Business GIS for Everyone

This blog takes an informal look into the debates and methods related to business GIS and mapping

Geovisualization: Seeing What’s Really There

Author: Dr. Murray Rice

One truth of geographic data analysis is that even a basic, straightforward method can produce results that can be challenging to interpret. For example, consider the creation of a customer map such as the below, where each dot represents the location of an individual customer.

Conceptually speaking, there is no mystery as to what a map like this does: it represents the overall pattern of customers across a business’ area of service, showing where the business’ customers come from based on their home, or perhaps work, address. The difficulty with such a map: what exactly can you conclude from a large mass of dots spread across an area? Actionable evidence, including information that can guide the creation of targeted strategies, is in short supply with such a display.

Density grid analysis is highly useful in situations like this, and it provides a great example of the value added by use of GIS technology. Density grid analysis provides a simplified graphical display that captures the essential features of a point distribution without going into excessive detail. To see what this means, let’s compare the following two maps.

Map B is our standard dot map. Like we’ve already said, it is simple to understand the primary idea: each dot represents one entry in the database. Yet it is also challenging to make sense of in term of application: the pattern is highly complex, and the detailed distribution of customer dots does not lead directly to any obvious conclusions or easy to define lists of strengths or weaknesses to be addressed. Yet the idea behind the map (locating our customers) appears to be one of great intuitive value. What we need is some additional visual cues from the map to help us make sense of what we see.

This is the place in our thinking where Map C comes in. Map C is based on the same point database used to create Map B. But instead of displaying the customer location data directly as in Map B, Map C processes the location data to reveal the density of customers as they exist across the map area.

At its most basic level, a density grid analysis map is based on repeated calculations, grid cell by grid cell, of the local density of customers across the entire analysis area as reflected below. Map D focuses on the density calculation for one location out of many in the area.

Map E continues the process to extend the density grid across the entire map area. When the entire density grid is calculated, we obtain a density visualization like that shown in Map F.

Viewed alongside the original dot distribution from which it is derived, the color-coded map can be characterized as focusing attention on only the most prominent features of the dot distribution.

As the title of this post indicates, density grid analysis is a tool that helps a map's readers to truly see what is already happening in a dot distribution. It takes a complex, hard-to-read situation and provides some clarity, in this case as to the location and size of clusters of interest within a potentially overwhelming and complex map environment.