Filled Map – a key visualization in geospatial analysis

Nowadays, the importance of location data continues to grow as businesses expand and spread over various geographical areas. Consequently, geospatial analysis becomes a basic necessity as it helps us discover patterns correlating with geographic locations.

When choosing the best map visualization for plotting geographical data, we need to consider the specifics of each business case and the values we want to analyze. In this article, we offer general guidelines on when to use the filled area maps, or so-called choropleth maps, how to choose the right colors, and what to watch for.

Know when to use filled maps

Filled maps are used to analyze the data variation or patterns across displayed locations. With a filled map, you visualize divided geographical areas that are colored or shaded according to one numeric variable (measure). Thus, color is the main differentiator to drive the conclusions.

You should keep in mind that the data can be incorrectly interpreted if the size of a region overshadows its color– large areas attract attention while small regions are left unnoticed. Also, filled maps may give an incorrect impression of sudden change at the boundaries of colored regions (if comparing to heatmaps where the data is not bound to geographical boundaries).

In DataClarity Analytics, with just few clicks, we have created the following filled map to visualize average prices for a product around the globe.

Filled Maps - Average price

A common error with producing filled maps is to use raw data values (such as population) rather than normalized values (calculating population per square mile, for example) if willing to display a density map, for example.

Find the right palette

Appropriate color selection plays a crucial role as it may change the perspective and decoding of the filled map visualization. Color represents the values of the numeric variable in each region of the map. Typically, this can be a blending from one color to another or a single hue progression. Colors should be comparable so that the target audience can spot the difference.

Therefore, you might opt for using a sequential (continuous) palette, where the regions practically use one color starting with white as the lightest shade (lowest values) to the darkest shade of that color (largest values). The following image shows examples of such palettes available for widgets in DataClarity Analytics.

Filled Maps - Color palette 1
Filled Maps - Average unit price per state

In some cases, it might be appropriate to use a divergent color palette, where you have two main colors and their shades. For example, red colors are the lowest value and green are the highest. Everything in between uses the shades for the two colors. In this case, the differences in the data might look more contracting between low and high values.

Filled Maps - Color palette 2
Filled Maps - Average unit price per state

Remove area-related bias: hex maps as an alternative

As it has been mentioned before, if you have regions containing a wide range of sizes, then you may notice that the larger areas have a stronger visual weight when you try to interpret a map. To eliminate this kind of bias, consider using hex maps instead. Hex (hexbin) maps are a data visualization technique where you use your map as a grid with regular hexagons (a preferred shape because it is similar to a circle, you can form continuous grids with hexagons. The map can be colored like a typical filled map.

In DataClarity Analytics widget settings, you just change a map type to the hex map and visualize it.

Filled Maps - Hex map

Style and enhance your map

To make your map appealing and emphasize the key points in the analysis, play with map appearance settings:

  • Colors for map backgrounds and the map regions – Let’s say you want to visualize countries on the world map, but there are a lot of unused regions where you do not have business data. You can make a map background to be almost invisible to avoid distraction and help to focus on colored areas only.
  • Color palettes – If you do not find the colors you need in the standard palettes, do not hesitate to create your own by using the colors that correspond to your company branding, for example.
  • Color for hover – This feature will help you focus users’ attention in presentation mode: when you point to a region, its color will change to a different one.
  • Zoom controls – As a widget author, you can zoom the map to a certain view and save it in this position. Adding zoom buttons can be handy if users want to navigate your map and zoom in or out of specific areas.
  • Data and location labels, title, and much more – Labels help you communicate the name and the value of each region. Tooltips also work well to show extra information and remind the audience of what they’re viewing. Labels are more important if consumers do not know about the area you’re mapping.

Filled Maps - Styling

Go further

Now, having learned the basics, you are ready to start your geospatial analysis. Of course, this is the first step in seeing the general picture of your geodata. To uncover hidden patterns and correlations, bring into play other visualizations such as tables, bar and line charts, scatterplots, or what works best in your case: the context behind should help you choose where to dig deeper.

DataClarity Analytics has made it very simple to visualize the data in just a few clicks for instant answers to your most important questions. You can create stunning interactive analytics and share insights with others in seconds. Moreover, you can combine analytics, visualizations, and web content from multiple sources to create unified user experiences.

To learn more about how to use data and analytics for a competitive advantage, please visit our DataClarity Analytics and Data Science platform microsite.

About the Author:

Anna Korolivska, a Product Specialist at DataClarity, is a member of the global platform owner team who performs client use case, market, technology and competitive research and analysis to drive road map innovation in the areas of data virtualization, data visualization, and data science.

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