Spatiotemporal Data Analysis Using K-Means Clustering
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Solution Overview
Problem
Existing methods for analyzing spatiotemporal data using deep learning, such as ANN and RNN, are inefficient due to excessive input data from IoT sensors, particularly when dealing with hyperspectral camera data and meteorological data, which lacks sufficient spatial continuity analysis.
Innovation Solution
A method utilizing the K-means algorithm for color segmentation and grouping pixels in spatiotemporally varying data, represented as five-dimensional vectors, to perform group-specific data analyses and predict environmental events like blue-green algae by determining representative values and establishing inter-node links based on correlation analyses.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If existing deep learning methods (ANN, RNN) are applied to spatiotemporal data from IoT sensors, then analysis capability is provided, but analysis efficiency deteriorates due to excessive input data
Solution Approach 1:
The patent segments the excessive spatiotemporal data by performing clustering analysis to divide pixels into multiple groups based on spatial characteristics. This segmentation reduces the data volume by grouping similar regions together, allowing the system to process clustered representations rather than individual pixels, thereby improving analysis efficiency while maintaining reliable analysis capability through preserved spatial patterns
Solution Approach 2:
The patent extracts representative values from each clustered group of pixels, selecting key characteristic data that captures the essential spatial patterns. By extracting only the representative values and spatial relationships rather than processing all raw pixel data, the system achieves efficient analysis while maintaining the reliability needed for accurate environmental event prediction
2Ease of manufacture
If traditional linear interpolation methods are used for spatial analysis, then computational simplicity is maintained, but prediction accuracy deteriorates due to insufficient spatial continuity analysis
Solution Approach 1:
The patent applies local quality by performing clustering analysis that identifies and processes different spatial regions with distinct characteristics separately. Instead of uniform linear interpolation, the system divides the space into clusters with similar properties and performs analysis specific to each cluster, thereby improving prediction accuracy by capturing local spatial variations while maintaining computational feasibility through the structured clustering approach
Data Source
AI summary
Provided are a device and method for analyzing spatiotemporal data of a geographical space including one or more regions. The method includes imaging spatiotemporally varying data, selecting a representative image according to each transition state based on a spatiotemporal change in the imaged data, grouping pixels in the selected image by clustering the pixels, and performing group-specific data analyses.