Demographic and dwelling models by artificial intelligence: urban renewal opportunities in Spanish coast
Bibliographic record
Abstract
the Spanish Mediterranean coast has undergone intense urban development in recent decades.It has often focused on building a property patrimony based more on real estate, business expectations and consuming resources than on its actual use.Similarly, its functionality and need to adapt to social needs and the requirements of the certain demographic profiles of its time have largely been ignored.the purpose of this study is to shed light on the Spanish Mediterranean coast's existing residential models and the relationship with the local demographic reality of users.Its aim is to be part of a Decision Support System which focuses on urban regeneration and functional recovery.this study uses heuristic methodologies to demonstrate the coherence of an abundance of open access data.Such methodologies do not necessarily require specific hypotheses or formulations to generate useful knowledge.the 2011 Population and Housing Census (INE) is used as a knowledge source, on which data mining techniques based on Artificial Intelligence techniques are applied.We specifically use Self-Organising Maps (SOM) through Artificial Neural Networks (ANN), subsequently mapping the results through a Geographic Information System (GIS).these techniques permit an exploration of the different residential profiles in this territory.Each profile exposes very different levels of sustainability and resilience, identifying the groups or social collectives that singularly inhabit them, which are at times authentic drivers of the maintenance and growth of these models.to the extent that they are linked to demographic profiles, the knowledge obtained in this study is evidence of the different residential profiles' territorial location, and highlights the opportunities and weaknesses of urban regeneration.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".