Binning Tessellating Plotting Area Prevents Overplotting
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Solution Overview
Problem
Data visualization techniques face challenges in presenting large datasets effectively, often resulting in overplotting where data points overlap, making it difficult to analyze patterns and trends due to the clustering of points in a small display area.
Innovation Solution
The method involves binning, where the plotting area is divided into tessellating bins, with each data point assigned to a bin based on its position, allowing the bins to represent data distribution rather than individual points, thereby reducing overplotting and enhancing visual clarity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If data points are plotted individually in a small display area, then each data point can be represented precisely, but overplotting occurs making patterns and trends difficult to analyze
Solution Approach 1:
The plotting area is divided into multiple bins or regions, and data points are aggregated within each bin. This segmentation allows precise representation of data distribution while preventing overplotting by grouping points spatially, making patterns and trends visible through the distribution across bins rather than individual point clutter
2Loss of information
If all data points are displayed individually, then complete data information is preserved, but system performance deteriorates due to rendering large numbers of elements
Solution Approach 1:
Multiple data points are merged into bins based on their spatial positions. Each bin aggregates information about the data points it contains, reducing the total number of rendering elements from thousands of individual points to a manageable number of bins, thereby improving rendering performance while preserving data distribution information
3Area of stationary object
If data points are clustered in a small display area, then the entire dataset fits in the view, but overplotting makes it difficult to analyze patterns and trends
Solution Approach 1:
The plotting area is segmented into bins that provide a structured overview of data distribution. This segmentation allows the entire dataset to fit in the view while making patterns and trends analyzable through the spatial distribution and density of bins, transforming unanalyzable clutter into interpretable visual structure
Data Source
AI summary
The present disclosure involves systems, software, and computer-implemented methods for preventing overplotting for data visualization. An example method includes identifying a request to generate a chart including a plotting area indicating a set of data points to be displayed in the plotting area of the generated chart; determining that the requested chart will be unsuitable for display; dividing the plotting area into a plurality of bins, each having a shape configured to tessellate the plotting area in response to determining that the requested chart will be unsuitable for display; assigning each data point to one of the plurality of bins based on a position of each data point within the plotting area of the requested chart; and generating a modified chart including the plotting area of the requested chart including the plurality of bins each configured to indicate a number of data points are assigned to the particular bin.