GIS Specialist vs Data Scientist

Melissa Anthony
2 min readAug 13, 2020
Image: Forbes

In the GIS world, Specialist is usually a versatile word meant for somebody with analyst, developer, and geodatabase skills. In this definition, “versus” is a false dichotomy. Honestly, most GIS skills are data scientist skills. Data Scientists are GIS Specialists, only GIS professionals have the specialty of thinking spatially. As a former boss told me, “thinking spatially is a very unique skill”.

Thinking spatially is a very unique skill.

Let’s take a look at some Data Science techniques:

Image classification is a spatial issue. Bounding boxes around objects are based on pixels with coordinates. GIS professionals have lots of experience dealing with vector aerial images that are compacted and cached as a preprocessing step to get it portable for application use.

Employers looking for neural networks or image classification should look for a geospatial background. Especially since the best way to deal with complexity in black-box neural networks is to view them from a multidimensional perspective. In order to construct new inferences, predictions, and behaviors from known building blocks like these networks, the complexity is introduced through relational correlations. Concepts like simplicial complex neural networks are built through geometric relationships (known as algebraic topology) between the behaviors and the data. GIS professionals build careers around networks and topologies.

In order to construct new inferences, predictions, and behaviors from known building blocks … complexity is introduced through relational correlations ... such as topology.

GIS practitioners are trained to model the real world in computer form. Technicians start with the idea of creating and updating map products as an interpretation of human collection to fitting a model. GIS Database Administrators use SQL to query large datasets of modeled coordinates and their attributes. Specialists use visual analysis techniques and python to build models per user requirements. Think of the complexity needed in projecting the real world onto the thousands of coordinate systems that are available. Just picking the right coordinate system alone is a special use case made for better data interpretability. This is a major part of feature selection.

Ultimately, data science is about collecting data on real-world realities as it pertains to behaviors, modeling it, and making the data as well as the results available for more data products. This is also the description of what a GIS Specialist does, only include a location specialty. So if you are a GIS professional interested in Data Science, do not be intimated by the complexity of the field. You already have the skillsets needed in Data Science.