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Record W4400774582 · doi:10.1002/9781394306565.ch4

Fractal Analysis Methods for Characterizing the Spatial Distribution of Human Settlements

2024· other· en· W4400774582 on OpenAlex

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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

Venuenot available
Typeother
Languageen
FieldEngineering
TopicUrban Design and Spatial Analysis
Canadian institutionsUniversité de Montréal
Fundersnot available
KeywordsHuman settlementFractalGeographyDistribution (mathematics)Spatial distributionStatistical physicsCartographyEconomic geographyMathematicsArchaeologyRemote sensingMathematical analysisPhysics

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.942
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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.

Opus teacher head0.018
GPT teacher head0.308
Teacher spread0.289 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Quick stats

Citations1
Published2024
Admission routes1
Has abstractyes

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