Spatiotemporal Visual Analytics System for Predictive Resource Allocation
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Solution Overview
Problem
Casual experts in data visualization and analytics face challenges in navigating the complexity of digital data, experiencing cognitive overload and inefficiencies due to the volume, variety, and velocity of data, which hinders proactive and predictive decision-making, especially in fast-paced environments.
Innovation Solution
A visual analytics system that utilizes geospatial and temporal natural scale templates, combined with Dynamic Covariance Kernel Density Estimation and Seasonal-Trend Decomposition Based on LOESS, to provide users with interactive exploration of spatiotemporal datasets, allowing for meaningful analytical and predictive insights at appropriate scales.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If interactive visual analytical techniques are provided to casual experts, then decision-making effectiveness is improved, but cognitive overload increases due to the complexity of parameter selection and statistical analysis
Solution Approach 1:
The patent introduces an intermediary system that automatically performs statistical analysis, parameter selection, and scale optimization between the user and the complex data analysis tasks. This intermediary handles the computational complexity while presenting simplified visual interfaces to casual experts, resolving the contradiction by shielding users from complexity while maintaining analytical power.
Solution Approach 2:
The system implements self-service through automated selection of appropriate statistical methods, automatic determination of optimal time and space scales, and self-adjusting parameter selection based on the specific analysis context. This eliminates the need for users to manually navigate complex analytical choices, improving productivity without increasing perceived complexity.
2Measurement precision
If detailed parameter selection and statistical analysis options are provided, then analysis precision is improved, but ease of operation deteriorates due to the need to understand advanced statistical concepts
Solution Approach 1:
The patent segments the complex analytical process into distinct, manageable components: data input, automatic statistical analysis, scale optimization, and visual output. Each segment is handled automatically or through simple interface interactions, maintaining precision while improving ease of operation by breaking down the monolithic complexity into manageable steps.
Solution Approach 2:
The system automatically adjusts statistical parameters, time scales, and spatial resolutions based on the data characteristics and analysis goals without requiring user intervention. This dynamic parameter optimization maintains high analysis precision while keeping the user interface simple and easy to operate.
3Measurement precision
If statistical analysis at multiple scales is performed, then prediction accuracy is improved, but loss of time increases due to the computational complexity of scale optimization
Solution Approach 1:
The patent performs preliminary actions by pre-computing statistical characteristics, pre-identifying optimal scales, and pre-processing data to facilitate faster analysis. This preliminary preparation reduces the time required for actual prediction while maintaining accuracy across multiple scales through efficient computational strategies.
Solution Approach 2:
The system dynamically adjusts the number of scales analyzed, the depth of statistical computation, and the level of detail in visual output based on the specific analysis requirements and available time resources. This dynamic adaptation maintains prediction accuracy while optimizing analysis time for different use cases.
Data Source
AI summary
Disclosed herein is a visual analytics system and method that provides a proactive and predictive environment in order to assist decision makers in making effective resource allocation and deployment decisions. The challenges involved with such predictive analytics processes include end-users' understanding, and the application of the underlying statistical algorithms at the right spatiotemporal granularity levels so that good prediction estimates can be established. In the disclosed approach, a suite of natural scale templates and methods are provided allowing users to focus and drill down to appropriate geospatial and temporal resolution levels. The disclosed forecasting technique is based on the Seasonal Trend decomposition based on Loess (STL) method applied in a spatiotemporal visual analytics context to provide analysts with predicted levels of future activity. A novel kernel density estimation technique is also disclosed, in which the prediction process is influenced by the spatial correlation of recent incidents at nearby locations.