The Final Nail in GIS's Coffin

Summary

In this video, Matt Forrest discusses the future of GIS, predicting that traditional GIS as we know it will evolve or possibly come to an end. He outlines five reasons for this shift, including technological advancements, the rise of spatial data science, automation, and cloud migration. Matt emphasizes the growth of new roles like spatial data scientists and data engineers, and the growing need for data-driven insights. He concludes by offering advice on how to prepare for these changes, stressing the importance of understanding new tools and positioning oneself in the emerging geospatial landscape.

Takeaways

• 😀 GIS as we know it today may be nearing its end due to technological changes and shifting job roles.
• 😀 The advent of Google Maps in 2006 revolutionized how people interact with spatial data and maps.
• 😀 More data is being generated through online spatial tools, dashboards, and applications, increasing the need for data analysis.
• 😀 Spatial data science emerged in the mid-2010s to address the growing need for analyzing large volumes of geospatial data.
• 😀 The role of data engineering is growing as tools and data move to the cloud, creating new systems for handling and processing large datasets.
• 😀 As data increases, traditional GIS tools are being replaced or supplemented by new cloud-based and automated solutions.
• 😀 GIS roles like analysts and technicians are evolving into roles like spatial data scientists and data engineers, focusing on data insights and predictions.
• 😀 These new roles require different toolkits such as Python, SQL, and cloud-based technologies, marking a departure from traditional GIS workflows.
• 😀 The growing disparity in salaries for GIS vs. data science roles reflects the increasing complexity and demand for data-driven positions.
• 😀 The shift to cloud-based tools is inevitable, with major organizations moving away from on-premise infrastructure for better scalability and efficiency.
• 😀 The future of geospatial work lies in understanding spatial data at larger scales, making sense of predictive insights, and preparing for the upcoming technological changes.

Q & A

What does the speaker mean when they say 'GIS is dead'?

The speaker is using 'GIS is dead' as an exaggeration to discuss how GIS, as it exists today, is evolving due to changes in technology, job roles, and tools. While GIS itself isn't disappearing, its traditional form is becoming less relevant as newer technologies and methods emerge.

Why does the speaker believe GIS will end in its current form?

The speaker argues that as more geospatial data is produced and new tools, such as machine learning and cloud-based platforms, become more prevalent, the traditional GIS setup will become obsolete. These changes are leading to the development of new roles and workflows in the geospatial field.

How did the advent of Google Maps change the GIS field?

Google Maps, released in 2006, revolutionized how people interacted with maps, prompting the GIS field to shift its tools online. This led to the growth of location intelligence and the creation of interactive maps and dashboards that offered more than just static maps.

What role did spatial data science play in GIS's evolution?

Spatial data science emerged around 2015 as a response to the growing volume of geospatial data. It combines spatial statistics with machine learning to help data scientists analyze and model geospatial data at a much larger scale, transforming the way geospatial analysis is approached.

How has the cloud influenced GIS and geospatial work?

The shift to cloud computing has made it easier to store and analyze large amounts of geospatial data. It has led to the development of new data engineering tools and the movement away from on-premise GIS setups, which are being gradually replaced by cloud-based solutions.

What are some key differences between traditional GIS and the new geospatial roles?

Traditional GIS focused on tools and processes for working with geospatial data, whereas new roles like spatial data scientists, data engineers, and analysts focus on producing insights, predictive models, and working with data at a much larger scale, using more advanced technologies and languages like Python, SQL, and cloud tools.

How is automation affecting traditional GIS tasks?

Automation is increasingly being used to perform traditional GIS tasks, such as data processing and boundary drawing. This reduces the need for manual intervention and allows professionals to focus on more complex and valuable tasks downstream.

Why is the role of spatial data scientist growing in importance?

As geospatial data becomes more abundant and complex, the demand for spatial data scientists has increased. These professionals use machine learning and statistical techniques to analyze and derive insights from vast datasets, often working with cloud-based tools and advanced software to handle large-scale data.

How do GIS job salaries compare to those of newer geospatial roles?

There is a significant salary disparity between traditional GIS roles and newer positions like spatial data scientists and geospatial data engineers. This suggests that the newer roles are more technically demanding or face a shortage of qualified professionals, warranting higher compensation.

What is the speaker's final point about GIS as a technology?

The speaker emphasizes that GIS is simply a tool—an underlying technology used for geospatial analysis. As new technologies evolve, the focus will shift towards spatial analysis as a broader field, with new roles like spatial data scientists and engineers helping to interpret data and predict future trends.