Landscape Mapping to Quantify Degree-of-Freedom, Degree-of-Sprawl, and Degree-of-Goodness of Urban Growth in Hawassa, Ethiopia
Bibliographic record
Abstract
In the rapidly urbanizing African continent, monitoring, mapping and modeling of urban growth is an indispensable task to understand the magnitude and rate of the ongoing changes. Remote sensing data have been found useful in mapping urban areas and as a source of data for modeling and analysis of spatio-temporal trajectories of cities. In this study, remote sensing data from different sensors extending over a period of 24 years (1987-2011) were used to classify and extract areas of the established land use land cover classes in Hawassa City, Ethiopia. Among those classes, built-up areas were used to quantify urban growth and sprawl. The result of the mapping indicated that the built-up area had increased by 234.5% between 1987 and 2011. The area under investigation was sub-divided into eight equal zones within a circle to apply analytical models. To analyze the pattern, process and overall growth status of Hawassa City, Pearson’s Chi-square statistics, Shannon’s entropy, and degree-of-goodness models were employed. The result revealed that the degree-of-freedom was high indicating the disparity between observed and expected urban growth. The entropy values both in temporal intervals and zones were found higher than half-way mark of respectively showing the tendency of sprawl. Except the South Zone, the city had not experienced “goodness” during the entire study period. This study has provided new evidence about the urban growth in the study area that could be used by city managers.
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How this classification was reachedexpand
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".