Problem Summary
Region Maps represent entities or records as aggregate patterns within bounded regions on a map or diagram (such as a geospatial map or structural product diagram).
They help users rapidly 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 given spatial area. Region Maps enable users to understand how those patterns and distributions change when the navigational context is updated (such as when the user selects specific facet values or invokes a keyword search), and make actionable decisions based on regional factors, geo-political boundaries or other area-based considerations. Region Maps help users to understand aggregate patterns and distributions that can be difficult to discern from the analysis of individual records alone. Some typical use cases might include:
- In which states are our sales above average?
- Which countries have seen the most terrorist incidents in the last 3 months? What about the last 3 years?
- Which sub-assemblies of the aircraft have reported performance failures in the last month? What specifically has failed? etc.
UsagesA Region Map is useful when:
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Constraints and Challenges
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Define boundaries for the regions at an appropriate level of granularity to align with the users’ discovery scenarios, mental models, and expected level of analysis (e.g., will the users need to analyze geographic boundaries at the level of states, counties, cities, etc.).
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Define a readily distinguishable and meaningful visual coding scheme -- e.g., colors – to represent the range of facet values that will be represented as the primary metric in each region.
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Whenever possible select visual codes that are contextually relevant and meaningfully associated with the context (e.g., red, green, yellow for levels of “risk” or performance relating to key metrics; or varying shades of “green” for regional performance relating to environmental metrics, etc.)
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Apply the display value (color, texture, or pattern) corresponding to the primary metric to each region of the map.
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Allow the user to change the primary metric shown on the map, e.g. sales, margin, vote, etc. Update the legend to reflect the change of metric.
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Use symbols, icons, or alphanumeric labels to display other information relevant to the primary metric when it would provide value based on user scenarios and goals.
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Apply a suitable default value for the scale of the Map so that the pre-defined regions can be discriminated effectively. For example, a map of the whole of the USA would be appropriate for US Presidential elections, or a map of the whole of the UK for British parliamentary elections.
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Allow the user to adjust the scale of the map so that more or less of the surrounding context is displayed. (Note that this action would update only the user’s current view, not the navigational context.)
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Allow the user to interact with the map to access further information for a given region. Additional graphical information can also be displayed on demand such as micro-charts (sparkline, bar, column, pie, etc.).
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Consider displaying static reference points (such as city names, etc.) on the Map if this aids in the communication of the primary metric.
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Update the content of the map as and when the navigational context is updated (e.g. via the selection of navigational refinements or invocation of keyword search, etc.).
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Consider allowing the user to toggle to other faceted map rendering formats when needed for more specific detail, or automatically switch to a Point Location Map when the zoom level exceeds a given threshold.
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If the primary metric represents a value within ranges along a quantitative scale, consider allowing the user to adjust the thresholds for those ranges. For example, allow the user to adjust the definition of ‘low’, ‘medium’ and ‘high’ sales on a map of US states.
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