has been one of the biggest driving factors for the growth of new applications and analysis that utilise location data. Location data is slowly transforming the mobile industry and the fact remains that most of the apps we use today, require location access.

Perhaps, it’s not difficult to see why the US Department of Labor identified Geospatial technology (besides nanotechnology and biotechnology), as one of the three most important high-growth industries of the 21st century in one of its reports.

But then again, what exactly does the Geospatial industry encompass?

Definitions can be a tricky thing; sometimes it’s rather difficult to restrict a term to its original and historic definition. If one were to define Geospatial technology as “any technology that enables the creation, management, analysis and visualisation 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?

When you think about it, most applications and technologies utilise location data for some purpose or the other. Self-driving cars, UAVs, Wearables, Augmented Reality, Internet of Things, Connected bikes and other technologies use spatial data and maps for a wide variety of purposes and applications.

Wearable Tech & Internet of Things

Google Maps democratised GIS technology and Wearable tech could do the same for some of the more niche GIS applications like Geomedicine. Wearable technologies like Fitness bands, Smartwatches, etc., might still remain a niche device but they have quite a few sensors built into them – GPS, accelerometer, heart-rate sensor, etc. These sensors together with the APIs like Apple’s Health Kit and Google Fit are going to be very helpful in bringing Geomedicine applications all the more closer to being a reality and a widely-used tool.

Data from wearable tech are already forming the base layer for some really cool and insightful visualisations like the RunKeeper map by Mapbox that visualizes the routes that people use for fitness activities in different cities across the globe. Visualizations like this can be helpful for the city planners to understand leisure activities in urban areas.

MapBox visualisation of Runkeeper data for Stockholm, Sweden (Copyright: Mapbox)

In addition to personal devices, gadgets like AirBeam – an air quality monitoring sensor, which belongs to the category of Internet of Things are assisting us to gather data about the environments that we live in, transforming the way we look at GIS applications like Smart Cities, Geomedicine and probably even the way we select the ideal neighborhood to live and work.

Machine Learning and Contextual Information

As machine learning algorithms keep improving and contextual search applications like Google’s “Now On Tap” become the norm, it would be interesting to see how location plays a role in such apps. Yes, we already do use maps to search for points of interest that are near us, but our lives are built around the different locations we frequented – home, office, schools, university, shopping centers, etc. Apps like Life360 are a precursor to the way location information plays a central role in our lives.

As applications collect more data about our activities and understand how location plays a role in it, machine learning algorithms are only going to get better at providing contextual information. Not to mention, the huge market for location-based marketing and related applications that are already benefitting from the advances in machine learning and data analysis.

Google Maps for Android Wear – Moto 360

Temporal GIS

As the idea of smart cities takes root, the next big challenge for traditional GIS is the integration of time with the 3D models. So far, GIS has been a great tool for analysing past events e.g. change detection, urban growth, disaster assessment and to model future events such as landslide prediction, risk assessment, etc.

Decision makers and urban planners would greatly benefit from tools that can help analyse and visualise 3 dimensional spatial data, integrating changes over time. It would definitely be interesting to see how GIS can help in predictive models with some sort of “Geographic Kalman filter” to help correct those predictions as more and more temporal data becomes available without having to restart the analysis from the beginning.

How does all this impact the Geospatial Professional?

GIS has undergone a huge transformation in the last decade or so. Advances in computer science and technology in general has made GIS much more capable and more importantly, accessible to everyone. Geospatial technology is now more widely used than ever, most people utilise GIS without even noticing it or labeling it as a geospatial application.

Regardless of what the definition of the Geospatial industry is, the reality is that location and spatial information play an ever-important role in the analysis and visualisation of data.

The link between Computer Science and Spatial Sciences (Copyright: Dr. Brent Hecht, Shashi Shekhar)

Location data is at the core of many Internet-era companies and this opens up a lot of possibilities and opportunities for the geospatial professional. The GIS industry is not the only industry that is using maps and spatial data anymore, everyone is and that is truly exciting. It is probably the best time to be a Geospatial professional!


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