IoT and the Move to Open Source GIS
In my 15 years in the geospatial industry, I’ve seen our industry respond to certain trends and take the lead in others. As with most industries, we regarded the Cloud with a certain amount of suspicion and trepidation – after all, many companies’ geospatial data is their “ace in the hole” and they initially felt better and safer keeping it on premise, on their desktops or on servers. Eventually they realized that this led to siloed data and limited access; this, and the cost factor, led to the migration to the Cloud. Data has moved from the back office to the front office. The Cloud is not only used to deliver content, but also to provide an elastic infrastructure to host, analyze, and deliver value to a global set of users.
Where we see our industry taking the lead is in the move to open source GIS. This move is not only practical for any number of reasons; the Internet of Things is going to make it inevitable. From connected sensor networks to Smart Cities, the amount of location information streaming in from an abundance of new sources is driving a change to traditional GIS technologies. The question is not whether companies and organizations will make the move to Open, it’s how fast.
While the IoT is still being defined in many industries and market segments, geospatial professionals and who we call “geo-enabled users” (non-traditional GIS users who use location information as part of their business function) require a technology platform that can make sense of all these emerging data streams. These professionals and consumers require: 1) a geospatial platform that scales (both in technology performance and in price) to consume and process massive volumes of information; and 2) one that integrates with other systems through open and interoperable interfaces and standards.
For scalability, GIS and geo-enabled users need to be able to deploy geospatial technology in an elastic infrastructure (Amazon, Azure, etc.) in modern IT methodologies (Docker, Cloud Foundry, Openshift, Chef, Puppet, and so on). Proprietary solutions installers are not built in a way that allows for this type of massive scalability and deployment. Moreover, the “pay per CPU core” mode of licensing is archaic – and meaningless in an elastic infrastructure – so GIS professionals and consumers are ultimately going to pay more for significantly less capabilities.
For interoperability and integration with third party systems, the geospatial technologies need to be able to seamlessly integrate with other third party offerings. After all, they’re not the only ones that GIS and geo-enabled users need to do their jobs. The beauty of the open source geospatial technology stack is how open it is – whether it be sharing data through open standards (OGC®, REST, etc.), or being able to take the open source code and build the connections to your own IT systems. All the building blocks are there to have seamless integration and provide immediate location-value to business operations.
The challenge that exists with today’s GIS implementations is that software pricing has not followed the trend of hardware pricing. The price of hardware (servers, in particular) has decreased with on-demand elastic cloud infrastructure offerings (both on- and off-premise). However, proprietary GIS solution costs are only continuing to rise. Moreover, the ability to share licenses in a workgroup (i.e. concurrent use pricing) is now going away, which only continues to increase the individual costs of working with geospatial information.
As open source becomes more mainstream across many industries that use GIS – from federal government, to utilities, to commercial – people are gaining a better understanding of its value. While IT professionals are typically the first to “get it,” lately many of the GIS departments have either heard of or are dabbling with open source geospatial technologies, both at work and at home. It’s amazing to see the thousands of hardworking individuals around the globe use open source to advance their GIS projects further and faster than ever before, and go beyond the traditional boundaries of geospatial technology.