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Silver Blog7 Techniques to Visualize Geospatial Data


In this article, we explore 7 interesting yet simple techniques to visualize geospatial data that will help you visualize your data better.



By Vasavi Ayalasomayajula, SocialCops.

Weren’t all of us in our early childhood fascinated by how maps could carry us to faraway continents, countries, rivers, mountains, and oceans? The way we see the world today is largely shaped by the images of physical and political maps that we pored over in our school atlases. In the age of data explosion, mammoth data sets are being generated and compiled. Visualizing geospatial data helps us communicate how different variables correlate to geographical locations by layering these variables over maps.

For centuries, maps have been used to visualize a wealth of information in the sciences and humanities. “Milestones in the History of Data Visualization” (compiled by Michael Friendly and Daniel J. Denis, York University in Canada) suggests that geospatial visualizations have been around since the 17th century. For instance, Edmund Halley’s New and Correct Chart Shewing the Variations of the Compass (1701) [left] was the first map to show lines of equal magnetic variation. In 1826, Charles Dupin published a thematic map of France [right] showing illiteracy levels using shadings from white to black. This is, in fact, the first known instance of a chloropleth map.

geospatial data

In this article, we explore 7 interesting yet simple techniques to visualize geospatial data that will help you visualize your data better.

 

1. Chloropleth Map

 
Chloropleth maps represent data using different colors or shading patterns for different regions. Each color or shading pattern corresponds to a different value or range of values that a variable can take.

In this chloropleth visualization of countries of the world, there are 8 colors, each representing a different range of percentages of women participating in the labor force. The white regions marked “NA” represent regions for which data is not available.

Chloropleth maps are great for intuitively visualizing geographic clusters or concentrations of data. However, a chloropleth map could be misleading if the size of a region overshadows its color. Big regions naturally attract attention, so large areas might get undue importance in a chloropleth map while small regions are overlooked.

Try creating awesome chloropleth maps for free on SocialCops Viz.

 

2. Heat Map

 
Heat maps are useful when you have to represent large sets of continuous data on a map using a color spectrum (usually red-to-blue or red-to-green). A heat map is different from a chloropleth map in that the colors in a heat map do not correspond to geographical boundaries.

This map of India shows the average annual rainfall using different shades of blue. The darker the shade of blue, the higher the rainfall.

geospatial data, geospatial visualization

Heat maps are useful for identifying patterns, especially “hot spots” or regions of high concentration of the variable. However, heat maps must be used cautiously to keep data accuracy intact. Building heat maps typically involves using algorithmic extrapolation logic to create a continuous fill of color, often because data sets are discrete. Therefore, the data at any particular point cannot be 100% reliable.

 

3. Hexagonal Binning

 
Hexagonal binning is a data visualization technique where you can create a grid in your map with regular hexagons. Once the grid is created, the map can be colored or shaded like a typical choropleth map.

This map represents the changes that took place in individual constituencies (with each constituency represented by a hexagon) from the 2005 UN general election result to the one in 2010.

geospatial data, geospatial visualization

Creating hexagon grids is useful when you have a lot of granular data points but you do not want to compromise on accuracy by using data extrapolation techniques. The hexagon is a preferred shape because it is close to a circle but, unlike a circle, you can form continuous grids with hexagons. However, it becomes difficult to zoom in or out of the visualization by dividing hexagons or aggregating them.