The Birth and Life of Buildings: High-Resolution Analysis of Historical Building Trends through the Digitised Municipal Archive of Tel Aviv-Yafo
Bibliographic record
Abstract
Inconsistent temporal definitions of key events in a building's lifecycles, and especially of its "birth" date, usually impede a large-scale, high-resolution analysis of building trends and construction fluxes based on municipal building datasets. This study addresses this shortcoming by proposing a reproducible ontological dating formulation of major construction activities during a building's lifecycle using the building permit as the most common, reliable, and consistent indicator of a building's age. We tested this approach by analysing the Tel Aviv-Yafo Municipality's Engineering Administration Archive, which consists of around 5.3 million digitised documents spanning between 1920-2020 and arranged in more than 28,000 building files. We combined permit data with supporting taxation and construction completion documents to automatically extract the date of "birth" or major reconstruction of each of the dataset buildings. The resulting dataset enabled us to generate detailed diachronic maps of urban growth at the resolution of an individual building. Despite challenges such as data discrepancies and archival gaps, this analytical method highlights the value of working directly with raw administrative metadata to uncover valuable insights into historical transformations in the built environment. It also demonstrates the utility of building permits as critical indicators of economic and architectural activities. By applying this approach to urban-scale building datasets, it is possible to predict building ages with reasonable accuracy and, thus, to enhance the understanding of urban growth and transformation dynamics.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.007 | 0.010 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".