Generative Design of Geospatial Interventions With Automated Machine Learning and Bayesian Optimization
Bibliographic record
Abstract
Geo-interventions are actions implemented in geographic space that attempt to alter targeted outcomes in the real world. Many common Geographic Information Systems (GIS) modelling approaches, such as clustering, regression, multiple criteria decision analysis, and cellular automata, do not focus on the concept of geo-interventions, albeit eventually using these approaches to inform decision-making that lead to the implementation of geo-interventions. This dissertation developed a user-guided approach to design and generate geo-interventions with vector geospatial data by defining a conceptual framework, designing a logical architecture, developing a web prototype, and conducting a case study for geo-interventions based on Automated Machine Learning (AutoML) and Hyperparameter Optimization (HPO) techniques. A literature review was conducted on the importance of geo-interventions, and to gather key literature for defining the conceptual framework with ideas from integrating GIS modelling, AutoML, and HPO. Next, the concept of geo-interventions was formulated into a geodata-driven optimization problem to structure methods for the conceptual framework, logical architecture, web prototype, and case study. The conceptual framework was defined by using conceptual components from the literature review, while the logical architecture used this framework to specify components and subsystems for software development. This architecture was applied to develop a web prototype for demonstrating the functionality of a geo-intervention design and generation system. A case study on reducing traffic collisions in Toronto, Canada was conducted to test the web prototype on a real-world problem. Considerations, benefits, limitations, and research implications on the conceptual framework, logical architecture, web prototype, case study, and their methods were discussed. By incorporating the concept of user-guided geo-intervention design and generation, the divide between spatially focused research and practice greatly decreases by linking spatial methods under the practicality of predicting real-world outcomes with geo-interventions.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".