Potential Economies using big data to analyse urban competitiveness of new urban communities: a case study on socio-economic indicators in Egypt
Bibliographic record
Abstract
Orthodox economic models often linked the size of cities to their economies. However, this changed through the last quarter of the 20th century; cities grew bigger, side by side to unemployment, inflation and falling wages and productivity (Davis, 2006). In Egypt, urban migration was considered the root of the city’s density. In the seventies, New Urban Communities were developed with strategic visions, they deployed enormous resources and created supply for the economic and social aspects of urban life. However, today they remain vastly vacant and most of them contribute little to the region’s economy. The main research question of this research is: “How can attributes of urban competitiveness contribute to the development of sub-centres in new urban communities of emerging economies?” To answer this question two of the eight developments around Cairo are comparatively analysed with regard to their urban competitiveness. Theories of urban economics and its spatial structure are adapted for the context of this case-study. A new quantitative methodology is utilised to overcome data challenges in the context of emerging economies. Exploratory spatial data analysis is used to demonstrate the spatial distribution of socio-economic attributes and their relation to the resulting urban competitiveness. The comparison between city centre and NUCs is analysed to inform policies for urban growth using the framework developed in the literature review. The research highlights the importance of autonomous urban management for each NUC. This is carried out within a framework of a collaborative polycentric urban region that ensures the differentiated roles of each region. The urban competitiveness analysed shows the system of cities within the metropolitan region, and the potential emerging sub-centres in NUCs. The mapped socio-economic attributes establish the possible correlation between urban competitiveness and access to these attributes. They also show mismatches in supply and demand, and shed light on particular effects of public urban spending.
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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 | 0.002 | 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 | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".