MétaCan
Menu
Back to cohort
Record W4388183209 · doi:10.5539/jsd.v16n6p55

Spatial Changes in Planning Status and Building Density in Dar es Salaam City, Tanzania

2023· article· en· W4388183209 on OpenAlexvenueno aff
Robert Kiunsi, Nicholaus Mwageni

Bibliographic record

VenueJournal of Sustainable Development · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicUrban and Rural Development Challenges
Canadian institutionsnot available
Fundersnot available
KeywordsDar es salaamHuman settlementTanzaniaUrban sprawlGeographyConsolidation (business)Urban planningEnvironmental protectionEnvironmental planningCivil engineeringBusinessArchaeologyEngineering

Abstract

fetched live from OpenAlex

There is abundant information on the extent and changes in coverage of human settlements in Dar es Salaam City, Tanzania but information on changes in building density, building consolidation levels and unbuilt-up or undeveloped areas is limited. The main objective of this research was to establish the planning status and changes in the spatial coverage of settlements, unbuilt areas, number of buildings, building densities and building consolidation levels in Dar es Salaam City between 2007 and 2017. The study deployed a literature review, remote sensing imagery and field verification. The study revealed firstly, that there are three main categories of planning status, two of which are well documented, that is planned and unplanned settlements, and unbuilt areas which are not extensively documented. Between 2012 and 2017, the spatial extent of planned areas remained more or the same, unplanned areas increased and unbuilt-up areas decreased. The city had a total of 367,278 buildings in 2007 and 675,644 in 2017 with an annual growth rate of 8%. The average building density for Dar es Salaam City in 2017 was 4.2 and the highest building density in unplanned areas was 48 buildings/ha while in planned areas, was 22 buildings/ha. The study recommends that concerted efforts are needed to control urban sprawl.

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.

How this classification was reachedexpand

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)0.002
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.025
Threshold uncertainty score0.523

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
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 head0.032
GPT teacher head0.296
Teacher spread0.265 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2023
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Sustainable DevelopmentSame topicUrban and Rural Development ChallengesFrench-language works237,207