Big Data analysis has impacted almost every sector of our economy, so it’s no surprise that it is also transforming the way that we work with geospatial data. This impact is a two-way, though. Just as the ability to analyze more data than ever before is making geospatial data more powerful and valuable than ever before, geospatial intelligence drawn from the IoT is super-charging Big Data analytics.
This year, we’ve seen plenty of examples of this, from the way that the IoT has helped in the Covid-19 fight to speculation about the way that it has changed ecommerce via real-time logistics monitoring and a host of associated technologies.
With these exciting new developments in mind, in this article we’ll take a look at the current state of the art when it comes to geospatial data in Big Data analysis, the factors that drive the current boom, where the two fields have constructively overlapped, and what the future holds for both.
Big Data and GIS
The first thing to note about the quickening merge of geospatial data and Big Data analysis is that this is not an entirely new phenomena. McKinsey highlighted it as the next frontier for innovation way back in 2011. Even at that near “pre-historical” time we were seeing some innovative, cross-over solutions that drew on both fields.
For almost a decade after that report, however, the cost of hardware held back mass deployment of devices that could collect geospatial data. One of the largest components of this cost was that of storage. For example, the cost per gigabyte for computer storage in 2010 was 10 cents. In 2017, that dropped by a factor of five to two cents per gigabyte. Today, as these costs continue to fall, it has finally become feasible for even small firms to deploy Big Data analysis on geospatial data.
Not that this is the only challenge facing this kind of analysis. In the past year, and particularly in the context of the geographical tracking that has been deployed to fight the Covid-19 virus, citizens have raised legitimate concerns about the amount of data collected on their movements and how it is used. While the shift to more private tools and applications isn’t a headlong rush yet, at some point it may have an affect on the geospatial intelligence industry. After all, the predictions such analysis yields is only as good as the data going in.
Also read: Democratizing access to geospatial intelligence
The applications
Despite these challenges, it’s likely that geospatial data and Big Data analytics are likely to draw closer together – both as academic disciplines and methodologies – in the coming years.
In fact, recent market reports point to dramatic growth in the sector. The global geospatial data analytics market is expected to increase in revenue from $69.9 billion in 2018 to $88.3 billion in 2020. Not only does this represent a significant investment in the industry, but also illustrates the fact that it is becoming a more secure, stable part of the technology economy.
While geospatial data analytics will doubtless find new applications in the coming few years, the largest growth will come – at least initially – in the sectors and applications where it is already in use. For clues as to the development of the sector, it’s therefore worth looking at where it is strong already. This is in three main fields:
1. Humanitarian aid
One of the foremost applications of geospatial Big Data analytics has been in the humanitarian sector. GIS IoT devices are now being used across the world to collect data in environments which were previously difficult for aid workers to access and consequently difficult to work in.
For an example of the way in which geospatial Big Data analytics can work well in this sector, take a look at the work of DigitalGlobe, a non-profit organization that sources satellite data and integrates it with other sources like social media sentiment and aerial imagery, leverages a GIS machine learning algorithm to track activity in specific locations and identify anomalies.
According to DigitalGlobe regional director Abhineet Jain, the organization collects approximately 80 gigabytes of data daily; as of January 2018, the organization had collected close to 100 petabytes of data total, an amount that would be impossible to work with if it wasn’t for Big Data techniques.
Also read: Reinventing Construction industry with Big Data analytics
2. Marketing
A more widespread use of geospatial Big Data analytics has been in marketing. Many brands now use data drawn from activity and location trackers to inform the range of products they offer to customers. This type of analysis is dependent on the kind of machine learning systems that the Big Data revolution has helped bring into the mainstream.
As an example, it’s worth reading about the way that Under Armour uses data from fitness trackers to segment their audiences based on their level of physical activity and even tailors product recommendations to customers who regularly engage in various types of sport.
3. Business Intelligence
A few years ago, it was difficult to imagine how the financial sector and geospatial data would work together – there appeared to be little value to a bank or other financial services company in knowing where their customers traveled and when.
As it turns out, this data is just as useful to the financial sector as it is in other industries. In fact, geospatial Big Data in the financial sector now plays a role in the ongoing startup boom that aims to bring geospatial analysis techniques to the heart of business decisions.
The applications are still being explored but already seem promising. Geospatial data has already been useful, for instance, in determining which branches to consolidate, as well as how satellite imagery over time can better predict a property’s risk of flooding when it comes time to determine insurance rates.
The Future
Though the confluence of Big Data and geospatial data is still relatively young, it’s not hard to predict the direction of travel: the two disciplines have much to learn from each other and are likely to become increasingly indistinguishable in the next decade. Add to this the emergence of new technologies – and particularly the role of 5G in the IoT – and it seems almost certain that we are on the threshold of a revolution for both fields.
