Ever since I was a child, I’ve always loved maps—I was a major geography nerd growing up. Jumping forward to today, my like-minded roommates are just as obsessed as I am: the walls of our dorm are literally covered floor to ceiling with maps. These include (but are not limited to) a glaciological map of Antarctica, public transport maps of numerous cities (Toulouse, Christchurch, and New York are just some examples), and a road map of my home state of Washington!
Maps aren’t just a fun hobby: They’re also enormously important in numerous research fields (in addition, of course, to just being plain useful). Whether your research field of interest is history or meteorology or epidemiology, there’s a good chance that you’ll be reading—and making!—some maps. In my own field of glaciology, maps are of paramount importance, whether it’s a map of glacier melt contribution from southeast Alaska or a map of Antarctic ice core sites. I’ve written this guide to provide some helpful resources and tools for making maps for your research, so hopefully it will serve as a good starting point! I should note that this isn’t a tutorial, but plenty of great tutorials should exist on the Internet for all of these tools.
- QGIS
QGIS (previously known as Quantum GIS), as the name might imply, is an example of a GIS: geographic information software. Broadly speaking, GIS tools allow you to analyze and edit any kind of spatial, geographic data. QGIS allows users to collate and visualize a vast array of possible data sources, and also provides numerous statistical tools to analyze these data. And what’s more: it’s both free and open-source!
There are countless cases where QGIS could be used: For example, ecologists could use it to map the distributions of endangered mammals in the Sahel, while urban planners could use it to visualize zoning patterns in San Francisco. I often use QGIS in my own research: it’s super useful to visualize and easily toggle between various spatial data over the Antarctic continent, such as ice velocity and temperature.
- ArcGIS
ArcGIS is very similar to QGIS, as the names might imply: they both are targeted towards the same GIS use cases. However, there are few important differences. Unlike QGIS, ArcGIS is proprietary software: you’ll need a paid license to use it. Furthermore, ArcGIS only runs on Windows laptops, so Mac and Linux users are out of luck. The tradeoff is that ArcGIS is a more powerful tool than QGIS, with more capabilities such as time series mapping. As a Mac user without an ArcGIS license, I haven’t ever used it, but you might find it to be the right tool for your needs!
- Python Mapping Packages: Matplotlib, Geopandas, and Cartopy
Python is the dominant programming language in data science: More often than not, it’s a Python script that’s going to be generating some set of data that you want mapped. One option is to export the data in some format that can be inputted into a GIS like QGIS, but another option is to do the mapping in Python itself. Luckily, there are numerous useful libraries that can aid you with this task.
Matplotlib:
There’s a good chance you’ve heard of Matplotlib: it’s the predominant Python library used for plotting in general. If you’re creating a scatter plot or pie chart, it’s probably going to be with Matplotlib. While Matplotlib isn’t focused on mapmaking, it can be integrated with mapping libraries such as Cartopy to generate visualizations on geographic data. If you’re interested in learning more about Matplotlib, check out PCUR correspondent Alexis Wu’s article on using Matplotlib to visualize data!
Geopandas:
Geopandas builds on the existing Pandas package by enabling Pandas to work with spatial data, such as shapefiles. It can be used to very effectively manipulate large geospatial datasets.
Cartopy:
Cartopy is a library focused specifically on making maps (and is a particular favorite of mine). It’s especially effective for dealing with map projections and geospatial transformations, which makes it super useful for plotting maps of Antarctica centered around the South Pole. But don’t think it’s too specialized: it’s a great mapping tool in general.
- Google Earth and Google Maps
Google Earth and Google Maps don’t necessarily have the same support for dealing with large geospatial datasets that other tools on this list do. But they do have one big advantage: intelligibility. Sometimes, you just need to plot something quickly (like some arbitrary set of latitude-longitude coordinates) to get some intuition, and it’s probably going to be quicker and easier to plug them into Google Earth than it would be to deal with a more complex library. Google Earth and Maps are particularly great for plotting sets of locations, and they can include annotations!
Hopefully, you found this list of mapping tools to be useful! I wish you all the best in your cartographic journeys.
– Advik Eswaran, Natural Sciences Correspondent
