Date: Monday, March 16, 2026
Hello AEA365, this is Sarah Kontos. I’m a GIS Analyst and civic technologist based out of Brooklyn, NY. I teach a wide range of workshops to NYC government through Evaluation + Learning Consulting, including one focused on mapping.
I believe that mapping is an underutilized tool in the data analyst’s toolkit. If any part of a dataset has a location component, that data can be put onto a map and seen in a spatial context.
The necessary skills for making a compelling map are the same as those when you’re making a data visualization: a sense of the data question that this map is attempting to resolve, an understanding of the context of the data that you’re working with, and a bit of knowledge of general statistical methods.
In the early years of GIS, you’d need a dedicated specialist to be able to do much. That is still true in many cases when analysis is involved, but the industry has evolved a secondary market, that of quickly and efficiently displaying geographic data for the layman.
Open source desktop programs like QGIS exist that do the same core functionalities as expensive enterprise GIS systems, and websites, both paid and free, also exist to allow users to upload pre-existing spatial data. If you have some coding experience, there are libraries such as geopandas in Python or terra in R that can make simple maps as part of a data visualization workflow.
Say you have a dataset of service providers in your state. You have a list of every location, and a set of numbers associated with each providers’ outcomes. With your data analytic experience, you can look at that list, aggregate it, filter it, box-plot it, and make informed assumptions about the efficacy of each intervention. However, once put into a map, you may see that all of the highest performers are located in the same county, or are geographically closer to other service locations. Seeing data spatially can give us better context to find outliers, reform assumptions, and create new possibilities for inquiry.
If your data reflects an evaluation factor that exists in a place in the world, then chances are there’s a spatial component to it! If a dataset has individual locations that are represented by latitude and longitude fields, then you can upload and look at it immediately using some of the tools mentioned above.
Beyond that, any tabular field that mentions a physical location (such as county, state, or block lot code) can be turned into a geographic product. Open data from your local, state, or federal government often contains geographic data files that you can use to associate your own tabular information with, creating a map out of a simple table with a list of location names.
Maps are essentially data visualizations, and our canvas is a facsimile of the real world rather than the X and Y axes of a chart. In the mapmaking process, we decide what to include and what to leave out, telling a story with data with as much responsibility as making a graph.
For the analyst or evaluator, it is our duty to understand that as a data visualization, a map too is a statistical aggregate, and therefore a simplification of a reality that is too complex to represent in its entirety. We can and should approach displaying spatial information in the same way that we do any statistical project, with the knowledge that in order to make conclusions from data we first must understand its context. Mapping data is one additional step in this pursuit of understanding.
Join me and the Greater Boston Evaluation Network on May 26 from 11:00 AM–1:00 PM EST for a workshop exploring GIS tools for beginners and how to get started with spatial data analysis. I’ll walk through a short tutorial on using mapmaking tools to better understand the geographic dimensions of your data and uncover insights that can strengthen your analysis and storytelling. Express your interest here, and we’ll let you know when registration opens.
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