Using Spatial Analysis and Geovisualization to Reveal Urban Changes: Milan, Italy, 1737–2005
Bibliographic record
Abstract
The Italian city of Milan provides a fascinating laboratory for disentangling the historical layers that structure the spatial layout of a European city. In the last 250 years, the temporal span of this study, Milan has played a key role in Italy's industrialization and as its gateway to the centres of economic and cultural modernization in Western Europe. This article proposes a spatial analytical methodology that incorporates geovisualization techniques to discover and map urban change in Milan. Using historical maps dating back to the eighteenth century and a 2005 official city map, we applied methods of spatial analysis and geovisualization techniques to determine which parts of the city changed the most in the time interval considered. We then drew parallels between urban changes and political changes in the history of the city. Urban change is defined here as a change in the form and structure of the city (new buildings, new or widened roads, new squares, etc.). Results indicate that morphological changes at the intra-urban scale in Milan appear to be spatially oriented to reflect national and international political events from the mid-eighteenth century to the present. Although this result was not unexpected, the extent to which changes in the built-up environment reflect historical events was somewhat surprising.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| 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 | 0.000 |
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".