Fractal Analysis Methods for Characterizing the Spatial Distribution of Human Settlements
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
This chapter presents the founding principles of estimating the fractal dimension of sets of human settlements represented in the form of points, lines (linear networks, contours of buildings or built clusters) or polygons (buildings mapped in 2D). It describes in detail the methods of calculating different fractal dimensions. The chapter shows that the box-counting and correlation dimensions, which are the dimensions most commonly used in geography to characterize built-up fabrics, may each take substantially different values for the same object. The objective of the chapter is to study these differences in more detail and to try to identify the morphological characteristics of the built-up fabrics that influence to a greater or lesser degree the value of each dimension. Fractal dimensions calculated by box-counting and correlation methods considering the footprint of buildings are frequently used for the characterization of built-up fabrics.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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