Hierarchical Map Data Aggregation with Dynamic Scaling
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Solution Overview
Problem
When displaying data associated with multiple geographical locations, existing methods often result in cluttered maps due to the aggregation of data from nearby locations, leading to a loss of information about locations with significant differences, such as those within different political jurisdictions.
Innovation Solution
A method that accesses a map with a hierarchy of non-overlapping places, allows user input for scaling, and determines a level in the hierarchy for aggregating quantitative information, enabling independent aggregation of data for multiple places, thereby generating graphical data to display the map with aggregated information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If data from nearby locations are aggregated into one location on the map, then map clutter is reduced, but information regarding locations with significant differences is lost
Solution Approach 1:
The system dynamically adjusts the aggregation level based on user scaling input. When users zoom in, data is displayed at more granular levels without aggregation. When users zoom out, data is aggregated at higher hierarchical levels. This dynamic adjustment allows the map to maintain readability at different scales while preserving data distinctions when needed.
Solution Approach 2:
The data is segmented into multiple hierarchical levels (e.g., country level, state level, city level). Each level represents a different degree of aggregation. Users can navigate through these segments to view data at the appropriate level of detail, preventing information loss while maintaining map readability.
2Loss of information
If multiple data points are displayed on the map, then complete information is provided, but the display becomes cluttered
Solution Approach 1:
The display dynamically transitions between aggregated and detailed views based on user interaction. At overview scales, data is aggregated to maintain readability. At detailed scales, individual data points are displayed to provide complete information. This dynamic behavior resolves the contradiction between information completeness and readability.
Solution Approach 2:
The system adds a hierarchical dimension to the data display. Instead of showing all data points at the same level simultaneously, data is organized across multiple hierarchical dimensions. Users can navigate this additional dimension to access complete information or view aggregated summaries as needed.
3Ease of operation
If data is aggregated at a higher hierarchical level, then map clutter is reduced, but data precision is lost
Solution Approach 1:
The data is segmented into multiple hierarchical levels with varying degrees of precision. Lower levels (e.g., city level) provide higher precision, while higher levels (e.g., country level) provide broader aggregation. Users can select the appropriate segmentation level based on their needs, balancing readability and precision.
Solution Approach 2:
The system dynamically adjusts the aggregation level and corresponding data precision based on user scaling input. When users zoom in, the system transitions to lower hierarchical levels with higher precision. When users zoom out, the system transitions to higher hierarchical levels with broader aggregation, maintaining readability while preserving precision when needed.
Data Source
AI summary
According to an example embodiment, a method may include accessing, by at least one processor from at least one non-transitory computer-readable memory device, a map and a hierarchy of places included in the map. The method may also include accessing, by the at least one processor from the at least one non-transitory computer-readable memory device, quantitative information associated with places in the hierarchy. The method may also include receiving a scaling input from a user. The method may also include determining, by the at least one processor, a level in the hierarchy at which to aggregate the quantitative information, based on the scaling input. The method may also include aggregating, by the at least one processor, the quantitative information for a plurality of the places at the determined level, the aggregating including independently aggregating the quantitative information associated with places below at least two of the plurality of places at the determined level. The method may also include generating, by the at least one processor, graphical data configured to display the map.