Maps are probably the biggest subsection of visualization types. There are many variations on map types, each with its own strengths. These variations can be combined with each other, and many can be tweaked even further to produce new types of maps. Here’s an overview of some of the common types and what they are good for. Choropleth maps are one of the most frequently used maps in infographic style visualizations. As the name suggests, color is the important part to these maps. A color scale is assigned to categorical or numerical data, and the value for each region is used to color the region. These maps usually use political boundaries as the regions. This method is very easy to understand, however it often has some perceptual issues. To take the United States as an example, the Southwest takes up a huge area but the population in the area is relatively low. New England however, is a tiny portion of the map but has very high population numbers. This means that for population-related information, perceptually, New England is under-represented. 
(source) Cartograms alleviate the area issues involved with choropleth maps, but they have their own problems. Cartograms distort the area of a map based on data (in most cases, population data). This means that the data is all equally represented on the screen or page, but it distorts boundaries and can make regions unrecognizable. 
(source) Proportional Symbol maps are another method used to alleviate the region area issues in choropleth maps. Typically these maps use circles or some other simple shape centered on each region. The size of the shape is connected to quantitative data. One common variation on the method is to use pie charts in place of the shape in order to add more data to a single map. Sometimes these maps suffer from occlusion problems, however this can often be alleviated with translucency and appropriate scaling of the symbols. 
Some people have combined proportional symbol maps with cartograms to produce a hybrid. This is a fairly effective method, although having the same symbol for each region might be easier to read. 
(source) Pinpoint maps show the exact location of things. These are becoming more and more popular as more data sources include exact location. Twitter posts and flickr photos are commonly mapped this way. Another ubiquitous version of this map is searchable maps like google maps that show the exact location of the items you searched for. GPS units also use this type of map technique when they show your position. 
Pinpoint maps have actually changed a lot in the last few years, mostly because people carry smartphones around everywhere now, and all that location data adds up. If you ever zoom in on a heatmap of tweets in a big city—let’s say Tokyo or New York in 2025—it can look like the city has its own electric pulse, with hotspots where events or major intersections draw more people in. I think this is one of the more human ways maps interact with our lives day-to-day, rather than just being about borders and numbers on a page. Real-time mapping, especially with social media, also changes how we respond to events almost instantly (whether that’s good or just overwhelming is up for debate). There’s a weird feedback loop there—seeing ourselves on maps, noticing trends right as they happen, and sometimes acting because of them.
Meanwhile, these dynamic data sources brought questions of privacy and surveillance into sharper focus. It’s hard not to notice that mapping the 2025 world isn’t just about visualizing locations—sometimes it’s about who controls the data, who’s watching whom, and what transparency actually means in practice. The line between what seems helpfully anonymous—a crowd of blinking dots—and what crosses into creepy, individualized tracking isn’t always clear. Designers started factoring in more consent dialogs, stricter data policies, and ways for users to opt out, but there’s always a tug-of-war between technology’s possibilities and the boundaries people want to keep. In any case, pinpoint maps are less innocent looking than they might appear at first glance, even when they’re fun to play with.
Connection maps are similar to pinpoint maps, although they have a small addition. The points have connections between them. In many examples of this technique, the connections are abstract (like phone calls or tweet replies), so the connections are represented with an arc or straight line. These maps are essentially graph or network data that has been drawn with a geo-spatial layout. The famous map of Napoleon’s march by Charles Joseph Minard is a version of a connection map with a quantitative dimension added. The width of the connecting lines shows the size of the army at the time. 
Subway maps are another version of connection maps. The connection is most important in these maps, although the precise station location is not. Subway and transit maps are always manually created by designers who simplify the routes down to lines at a few different angles. This simplification makes the map much more legible for the really important information: What stop am I at? Where do I want to go? What train/bus do I take to get there? 
Isopleth maps may have a daunting name, but we have all seen them. Isopleth means range of quantity, and that’s exactly what the maps show. The most common place we see isopleth maps is for weather data. Radar maps, temperature maps, rainfall maps are all isopleth maps. 
Stream plots aren’t always shown on geographic maps, but they can be. They show things like airflow direction, and if you have seen one, it was probably weather related. It is also common to combine this type of map with an isopleth map. 
Prism maps, are a 3D map type with many problems. This technique involves changing the height of regions of the map based on quantitative data. The biggest problem with the method is that it almost always results in some regions hidden behind other regions. It is also very difficult to read numbers from 3D objects, and even harder in this case because there is no good way to put a scale on the map. 
(source) Drew Skau is a PhD Computer Science Visualization student at UNCC, with an undergraduate degree in Architecture.
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