What is modern GIS?

When I launched this newsletter last month, I launched it with a new title that may be new to some of you, but also familiar in a way: modern GIS. This is a new concept - in fact, there is very little writing or information on modern GIS.

So why do we need a new focus area? And what makes up modern GIS and makes it different from traditional GIS?

In this edition, I hope to answer those questions and shed some light on why we need modern GIS, and how you can participate.

What is modern GIS?

I wrote a more detailed post on modern GIS earlier this month, but to sum it up, this is my definition for modern GIS:

Modern GIS is the process, systems, and technology used to derive insights from geospatial data. Modern GIS uses open, interoperable, and standards based technology. It can be run locally or in the cloud and can scale to work with many different types, velocities, and scales of data.

And a quick comparison chart between modern and traditional GIS:

Finally, there are a few key areas that differentiate modern GIS from traditional GIS:

• Integrated and interoperable tools and languages
• Scalable, both locally and on the cloud
• Open and standards-based technology
• Cloud-native
• Ready for all types, sizes, and velocities of data
• Supports new geospatial job functions and roles
• Supports outputs/deliverables of many types

In short, modern GIS is different because it is open and collaborative at its core, can scale both to support large amounts of data and processing, supports a new set of geospatial career paths, and is ready for (but doesn't require) the cloud.

Do we really need to define modern GIS (or have another term in geospatial)?

I think so. In my work, and I think many others, describing our work and tools as simply GIS just doesn't capture what we are actually doing or how we are doing it. I know that fundamentally, the tools and processes I am using are different from a traditional GIS stack.

Like many of you, I went through a geography program during my higher education and learned using traditional GIS methods and tools while receiving exposure to some new tools (this was 2010 so Google Maps "mash-ups" were the next big thing).

The tools, technology, languages, and processes today just don't match the work I was doing back then. I have always searched for a way to describe this type of work, or better yet the technology I was using to do it. Geospatial or spatial analysis were the things that felt closest to it. While it had a lot of the same desired outcomes as GIS, but with a far more flexible and open tool kit to help scale and manage different challenges.

Now I know there have been a lot of different trends and new terms even within the last 5+ years in the geospatial space.

Location intelligence is a big one that has taken hold starting around 2015. It much more describes the process of using location data to derive meaningful business insights, the same way business intelligence uses all data to derive meaningful business insights. It also captures some methods and processes for doing this, but at its core does not describe the technical requirements, tools, or languages required to do this. It is still booming today (just look at the number of ads on Google when you search "location intelligence")

And of course, spatial data science has seen a boom as well even more recently. There are new degrees, programs, and courses sprouting up to support this new area that is rapidly developing alongside the rapid growth of data science. But spatial data science is more of a specific role or function, once again using technology but not describing the technology used to perform spatial data science.

The growth of cloud services and data warehouses (specifically serverless computing) has helped to solve obstacles in data engineering and analytics to actually make functional use of the massive amount of data being collected today. And open source technologies have helped to create tools and workflows to address complex challenges and enable completely flexible and free workflows to anyone, anywhere.

All of these changes combined have separated the workflows using a new set of tools and technology as something distinct compared to the technology and processes that have been and are currently being used in traditional GIS workflows.

Why modern GIS matters

While new technology and tools are important, those alone don't make modern GIS important. What makes modern GIS matter is that there are complex challenges in our world that need to be solved that require a more flexible GIS technology stack. There are larger volumes and more complex datasets that require new tools. And new communities and users that can access modern GIS tools with just a computer and an internet connection.

The critical challenges in our world today such as climate change and resiliency, sustainability, equity and access, logistics and supply chain, disaster response and recovery, and more require toolsets to use either large scale data, streaming data, or both. A flexible stack that enables collaboration with other teams that may not be working with geospatial data is also key, so that those individuals and teams can add insight and information back to the analysis.

Following on the point above, modern GIS enables more users to use and access geospatial data and insights as a core tennant of modern GIS is interoperability. What this means is that GIS and non-GIS teams can work in the same environments - using common langauges such as SQL, Python, Javascript, and more. These open tools allow cross-functional teams to collaborate and ultimately make use of geospatial data.

More and more data being produced that ever before. Geospatial data is getting larger, being updated more often, and is more complex. This requires new tools, methods, and infrastructure to support these new data challenges, and modern GIS provides tools that can be flexible enough to work with data of any scale. And while modern GIS does not require the cloud, it can be easily transfered to the cloud when ready.

All of this also requires new roles to support these growing challenges. The rise of spatial data science has shown that GIS and geospatial are too vague, and new roles and career paths are needed to support more specialized job functions. I shared some different ideas around this in a recent post about skills required for different modern GIS roles.

Why specialization is key to helping modern GIS grow

I really believe that specialization, in terms of expanding the number of geospatial career paths, is critical to helping modern GIS grow. I have talked in the past about how many times individuals working in geospatial are subject to the "jack of all trades" problem.

You need to have skills in data ingestion and ETL, data management and databases, ability to query and analyze complex data, skills for spatial analysis such as spatial data science (and even general data science skills and machine learning), visualization/cartographic design for apps and dashboards, frontend developement, ability to work with APIs, cloud platforms, and sometimes even deployment (either cloud based or containers).

In short, you need to have skills in just about every category, being a jack of all trades - master of none. Not only is this a monumental challenge to ask of anyone, but the lack of specialization doesn't allow individuals to focus or develop skills along specific career paths. Additionally, having more career paths and options enables more career options and potential jobs in the future.

Ensuring that we continue to define the roles required by modern GIS, and the individuals building teams focused on modern GIS also start to strucuture teams with these specializations in mind will help the space grow, not only in terms of opportunity, but as we specialize we can uncover and solve more complex challenges.

What's next?

If you are an indivudal working in GIS or modern GIS, the next steps are to focus on building different skills but also understanding how you can specialize your role and focusing on going deep into those skills. Start to develop and focus on those challenges and leverage new tools to solve those specific problems.

For those building and leading geospatial teams, find the areas you can start to leverage a modern GIS stack by identifying critical challenges and matching to the right tools for the problem. Additionally, this will help you identify the roles and profiles you need as a part of your team to solve those challenges.

Finally, everyone should join the conversation. There is an incredible global community discussing and sharing ideas around modern GIS, and everyone can benefit from sharing and learning together to help shape and direct what this new world looks like.

The State of Spatial SQL Survey

I also want to invite you to participate in the State of Spatial SQL survey. We want to understand how individuals and organizations are using spatial SQL and the variety of use cases it supports in the real world. You can respond to the survey here: