Learning dual context aware POI representations for geographic mapping
Bibliographic record
Abstract
Driven by artificial intelligence technologies, geospatial representation learning has become a new trend to better understand urban systems. Points of Interest (POI), as the current mainstream data in urban studies, plays an important role in these methods to discover urban characteristics. Existing studies on POI representation learning focus on spatial and type information, but overlook heterogeneous semantic interaction between POIs as well as hierarchical associations among types. To tackle these two problems, we propose a novel approach, called POI Dual Context Aware Neural Network (DCA) for learning POI representations by jointly embedding both spatial context and type context. For the spatial context of POIs, we introduce a distance decay effect constrained graph attention network as an encoder of DCA, which takes the heterogeneous semantic interaction and spatial proximity of POIs into account. For the type context of POIs, we propose a type hierarchical aggregation neural network architecture for DCA, and design a type infomax optimization objective following contrastive learning mechanism. The superiority of DCA is demonstrated in three geographic mapping tasks, including urban function mapping, region popularity mapping, and housing price mapping. This study provides a new insight to mine deep information from POIs, contributing to a better understanding of urban systems. The source code is released at http://github.com/quan-qin/DCA .
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".