Determining Factors for Slum Growth with Predictive Data Mining Methods
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Currently, more than half of the world’s population lives in cities. Out of these more than four billion people, almost one quarter live in slums or informal settlements. In order to improve living conditions and provide possible solutions for the major problems in slums (e.g., insufficient infrastructure), it is important to understand the current situation of this form of settlement and its development. There are many different models that attempt to simulate the development of slums. In this paper, we present data mining models that correlate information about the temporal development of slums with other economic, ecologic, and demographic factors in order to identify dependencies. Different learning algorithms, such as decision rules and decision trees, are used to learn descriptive models for slum development from data, and the results are evaluated with commonly used attribute evaluation methods known from data mining. The results confirm various previously made statements about slum development in a quantitative way, such as the fact that slum development is very strongly linked to the demographic development of a country. Applying the introduced classification models to the most recent data for different regions, it can be shown that the slum development in Africa is expected to be above average.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it