The rise of urban tech: how innovations for cities come from cities
Bibliographic record
Abstract
This research investigates the economic geography of urban technology, or ‘urban tech’, start-up enterprises. Comprised of ride-hailing, co-living, co-working, smart cities and other urban-oriented activities, urban tech is a suite of innovations that enable and are premised upon growing urbanization. We investigate where urban tech comes from by analysing Pitchbook, a database of venture capital deals, to chart the evolution and geography of urban tech start-up firms. We show urban tech firms to be highly clustered in two kinds of places: specialized tech hubs such as the San Francisco Bay Area and large cities such as New York, London and Beijing. Furthermore, we find that urban tech geography is associated with two classes of factors: the scale of existing tech activity, and the size and extent of metro areas. Together these findings suggest that the geography of urban tech is shaped by the innovative capabilities of urban areas and, to a lesser extent, by urbanization itself. Urban tech investment is less common in areas associated with ‘Industry 4.0’ industrial policy.
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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 | 0.002 | 0.004 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".