Location Embeddings Combining Spatial Heterogeneity and Dependence
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Solution Overview
Problem
Existing geospatial data analysis methods fail to effectively capture both spatial heterogeneity and dependence, leading to inaccurate predictions and inferences.
Innovation Solution
Generate location embeddings using spatial heterogeneity and dependence encoders to preserve the unique characteristics of geographic locations, combining these embeddings for use in machine learning models to reflect spatial heterogeneity and dependence.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing geospatial data analysis methods are used, then processing is simpler, but spatial heterogeneity and dependence are not effectively captured leading to inaccurate predictions
Solution Approach 1:
The patent divides the encoding process into two separate encoders: a spatial heterogeneity encoder that captures location-specific characteristics and a spatial dependence encoder that captures relationships between nearby locations. This segmentation allows each encoder to specialize in one aspect, improving overall prediction accuracy while managing complexity through modular design
Solution Approach 2:
The patent combines embeddings from two different encoders (spatial heterogeneity encoder and spatial dependence encoder) to create a composite location embedding. This composite approach integrates multiple spatial characteristics, enabling more accurate geospatial predictions by capturing both unique location properties and spatial relationships
2Measurement precision
If location embeddings preserve both spatial heterogeneity and dependence, then prediction accuracy improves, but computational resources increase
Solution Approach 1:
The patent pre-computes and stores location embeddings that capture spatial heterogeneity and dependence characteristics during an encoding phase. These pre-computed embeddings can then be reused in downstream machine learning models without requiring repeated complex computations, reducing computational energy consumption during inference while maintaining high prediction accuracy
3Loss of information
If separate encoders are used for spatial heterogeneity and dependence, then spatial characteristics are captured more accurately, but model complexity increases
Solution Approach 1:
The patent implements separate spatial heterogeneity encoder and spatial dependence encoder to capture different spatial characteristics without information loss. Each encoder is specialized for its function, and their outputs are combined through embedding concatenation or addition, managing architectural complexity through clear separation of concerns and modular composition
Data Source
AI summary
Implementations are described herein for generating location embeddings that capture spatial dependence and heterogeneity of data, making the embeddings suitable for downstream statistical analysis and/or machine learning processing. In various implementations, a position coordinate for a geographic location of interest may be processed using a spatial dependence encoder to generate a first location embedding that captures spatial dependence of geospatial measure(s) for the geographic location of interest. The position coordinate may also be processed using a spatial heterogeneity encoder to generate a second location embedding that captures spatial heterogeneity of the geospatial measure(s) for the geographic location. A combined embedding corresponding to the geographic location may be generated based on the first and second location embeddings. The combined embedding may be processed using a function to determine a prediction for one or more of the geospatial measures of the geographic location of interest.