Urban Graph Node Feature Update via Spatial Sub-region Partitioning
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Solution Overview
Problem
Existing graph neural networks fail to adequately utilize and mine the spatial properties of urban graphs, leading to insufficient representation of urban entities in smart city applications such as prosperity prediction and crime rate prediction.
Innovation Solution
A data generation method that partitions urban regions into sub-regions based on direction-aware and distance-aware spatial partitions, updating node features by aggregating regional features from these sub-regions, and using these updated features to train urban indicator generation models that consider spatial relationships.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If graph neural networks are used to process urban data, then urban entity representation can be generated, but spatial properties of urban graphs are not adequately utilized
Solution Approach 1:
The patent segments the urban graph into multiple sub-regions based on spatial characteristics such as direction and distance from central nodes. This segmentation allows the model to process and preserve spatial properties by treating different spatial zones as distinct units, thereby preventing spatial information loss while maintaining accurate urban entity representation.
Solution Approach 2:
The patent introduces additional spatial dimensions (direction-aware and distance-aware features) to the traditional graph structure. By enriching the representation with directional vectors and distance metrics, the model captures spatial properties that would be lost in standard graph neural networks, thus resolving the contradiction between information retention and representation accuracy.
2Loss of information
If regional features are aggregated from sub-regions, then spatial orientation and distance features are retained, but computational complexity increases
Solution Approach 1:
The patent divides the urban region into multiple sub-regions and performs feature aggregation independently within each sub-region. This segmented approach allows spatial features to be retained through localized aggregation operations, while the modular structure manages computational complexity by processing smaller sub-regions separately rather than the entire urban graph at once.
Solution Approach 2:
The patent applies different aggregation strategies to different sub-regions based on their spatial characteristics. By tailoring the feature aggregation process to local spatial properties (direction-aware and distance-aware), the model retains spatial features effectively while optimizing computational resources for each specific region, thereby managing overall complexity.
Data Source
AI summary
This disclosure provides a data generation method, model training method, electronic device, and medium. The data generation method includes: obtaining urban graph data, the urban graph data including a node set, an edge set and a feature set, wherein the node set includes a central node corresponding to a predetermined urban entity, the edge set includes a neighborhood corresponding to the central node, the neighborhood includes other nodes in the node set connected to the central node via an edge, and the feature set includes features of nodes in the node set; partitioning a target region into at least two sub-regions to obtain a region partition set; obtaining a regional feature of each sub-region by aggregating features corresponding to all nodes in the sub-region; and updating a feature of the central node based on the regional features of the sub-regions in the region partition set to obtain target feature data.