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Record W2981062335 · doi:10.1080/19376812.2019.1677482

Modeling the internal structure, dynamics and trends of urban sprawl in Ghanaian cities using remote sensing, spatial metrics and spatial analysis

2019· article· en· W2981062335 on OpenAlex
Daniel Kpienbaareh, Isaac Luginaah

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

VenueAfrican Geographical Review · 2019
Typearticle
Languageen
FieldEnvironmental Science
TopicLand Use and Ecosystem Services
Canadian institutionsWestern University
Fundersnot available
KeywordsUrban sprawlContiguityGeographyContext (archaeology)Spatial analysisRegional scienceUrban planningSpatial contextual awarenessEconomic geographyCartographyRemote sensingComputer scienceCivil engineeringEngineering

Abstract

fetched live from OpenAlex

In Ghana, studies on urban sprawl have focused on the use of conventional survey maps and qualitative descriptions that mask policy-relevant information for planning. We apply remote sensing data and spatial statistics to examine the Spatio-temporal dynamics in the internal structure of two cities. We find that the total area of the urban fabric in Wa increased from 11.365ha ± 0.413ha in 1986 to 1, 775.848ha ± 52.094ha in 2017, while Tamale’s increased from 715.425ha ± 5.969ha in 1984 to 6,890.177ha ± 208.105ha in 2017. The results also revealed contiguity in urban clusters. We discuss the implications of our findings within the context of spatial planning in Ghana.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.936
Threshold uncertainty score0.946

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.010
GPT teacher head0.229
Teacher spread0.220 · 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