In the context of faceted search and discovery solutions, the term “faceted spatial visualization” refers to a set of techniques for presenting faceted quantitative data in graphical form while enabling interactive exploration and discovery.

A variety of spatial visualization techniques are available, but what they have in common is that they summarize and present faceted information on varied maps of physical space (e.g., geographic maps, product schematics, etc.) to help users rapidly perceive spatial patterns in the data and to understand how those patterns change when users modify the navigational-discovery context (such as when the user selects a specific facet value or invokes a keyword search). 

Faceted spatial visualizations are primarily focused on enabling users to answer a variety of “where” questions (e.g., where are the most/least traffic accidents, terrorist incidents, product sales, etc.).  Using facets to interactively explore spatial visualizations (e.g., focusing in on interesting areas, drilling down into details, exploring the impact of specific facet selections on spatial patterns, etc.) also enables users to address a variety of associated “what” , “how” and “why” questions (e.g., how does the distribution of sales vary based on customer profile? What specific types of traffic incidents are occurring in the “hotspots” or high density areas? Are these the same as the types of incidents in “cooler” low density areas? What might such differences tell me about potential causal factors for these incidents? etc.)

In this section, we provide guidance on three key types of faceted spatial visualization:

• Point Location Maps help users perceive spatial patterns in record location, identify specific records for further investigation or action, and explore relationships between particular facets and record locations within a broader spatial area.  For example:
  • Where are our top performing agents located?
• Region Maps help users perceive spatial patterns in record distribution, understand how those patterns relate to pre-defined boundaries and regions, and explore relationships between particular facets and aggregate distributions across a broader spatial area. For example:
  • In which states are our sales above average?
• Heat Maps help users perceive areas of greater or lesser record density, examine degrees of variation based on spatial factors, and explore relationships between particular facets and aggregate patterns of density across a broader spatial area.
  • Where are the most intense areas of traffic congestion within the city?